Subjective wellbeing: An Integration of
Depression, Stress, and Homeostasis Theory
Vanessa Cook
B.Sc. (Deakin University)
B.Sc. (Psychology) (Hons.) (RMIT)
Submitted in fulfillment of the requirements for the degree of
Doctor of Psychology (Health)
Deakin University
November 2003
Acknowledgements
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A New Direction for Subjective Wellbeing
Table of Contents
TABLE OF CONTENTS .................................................................II
LIST OF TABLES .......................................................................... XI
LIST OF FIGURES .................................................................... XIII
ABSTRACT.................................................................................... XV
SECTION ONE – SUBJECTIVE WELLBEING AND
HOMEOSTASIS............................................................................................ 1
SECTION 1 OVERVIEW ..................................................................... 2
CHAPTER 1 ...................................................................................... 4
Quality of Life and Subjective Wellbeing ................................... 4
1.1
Nomenclature ................................................................ 5
1.2
An Overview of Subjective Wellbeing ......................... 6
1.3
An Operational Definition of Subjective Wellbeing ..... 8
CHAPTER 2 .................................................................................... 11
Homeostasis Theory ................................................................. 11
2.1
A Model of SWB Homeostasis ................................... 16
CHAPTER 3 .................................................................................... 18
1st Order Determinants of Subjective Wellbeing ...................... 18
1st Order Determinants of SWB ........................................... 19
3.1
Personality Factors ...................................................... 20
3.1.1
3.2
The Relationship Between Personality and SWB 21
Affective Factors ......................................................... 25
3.2.1
Affect Measurement Issues .................................. 26
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A New Direction for Subjective Wellbeing
3.2.2
Independence Versus Bipolarity of Affect ........... 27
3.2.3
The Evolution of Affect Conceptualization ......... 29
3.2.4
Affect as a Trait or a State .................................... 34
3.3
Link Between Personality and Affect ......................... 37
3.4
Influence of Personality and Affect on SWB .............. 39
CHAPTER 4 .................................................................................... 43
2nd Order Determinants of Subjective Wellbeing ..................... 43
4.1
Cognitive Buffers ........................................................ 43
4.1.1
Self-Esteem .......................................................... 44
4.1.2
Control .................................................................. 48
4.1.3
Optimism .............................................................. 50
4.2
The Adaptive Outcomes of Positive Cognitive Biases 53
CHAPTER 5 .................................................................................... 55
Depression ................................................................................ 55
5.1
Prevalence ................................................................... 56
5.2
Depression Assessment ............................................... 57
5.3
Models of Depression .................................................. 59
5.4
SWB and Depression................................................... 66
5.5
Theory of SWB Homeostasis and Depression ............ 68
5.5.1
Personality, Affect and Depression ...................... 68
5.5.2
Cognitive Buffers and Depression ....................... 69
5.5.3
Hypothesised Model of SWB Homeostasis
Breakdown
5.5.4
70
Rationale for the use of SWB indicators in
depression diagnosis and treatment evaluation ............................ 77
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A New Direction for Subjective Wellbeing
5.6
Summary ..................................................................... 82
CHAPTER 6 .................................................................................... 85
Study 1 Methodology ................................................................ 85
6.1
Aim .............................................................................. 85
6.2
Research Hypotheses ................................................... 86
6.3.1
Materials ............................................................... 87
6.4.2
Demographic Data ................................................ 87
6.3.3
Australian Unity Wellbeing Index ....................... 87
6.3.4
Hospital Anxiety and Depression Scale ............... 89
6.3.5
NEO Five-Factor Inventory.................................. 90
6.4
Procedure ..................................................................... 91
6.4.1
Recruitment of Participants .................................. 92
CHAPTER 7 .................................................................................... 93
Study 1 - Data Analysis ............................................................ 93
7.1
Assumptions of Normality Testing ............................. 94
7.1.1
Demographic Data ................................................ 94
7.1.2
Personal Wellbeing Index .................................... 95
7.1.3
NEO Five-Factor Inventory.................................. 96
7.1.4
HAD Scale ............................................................ 97
7.2
General Data Set Comparison ..................................... 98
7.3
Exploring Relationships ............................................ 100
7.3.1
Pearson Product-Moment Correlation – Data Set B
100
7.3.2
Standard Multiple Regression – Data Sets A & B
102
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A New Direction for Subjective Wellbeing
7.3.3
B
Hierarchical Multiple Regression – Data Sets A &
105
7.4
Testing the Homeostatic Regulation of SWB – The
Relationship Between SWB and Depression ................................. 107
7.5
Summary ................................................................... 110
CHAPTER 8 .................................................................................. 112
Discussion .............................................................................. 112
8.1
Data Set Characteristics............................................. 112
8.2
Variable Correlations ................................................ 114
8.3
Prediction of SWB..................................................... 115
8.3.1
8.4
Influence of Demographic Data ......................... 117
The Homeostatic Mechanism .................................... 119
SECTION TWO – THE CIRCUMPLEX MODEL OF AFFECT
.................................................................................................................... 123
SECTION TWO OVERVIEW ........................................................... 124
CHAPTER 9 .................................................................................. 126
Measurement of the Circumplex............................................. 126
9.1
Revisiting the Circumplex Model ............................. 126
9.2
Early Affect Measurement ........................................ 127
9.3
The 4-Dimensional Mood Scale ................................ 128
9.4
Conclusions ............................................................... 130
CHAPTER 10 ................................................................................ 132
Study 2 Methodology .............................................................. 132
10.1
Aim .......................................................................... 132
10.2
Rationale .................................................................. 132
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A New Direction for Subjective Wellbeing
10.3
Research Hypotheses ............................................... 133
10.4 - Method ..................................................................... 133
10.4.1
Materials ........................................................... 133
10.4.2
4-Dimensional Mood Scale .............................. 134
10.5
Procedure ................................................................. 135
10.5.1
Recruitment of Participants .............................. 135
CHAPTER 11 ................................................................................ 136
Study 2 Data Analysis............................................................. 136
11.1 – Study 2 - Assumptions of Normality Testing .......... 136
11.2
Replicated Principle Components Analysis with
Varimax Rotation ........................................................................... 138
11.3
Final Solution and Reliability Analysis ................... 140
11.4
Revised Affect Sub-Scales ...................................... 142
11.4.1
High Negative Affect (Negative Arousal) ........ 143
11.4.2
High Positive Affectivity (Positive Energy) .... 145
11.4.3
Low Negative Affectivity (Tiredness) ............. 146
11.4.4
Low Positive Affectivity (Relaxation) ............. 148
11.5
Subscales
Factor Analysis and Reliability Analysis of Revised
148
11.5.1
Subscales
149
11.5.2
11.6
High Negative and High Positive Affect
Low Negative Affect Subscale ......................... 151
Summary ................................................................. 158
CHAPTER 12 ................................................................................ 160
Discussion .............................................................................. 160
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A New Direction for Subjective Wellbeing
12.1
Replication of the 4-Dimensional Mood Scale ....... 160
12.2
Three Proposed Affect Subscales ............................ 162
SECTION THREE – INTEGRATION OF SWB AND STRESS
THEORY .................................................................................................... 165
SECTION THREE OVERVIEW ........................................................ 166
CHAPTER 13 ................................................................................ 168
Conservation Of Resources Theory (Hobfoll, 1988) .............. 168
13.1
Stress ....................................................................... 170
13.2
Resources................................................................. 173
13.2.1
Object Resources .............................................. 175
13.2.2
Personal Characteristic Resources .................... 175
13.2.3
Condition Resources......................................... 177
13.2.4
Energy Resources ............................................. 179
13.3
Summary ................................................................. 180
CHAPTER 14 ................................................................................ 182
Integration of Homeostasis and CoR Theory ......................... 182
CHAPTER 15 ................................................................................ 191
Study 3 Methodology .............................................................. 191
15.1
Aim .......................................................................... 191
15.2
Research Hypotheses ............................................... 192
15.3
Method..................................................................... 193
15.3.1
Materials ........................................................... 193
15.3.2
Demographic Data ............................................ 194
15.3.3
Australian Unity Wellbeing Index ................... 194
15.3.4
NEO-Five Factor Inventory (NEO-FFI)........... 195
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A New Direction for Subjective Wellbeing
15.3.5
Affect Scale ...................................................... 195
15.3.6
Life Orientation Test-Revised (LOT-R)........... 195
15.3.7
Self-Esteem Scale (SES) ................................. 196
15.3.8
Perceived Control of Internal States Scale
(PCOISS:12)
15.3.9
196
Depression Anxiety Stress Scale (DASS-21) ... 197
CHAPTER 16 ................................................................................ 198
Study 3 Data Analysis............................................................. 198
16.1
Assumptions of Normality Testing ......................... 199
16.1.1
Demographic Data ............................................ 199
16.1.2
Personal Wellbeing Index ................................ 200
16.1.3
NEO Five-Factor Inventory.............................. 201
16.1.4
Depression Anxiety Stress Scale (DASS) ........ 202
16.1.5
Self-Esteem Scale ............................................. 203
16.1.6
Life Orientation Test (LOT) ............................. 203
16.1.7
Perceived Control of Internal States Scale
(PCOISS)
16.2
204
Exploring Relationships .......................................... 204
16.2.1
Pearson Product-Moment Correlation .............. 204
16.2.2
Standard Multiple Regression .......................... 208
16.3
Comparison of Groups ............................................ 213
16.4
Testing the Homeostatic Regulation of SWB – The
Relationship Between SWB and Depression ................................. 215
16.5
Structural Equation Modelling ................................ 219
16.6
Summary ................................................................. 225
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A New Direction for Subjective Wellbeing
CHAPTER 17 ................................................................................ 228
Study 3 Discussion.................................................................. 228
17.1
Relationship Between Variables ............................. 228
17.2
Characteristic Differences Between Individuals with
Normal and Sub-Average SWB. .................................................... 229
17.3
Prediction of SWB................................................... 230
17.4
The Homeostatic Mechanism .................................. 231
17.5
Testing the Hypothesised Integrated Model ............ 233
17.6
Summary ................................................................. 236
CHAPTER 18 ................................................................................ 238
General Discussion ................................................................ 238
18.1
The Normative SWB Range .................................... 239
18.2
The Homeostatic Mechanism .................................. 241
18.2.1
Prediction of SWB............................................ 242
18.2.2
Homeostatic Failure ......................................... 247
18.2.3
Rationale for the Use of SWB Inventories for
Depression
18.3
251
The Conceptualisation and Measurement of Affect 254
18.3.1
Proposed Affect Subscales ............................... 254
18.3.2
Recommendations for Scale Improvement ...... 256
18.4
The Integration of Homeostasis, Stress and
Depression Theory ......................................................................... 257
18.4.1
Evaluation of the Hypothesised Models ........... 258
18.4.2
Justification for an Integrated Model ............... 259
18.5
Limitations............................................................... 261
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A New Direction for Subjective Wellbeing
18.5.1
Study 1 .............................................................. 261
18.5.2
Study 2 .............................................................. 262
18.5.3
Study 3 .............................................................. 263
18.6
Conclusions ............................................................. 264
18.6.1
Brief Overview of Findings .............................. 264
18.6.2
Future Research and Practical Applications ..... 266
REFERENCES.............................................................................. 269
APPENDICES ............................................................................... 292
APPENDIX A ................................................................................ 293
Deakin University Human Research Ethics Committee
Approval ............................................................................................. 293
APPENDIX B ................................................................................ 294
Study 1 Questionnaire ............................................................ 294
APPENDIX C .................................................................................. 15
Study 3, Questionnaire One...................................................... 15
APPENDIX D .................................................................................. 20
Study 3, Questionnaire Two ..................................................... 20
APPENDIX E................................................................................... 25
Means and Standard Deviations of all Study 1 items ............... 25
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A New Direction for Subjective Wellbeing
List of Tables
Table 1 - Mean and Standard Deviation of PWI Variables in Data
Sets A and B .................................................................................................. 96
Table 2 - Pearson Product-Moment Correlations Between Measures
of PWI and Neuroticism, Extraversion, Anxiety and Depression (Set A
N=224; Set B N=240) ................................................................................ 101
Table 3 - Standard Multiple Regression of Extraversion,
Neuroticism, Depression and Anxiety on PWI (Personal Domain SM)
Scores ......................................................................................................... 104
Table 4 - Hierarchical Regression Equations Predicting PWI Scores
Controlling for Demographic Variables .................................................... 106
Table 5 - Means and Standard Deviations of PWI Scores of Nine
Groups of Increasing Depression Scores ................................................... 108
Table 6 - Affect factors and items loadings in the replication of the
4-DMS ........................................................................................................ 141
Table 7 Subscale Correlations of the Replicated 4-DMS.............. 142
Table 8 - Two-Factor Affect Solution for Data Sets A and B ......... 150
Table 9 - Four-Factor Affect Solution ............................................ 154
Table 10 – Scale Reliability of the Three-Factor Affect Scale ....... 155
Table 11 Subscale Correlations of the Proposed Scale ................ 157
Table 12 Comparison of Subscale Correlations Between the 4-DMS
and the Proposed Scale .............................................................................. 161
Table 13 - Mean and Standard Deviation of PWI variables .......... 201
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A New Direction for Subjective Wellbeing
Table 14 - Pearson Product-Moment Correlations Between
Measures of PWI and Neuroticism, Extraversion, Anxiety and Depression
.................................................................................................................... 205
Table 15 - Pearson Product-Moment Correlations Between
Measures of PWI and Affect, and Neuroticism, Extraversion, Optimism and
Control........................................................................................................ 206
Table 16 - Pearson Product-Moment Correlations Between
Measures of PWI and Affect, and Depression, Anxiety, Stress, and SelfEsteem ........................................................................................................ 207
Table 17 - Standard Multiple Regression of Extraversion,
Neuroticism, Control, Optimism and Affect on PWI Scores ...................... 210
Table 18 - Standard Multiple Regression of Depression, Anxiety,
Stress, Self-Esteem and Affect on PWI Scores ........................................... 212
Table 19 Summary of MANOVA for differences between Two Groups
of PWI Scores and Affect, Optimism, Control, Neuroticism and Extraversion
214
Table 20 Summary of MANOVA for differences between Two Groups
of PWI Scores and Affect, Stress, Anxiety, Depression and Self-Esteem ... 215
Table 21 ......... Means and Standard Deviations of PWI Scores of Ten
Groups of Increasing Depression Scores ................................................... 216
Table 22 ...................... Goodness of Fit Indices for Structural Models
225
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A New Direction for Subjective Wellbeing
List of Figures
Figure 1 The Normal Distribution of Subjective Wellbeing
(Cummins et al., 2002) ________________________________________ 15
Figure 2 A Homeostatic Model for Subjective Wellbeing. ______ 16
Figure 3 Comparison of Bipolar and Independence Models of Affect
__________________________________________________________ 29
Figure 4 Russell’s (1980) Original Cirumplex Model of Affect ___ 31
Figure 5 Watson & Tellegen’s (1985) Model of Affect _________ 32
Figure 6 The Relationship Between SWB and Extrinsic Conditions
(Cummins et al., 2002) ________________________________________ 71
Figure 7 Hypothesised Model of Depression Based on the SWB
Homeostasis Theory __________________________________________ 75
Figure 8 Beck’s Negative Thought Cycle ___________________ 76
Figure 9 - Curvilinear Relationship Between SWB and Depression86
Figure 10 Mean PWI Scores for Depression in Incrementing
Groups of Ten ______________________________________________ 109
Figure 11 The normal distribution of subjective quality of life with
reference to mean satisfaction scores for Data Sets A and B _________ 113
Figure 12 - Integrated Model of Homeostasis and COR Theory:
Homeostasis Regulation ______________________________________ 186
Figure 13 Integrated Model of Homeostasis and COR Theory:
Homeostasis Failure_________________________________________ 188
Figure 14 Mean PWI Scores for Depression in Incrementing
Groups of Ten ______________________________________________ 217
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A New Direction for Subjective Wellbeing
Figure 15 Mean PWI Scores for Depression Groups: Comparison
of Normal and Sub-Average PWI Groups ________________________ 218
Figure 16 Integrated Model of SWB Homeostasis and COR Theory:
Hypothesised Model 1 _______________________________________ 220
Figure 17 Revised Model 1 _____________________________ 222
Figure 18 Hypothesised Model 2 ________________________ 222
Figure 19 Revised Model 2 _____________________________ 224
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A New Direction for Subjective Wellbeing
Abstract
This thesis investigates the maintenance of subjective wellbeing, and the
relationship of subjective wellbeing to theoretical models of depression and
stress. The thesis is divided into three sections, exploring the proposed
theory of Subjective Wellbeing Homeostasis, the conceptual structure and
measurement of affect, and the integration of the Subjective Wellbeing
Homeostasis and Conservation of Resources Theories, respectively.
The rationale for the proposed research is that a test of subjective wellbeing
should be used in conjunction with depression inventories for the detection
of depression within the community. It is predicted that depression
assessment based on Subjective Wellbeing domains will lead to the
increased specificity of depression intervention available to clients.
Furthermore, the homeostasis of subjective wellbeing is closely tied to the
concept of stress, thus the integrated model of homeostasis and conservation
of resources theory presented operationalizes the process of subjective
wellbeing regulation.
The first section of this thesis investigates the relationship between
subjective wellbeing and depression, and explores the rationale for the use
of a subjective wellbeing inventory for depression detection and treatment
evaluation. Individual differences in personality and affect influence the
level of subjective wellbeing experienced, and these have been proposed to
act as major components in a homeostatic mechanism that regulates and
maintains levels of subjective wellbeing. Subjective wellbeing levels are
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A New Direction for Subjective Wellbeing
held for each person at an idiosyncratic value that lies between 50-100
percent of maximum. Therefore, the first study of this thesis explores the
relationship between personality, depression, anxiety and subjective
wellbeing. Results indicated that neuroticism and depression tend to be the
strongest predictors of subjective wellbeing, however extraversion may be a
stronger predictor for individuals or groups with higher depression
symptomatology or lower subjective wellbeing levels. This study also
aimed to test for the hypothesised curvilinear relationship between
depression and subjective wellbeing. The findings lent some tentative
support for this hypothesis.
In the second section of this thesis, the circumplex model of affect is
reviewed, followed by a critical analysis of the 4-Dimensional Mood Scale
devised by Huelsman, Nemanick and Munz (1998). Two alternative affect
sub-scales are then formed via factor analysis according to the theoretical
circumplex model. These subscales, consisting of High Activation Negative
Affect and High Activation Positive Affect, were found to be as reliable and
statistically significant as the original subscales. Moreover, the new
subscales provide a more thorough measurement of affect, as based on the
conceptualisation of the circumpelx, due to the affective breadth of the
items.
The final section of the thesis reviews literature on the Conservation of
Resources Theory (Hobfoll, 1988). An integrated model of subjective
wellbeing homeostasis and Conservation of Resources theory is then
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A New Direction for Subjective Wellbeing
presented. Study 3 aimed firstly to test the relationships between the
variables within the proposed model. The relationships between the model
variables, which include subjective wellbeing, depression, anxiety, stress,
personality, affect, self-esteem, control and optimism, were tested. The
results indicated that the were consistently stronger predictors of Personal
Wellbeing Index scores than depression and neuroticism. A second aim was
to test for differences between variable mean scores between groups with
normal and sub-average Personal Wellbeing Index scores. A Multivariate
Analysis of Variance was performed, with results indicating that there were
significant differences on all variables between individuals within the
normal Personal Wellbeing Index range, and those within the sub-average
range. The third aim was to re-test the hypothesised curvilinear relationship
between subjective wellbeing and depression. In this study, there was
stronger support for the curvilinear relationship, which may be a result of
the use of a different depression inventory (the Depression Anxiety Stress
Scale as opposed to the Hospital Anxiety Stress Scale), or the higher
average Personal Wellbeing Index score of the participant group. Finally,
the study aimed to test the proposed integrated model through Structural
Equation Modelling. As Study 3 was based on two questionnaires, two
separate hypothesised models were generated and tested in AMOS. The
hypothesised models did not initially provide a good fit to the data, however
the models were then revised according to the Modification Indices. After
modification, both models provided a good fit to the data, and explained
43% to 88% of the variance in PWI and depression, anxiety and stress
xvii
A New Direction for Subjective Wellbeing
scores. These results provide justification for an integrated subjective
wellbeing and stress theory.
xviii
Section One – Subjective Wellbeing and Homeostasis
1
Subjective Wellbeing and Homeostasis
Section 1 Overview
The conceptualization of subjective wellbeing has evolved from an
immense body of literature on the related topics of quality of life, happiness
and life satisfaction. While traditionally used as a measure of societal
wellbeing in social indicator research, in the last two decades subjective
wellbeing has been applied to psychological research, and viewed as an
individual difference factor. Thus, subjective wellbeing is commonly
measured alongside factors such as personality and affect, and used to
explain the ways in which people conduct and perceive their lives. In this
section, literature on subjective wellbeing is reviewed, followed by the
presentation of a model of subjective wellbeing homeostasis. The review
then focuses on the factors that are hypothesized to contribute to subjective
wellbeing, which are personality (extraversion and neuroticism), affect
(positive and negative affect) and cognitive buffers (optimism, control and
self-esteem). Literature on depression is then reviewed, with an emphasis
on cognitive models of depression. The aim of Study 1 was to test the
relationships between the variables of personality (extraversion and
neuroticism), anxiety, depression and subjective wellbeing, within two
comparable data sets. The hypothesis that depression, neuroticism and
anxiety would be negatively correlated, and extraversion would be
positively correlated with subjective wellbeing, was supported. However,
interesting differences were found in the prediction of subjective wellbeing.
The results of the first data set support the hypothesis that depression and
neuroticism are the strongest predictors, whereas extraversion was the
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Subjective Wellbeing and Homeostasis
strongest predictor within the second data set. This study also aimed to test
for the hypothesized curvilinear relationship between depression and
subjective wellbeing. The findings lent some tentative support for this
hypothesis, however further research into the relationship between
depression and subjective wellbeing is recommended
3
Subjective Wellbeing and Homeostasis
Chapter 1
Quality of Life and Subjective Wellbeing
Literature on Subjective Quality of Life and related constructs is
expansive, growing out of an historical desire to understand the experience
and evaluation of life. Due to the complex and philosophical nature of
Quality of Life, there are many different ways it can be perceived, and many
fields of study, and purposes, to which it can be applied. Although terms
relating to Quality of Life are colloquially synonymous, the academic study
of Quality of Life requires clear definitions that differentiate between these
similar terms. The concept of Quality of Life has undergone much
evolution, and after four decades of research there still exists a lack of
uniformity or consensus regarding the academic definition and theoretical
structure of Subjective Quality of Life.
Historically, the concept of quality of life arose from American
research on social indicators during the 1960’s, when it was recognised that
subjective indicators of social wellbeing were as important for measuring
social change and the prosperity of nations as objective economic indicators
of national wealth (Land, 2000). The concept of quality of life then evolved
from an indicator of social wellbeing to an indicator of individual wellbeing.
This evolution coincided with the introduction of synonymous yet
conceptively distinct constructs related to quality of life. The term ‘life
satisfaction’ became popular, measured either through a single, generic
question such as “How satisfied are you with your life as a whole”
(Andrews & Withey, 1976), or a set of questions measuring satisfaction
4
Subjective Wellbeing and Homeostasis
within specific life areas (Cummins, 1997a). Over time, literature relating
to quality of life has been published using alternative or historically
ambiguous terms such as; subjective wellbeing, satisfaction, psychological
wellbeing, and happiness. Thus, the initial chapter will first clarify and
define quality of life-related terminology.
1.1
Nomenclature
The literature tends to use the word ‘happiness’ in relation to current
affect or the emotional aspect of subjective wellbeing (Heaven, 1989).
‘Happiness’ emphasizes the affective element involved in an individual’s
evaluation of life. It is experienced as the emotional state of joy (Argyle &
Martin, 1991), and has been conceptualized as a balance between positive
and negative affect (Bradburn, 1969). ‘Life satisfaction’, on the other hand,
is perceived as reflecting a broad cognitive judgment of how satisfied an
individual is with their life (Heaven, 1989); a cognition that is the result of
reflection (Argyle & Martin, 1991). This cognitive judgment is based on a
comparison of the evaluation of current circumstances with internally
imposed standards (Diener, Larsen, Levine & Emmons, 1985; Headey &
Wearing, 1987).
In contrast, ‘subjective wellbeing’ (or subjective quality of life) is
generally considered a combination of happiness (encompassing positive
and negative affect) and life satisfaction (Andrews & Withey, 1976; Diener
& Diener, 1995; Heaven, 1989; Headey & Wearing, 1989) across specific
life areas or domains.
5
Subjective Wellbeing and Homeostasis
Subjective wellbeing (SWB) is, thus, the most global term to
describe how people feel about their lives, and is based on both an
emotional reaction and cognitive judgement. According to Diener (1984), an
assessment of subjective wellbeing involves the subjective experience of the
individual, as the integrated judgment of the person’s life both within
specific domains and as a global assessment. SWB reflects the integration
of a high sense of satisfaction with life at the cognitive level, within specific
areas of life, and a propensity to experience positive emotions at the
affective level (Myers & Diener, 1995). To avoid confusion between
similar concepts, the term ‘subjective wellbeing’ will be used herein, to
represent the evaluation of life quality as the integrated cognitive judgment
(Lewinsohn et al., 1991) and composite of affective satisfaction in life
domains.
1.2
An Overview of Subjective Wellbeing
There are two broad perspectives when conceptualizing SWB. In
one, SWB is a global, unitary entity (Andrews & Withey, 1976), and in the
other it is composed of discrete domains (Cummins, 1996). These
approaches relate to evaluations of SWB using top-down theory or bottomup theory respectively (Shmotkin, 1998).
Top-down theory assumes that global assessments of SWB and
happiness are influenced by the trait-like factors of personality and attitude,
enabling a person to enjoy pleasures because they are happy, not vice versa
(Diener, 1984). Top-down SWB influences the satisfaction one perceives in
various life domains (Mallard et al., 1997).
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Subjective Wellbeing and Homeostasis
Andrews and Withey (1976) defined this construct from a broad topdown perspective in which quality of life is a unitary concept. A single item
questionnaire was used to gauge overall perceptions of quality of life
(Andrews & Withey, 1976), measured with a Likert scale response format
of satisfaction to dissatisfaction. This measurement is crude. Firstly, there
is only a small area of discrimination between different satisfaction levels
presented by the scale. Secondly this method of assessment provides no
understanding of the factors that contribute to subjective wellbeing. Finally,
this global measure has limited utility for small group comparisons
(Cummins, 1996).
In contrast, according to bottom-up theory, SWB is the accumulation
of happy moments, and is judged via the summation of many happy
experiences (Diener, 1984; Mallard, Lance & Michalos, 1997). In this
conceptualization, SWB is the combination of satisfaction levels in a
number of smaller domains (Brief, Butcher, George & Link, 1993; Mallard
et al., 1997).
Finally, the top-down and bottom-up models of SWB have been
integrated to form the bi-directional model (Gerhart, 1987), which
presupposes that both influences may be present simultaneously. SWB then
both results from satisfaction in life domains, and influences satisfaction in
those domains (Lance, Lautenschlager, Sloan & Varca, 1989).
The three models of the relationship between overall SWB and
satisfaction within life domains were tested in a study conducted by
Mallard, Lance and Michalos (1997). These authors found overwhelming
7
Subjective Wellbeing and Homeostasis
support for the bi-directional model, with some support for the top-down
models and the least support for the bottom-up model.
While theories of SWB continue to evolve, researchers are now less
inclined to view it from a solely top-down or bottom-up perspective. As
Brief and colleagues (1993) concluded, it is not necessary to choose the
better of the two theories (bottom-up vs. top-down) as SWB displays both
bottom-up and top-down effects. SWB exists both as a product of
satisfaction in specific domains of life, and as a distinct, separate entity
within its own rite. Likewise, satisfaction within life domains is both the
outcome of cognitive assessment, and influenced by our predetermined
experience of SWB (Brief, Butcher, George & Link, 1993; Mallard et al.,
1997).
1.3
An Operational Definition of Subjective Wellbeing
Both objective and subjective factors are considered important to
Quality of Life, however recent studies have concluded that objective and
subjective measures of quality of life are only weakly correlated (Cummins,
2000; Veenhoven, 2000). Therefore Quality of Life can be defined as a
three-factor model, incorporating a) objective measures of life domains, b)
domain weighting according to importance, and c) subjective satisfaction
self-ratings. This model recognizes the influence of objective and
subjective satisfaction, as well as the influence of individual weighting of
domains by importance. This model specifies that objective quality of life
should reflect cultural norms of objective wellbeing that are valued by
society, such as wealth and physical health, while recognizing that people
8
Subjective Wellbeing and Homeostasis
weight the importance of these objective measures differently (Cummins,
2000). Thus, the importance of objective measures is relative to the
individual. Likewise, Brief et al (1993) stated that an individual’s
interpretation of objective life conditions, and the weighting of importance
placed on objective conditions, directly influences SWB.
The fifth edition of the Comprehensive Quality of Life Scale
(ComQol) was devised by Cummins in 1997, and operated on the procedure
of weighting levels of satisfaction within discrete domains of life by
importance. While this process is conceptually reasonable, the procedure of
multiplying importance and satisfaction is invalid. Therefore, through the
development of the scale’s conceptual construction, the seven domains of
satisfaction from the ComQol have formed the basis for a new scale called
the Personal Wellbeing Index (PWI). This scale comprises seven domains
of satisfaction rated on an 11-point Likert response scale. The PWI
conforms to the operational definition proposed by Cummins (2002), stating
that:
“Quality of life is both objective and subjective. Each of
these two axes comprises several domains which, together,
define the total construct.
Objective domains are measured
through culturally relevant indices of objective wellbeing.
Subjective domains are measured through questions of
satisfaction.” (Cummins, 2003).
This definition incorporates both the objective and subjective elements
of quality of life. An operational definition such as above enables the
9
Subjective Wellbeing and Homeostasis
separation of the subjective from the objective components of quality of life,
so research can be focused clearly on the experience, or subjectivity of
wellbeing. The nature of SWB can therefore be studied. For example,
whether it is changeable or stable over time, if there are social or population
wide patterns, and what factors or components influence or control SWB
levels.
This research focuses on subjective wellbeing rather than objective
determinants of wellbeing. Thus the Personal Wellbeing Index is utilized as
the measure of SWB.
10
Subjective Wellbeing and Homeostasis
Chapter 2
Homeostasis Theory
It is a common misconception that the amount of SWB that can be
experienced is limitless. This ideology has been challenged, and it has been
found that the experience of happiness is confined within set boundaries
(Cummins, 1995; Veenhoven, 1994). Literature focusing on the SWB level
of populations has found that the data do not fit a normal curve, but are
instead consistently negatively skewed (Andrews & Withey, 1976;
Campbell, Converse & Rodgers, 1976; Cummins, 1998, 2000, Cummins,
Gullone & Lau, 2002; Diener & Diener, 1996; Heady & Wearing, 1989).
Moreover, despite the variety of approaches used to operationalize and
measure SWB, different scales show relatively high correlations with one
other (Costa & McCrae, 1980), showing that SWB lies in the positive range
regardless of the definition guiding the measurement. The consistent
negative skew associated with SWB has led to the proposition that a
‘homeostatic mechanism’ governs and regulates the SWB of individuals,
maintaining satisfaction levels within the positive range (Cummins, 1995;
1998; Cummins et al., 2002).
The application of the concept of adaptation from biology to
psychology occurred around the 1950s in an attempt to explain the
phenomenon of behavioural adaptation to the environment (Helson, 1964).
Study into the related physiological concepts of homeostasis and
equilibrium then led to Helson’s (1964) Adaptation Level Theory, which
asserts that we are capable of adapting to changing levels of stimulus. The
most general principle of adaptation level theory, stated by Brickman,
11
Subjective Wellbeing and Homeostasis
Coates and Janoff-Bulman (1978, p918), is that “people’s judgments of
current levels of stimulation depend upon whether this stimulation exceeds
or falls short of the level of stimulation to which their previous history has
accustomed them”. The process of adaptation occurs through contrast and
habituation. Contrast occurs when, for instance, experiencing an extremely
positive event such as winning the lottery causes other ordinary events to
seem less pleasurable. Habituation occurs when the thrill of winning the
lottery wears off (Brickman et al., 1978). The same principles apply to the
reverse situations, in extreme negative situations.
Adaptation Level Theory formed the basis of homeostatic theories of
SWB. The ofiginal form is Headey and Wearing’s (1989) Dynamic
Equilibrium Model. This model proposed that each person has a ‘normal’
level of SWB and a normal pattern of life events, both of which are
predictable by personality characteristics. Carver (2000) also created a
model that stemmed from the general framework of Adaptation Level
Theory (Helson, 1964) in which SWB is continually recalibrated and thus
maintained homeostatically.
The presence of a ‘set-point’ for SWB was first proposed by Headey
and Wearing (1989), who suggested that SWB levels (under normal
conditions) are maintained within a restricted range, akin to the process of
equilibrium. The discovery that subjective wellbeing levels are constrained
to a certain range via floor and ceiling effects (rather that the full 0 to 100%
range) has inspired a growing scientific consensus that SWB levels are
homeostatically regulated to predetermined levels (Cummins, 1995, 1998;
Headey & Wearing, 1989).
12
Subjective Wellbeing and Homeostasis
SWB homeostasis theories in general purport that the level of SWB
experienced is stable and maintained at a set point, and any conditions that
increase or decrease SWB are adapted to over a short period of time,
restoring the stable SWB level. These models guided Cummins (1995) to
further investigate the maintenance of SWB, leading to the coining of the
term ‘SWB homeostasis’, and the formulation of a model depicting the
mechanism of SWB homeostasis (Cummins, 1998).
According to this model of SWB homeostasis, SWB levels deviating
from the set-point level are regulated through the process of adaptation,
whereby cognitive buffers restore SWB levels to a normal range (Cummins,
et al., 2002). Homeostatic breakdown can occur, for instance, under
conditions of prolonged chronic negative circumstances that are too severe
or lengthy to be successfully moderated by internal homeostatic
mechanisms. Support for this concept is found within Headey and
Wearing’s (1989) Dynamic Equilibrium Model, wherein SWB is predicted
to remain stable when the normal pattern of events is maintained, however,
deviations from normal events may change the normal level of SWB. This
occurrence is known as homeostatic breakdown within Cummins, Gullone
and Lau’s (2002) model, and will be explored further in the following
section. Homeostasis breakdown can be hypothesized to precipitate or
increase the predisposition to dysthymia and depression.
The SWB homeostasis theory was tested Cummins (1995; 1998;
2000) by reviewing a large number of studies by different researchers, and
confirmed the stability of subjective wellbeing with both Western and nonWestern populations. This research resulted in the development of a “gold
13
Subjective Wellbeing and Homeostasis
standard” for SWB, which could be expressed as 75.0 ± 2.5% Scale
Maximum for Western populations (Cummins, 1995). The percentage of
scale maximum (%SM) score integrates the results of normative population
studies using different SWB scales, standardizing the Likert scale data to a
range from 0 to 100%. This statistic is calculated, where a Likert scale has
been coded 1 to x, with the formula:
%SM = (scale score – 1)  100 / (x-1)
For instance, a score of 4 on a scale coded from 1 to 7 would
produce (4-1)  100 /(7-1) = 50%SM (Cummins, 1998). Using two
standard deviations to describe the normative range, it was concluded that
the population norms for life satisfaction lay within the 70-80%SM range.
A later review of two hundred and six articles concerning the topic
of life satisfaction using Western and non-Western population means was
conducted, finding a life satisfaction value of 70 ± 5% SM (Cummins,
1998). Therefore, world population life satisfaction scores were found to
predictably fall within the two standard deviation range of 60-80%SM, with
an adaptive range of 50 to 100% SM. When examining the distribution of
life satisfaction scores within population samples, Cummins (2002) found
that the standard deviations from Western populations, when combined,
produce a mean of 12 ± 1%SM. Thus, using two standard deviations to
describe a normal distribution around a mean of 75%SM, the normal range
within a general population sample is 50-100% (Cummins, 2000).
Therefore, SWB is maintained within a predictable range, with the
homeostatic control of SWB creating a floor and ceiling effect, see Figure 1.
14
Subjective Wellbeing and Homeostasis
Lower
threshold
for
individuals
Lower and
upper
thresholds
for general
population
samples
F
r
e
q
u
e
n
c
y
0
10
20
30
40
50
60
70
80
90
Population Mean
Percentage of Scale Maximum (%SM)
Figure 1 The Normal Distribution of Subjective Wellbeing (Cummins
et al., 2002)
There is now a consensus that SWB levels are relatively stable over
time, with growing support for the model of homeostasis proposed by
Cummins (1995). To emphasize this point, Argyle and Martin (1991)
suggest that SWB may involve three partially independent components;
including the frequency and degree of positive affect experienced at the
time, the absence of severe negative feelings such and depression and
anxiety, and the average level of satisfaction over a period, which may be
interpreted as one’s homeostatically maintained set-point level of
satisfaction. The mechanism responsible for the regulation of SWB
homeostasis has been depicted in a model by Cummins, Gallone and Lau
(2002), which is explored in the following section.
15
100
Subjective Wellbeing and Homeostasis
2.1
A Model of SWB Homeostasis
The theory of SWB homeostasis states that subjective wellbeing is
maintained within a predictable range around a set-point, and has become
commonly accepted within the research. A proposed model devised by
Cummins, Gallone and Lau (2002) depicts the influence of personality,
affect and positive cognitive biases as the regulating mechanisms of SWB.
This model is presented in Figure 2.
FIRST ORDER
SECOND ORDER
DETERMINANTS
DETERMINANTS
AS PERSONALITY
AS INTERNAL BUFFERS
OUTPUT
AND AFFECT
(POSITIVE INPUT)
EXTROVERSION
NEUROTICISM
POSITIVE AFFECT
CONTROL
SELF-ESTEEM
SUBJECTIVE
WELL-BEING
OPTIMISM
NEGATIVE AFFECT
(NEGATIVE INPUT)
Figure 2 A Homeostatic Model for Subjective Wellbeing.
This model describes a system that combines primary genetic
(personality) and stable affective factors with a secondary buffering system
(cognitive buffers). Neuroticism, extraversion and positive and negative
affect together create an individual predisposed level, or set-point, for
subjective wellbeing, which is on average 75%SM. Positive cognitive
biases constitute a regulatory system that ‘buffer’ or reduce the
16
Subjective Wellbeing and Homeostasis
psychological impact of external events, and restores equilibrium or
homeostasis to the system allowing the neutral set-point level of SWB to be
reestablished (Cummins & Nistico, 2002). The theory presumes that SWB
can only be elevated beyond set-point levels for a short period of time, and
that personality factors and cognitive buffers will reinstate the
homeostatically regulated set-point level of SWB. Therefore, higher levels
of SWB are more likely consequences of frequent ‘happiness bursts’ due to
positive extrinsic conditions, rather than the effect of one positive event
maintained over a longer period of time.
The individual components of the SWB homeostasis model will be
explored in the two following chapters, with reference to the link between
each factor and their relationship with SWB.
17
Subjective Wellbeing and Homeostasis
Chapter 3
1st Order Determinants of Subjective Wellbeing
The model of SWB homeostasis is compelling, but prior to an
explanation of its components, the necessity for such a device as ‘SWB
homeostasis’ is explored.
The reality of the world can be harsh and depressing. In the Western
world people’s lifestyles and opportunities are somewhat dependent on their
wealth. People need money to afford food and shelter, and are judged and
influenced by how much money and therefore social status they have, and
most people spend the majority of their lives trying to earn as much money
as possible. On a global level, the world is filled with disease, war, famine
and tragedy, and in many ways it can be said that humans are their own
worst enemies. We live in a reality where children starve in the streets
whilst others cruise in luxury cars; where a huge proportion of the nation’s
health care budget is used to remedy the consequences of almost epidemic
proportions of our self-imposed sloth and gluttony; and where there is such
hate and mistrust in the world that tragedies such as September 11 are
becoming common threats. How can we live in this reality without
becoming incapacitated by the depressiveness and futility of life?
It is herein argued that we are not doomed to become a race of
realistic, fatalistic, depressed people who see the world as it is. Instead, we
are able to don our rose-coloured glasses and see what we want to see. We
see the miracle of life, not the misfortune. We are capable of appreciating
what we have as a result of tragedy, and we believe that the future is bright
18
Subjective Wellbeing and Homeostasis
and fulfilling. It is the combined effect of our cognitive buffers, through
personality and affective factors, that provide the homeostatic regulation of
our SWB. Our positive cognitive buffers supply a counterbalancing force
that ensures the maintenance of SWB and motivation (Cummins & Nistico,
2002). This homeostatic regulation enables us to view life as positive,
which in turn makes life worth living. “It seems intuitive that, in
evolutionary terms, the human organism would benefit from the
maintenance of a fairly predictable and moderately high level of
satisfaction” (Cummins & Nistico, 2002:41). Satisfaction with life is
presumed necessary to support adequate levels of motivation toward life
(Veenhoven, 1994) and to avoid the debilitating motivational consequences
of depression (Cummins & Nistico, 2002).
This chapter will review literature on personality and affect, and the
link between these constructs. The relationship between personality, affect
and SWB will then be discussed. The cognitive buffers briefly mentioned in
this introduction will be further discussed in the following chapter.
1st Order Determinants of SWB
In order to understand SWB, it is necessary to conceptualise and
define its determining factors. Cummins (2000) outlined a model of
homeostasis in which the psychological processing that leads to SWB
comprises two levels of determinants. The first order determinants refer to
the genetic capacity of an individual to achieve SWB through personality.
Due to the traditional theoretical link between affect and SWB and their
trait-like properties, positive and negative affect is also considered a first-
19
Subjective Wellbeing and Homeostasis
order determinant of SWB, as affect incorporates the propensity to
experience positive and negative mood states. A secondary tri-partite
system of cognitive buffers, it is proposed, further influences SWB, and is
itself affected by positive and negative input or experiences of the
environment (Cummins 2000). Cognitive buffers influence patterns of
thinking and the processing of positive and negative experiences. The
interaction between these determinants gives rise to individual level of
SWB. A diagram of this model of SWB is provided in Figure 2. This
section of the paper will focus on the role of affect and personality in
relation to SWB.
3.1
Personality Factors
Theories of the relationship between personality and SWB were
devised in an attempt to answer the classic question regarding why some
people are consistently happier than others (Lu & Shih, 1997). Personality
refers to ‘characteristic response tendencies’, which are considered to have
both genetic and environmental components (Diener, 1988). The
demographic variables of age, sex, race and income give little indication of
one’s SWB. Income is a relatively poor predictor of SWB assuming the
person can afford life’s necessities. Studies reveal that age is relatively
unimportant as no time of life is notably happier or unhappier than any
other. Furthermore, although women tend to experience bad circumstances
more negatively, and good circumstances more positively than men, overall,
gender explains a mere 1% of SWB variance (Myers & Diener, 1995). Race
gives little clue as to a person’s SWB, and only seems to influence levels of
20
Subjective Wellbeing and Homeostasis
wellbeing when it is scarce, in conditions of poverty (Myers & Diener,
1995). Therefore, several theories of SWB have proposed that personality
factors are involved in the individual disposition and maintenance (or
homeostasis) of SWB level.
The five-factor personality model is a popular typology of
personality (Goldberg, 1992; McCrae & Costa, 1992), and conceptualizes
personality as comprising Extraversion, Neuroticism, Openness to
Experience, Conscientiousness and Agreeableness. Extraversion and
Neuroticism have the strongest influence over SWB, with extraversion
being originally linked to personality traits such as sociability, liveliness,
assertiveness, activity and being carefree, and neuroticism being linked to
the traits of anxiety, depression, guilt, irrationality and tension (Eysenk,
1981). Extraversion traits contribute to positive enjoyment without
reducing the unpleasantness of adverse circumstances, and neuroticism traits
predispose one to suffer more acutely from misfortunes, without
diminishing positive experiences (Costa & McCrae, 1980).
3.1.1
The Relationship Between Personality and SWB
The NEO-Personality Index-Revised (Costa & McCrae, 1992),
which is a personality inventory based on the Big-Five model, measures six
separate facets of extraversion: activity (need to keep busy); assertiveness
(leadership, dominance), excitement-seeking (craving stimulation);
gregariousness (companionship and social enjoyment); positive emotion
(optimism, joyfulness); and warmth (capacity for interpersonal
relationships). In a study examining the relationship between each of the six
21
Subjective Wellbeing and Homeostasis
facets of extraversion with SWB, Herringer (1998) found that overall, total
extraversion was significantly positively correlated with SWB as expected.
Of the six facets of extraversion, the two most strongly correlated with SWB
were assertiveness and positive emotion.
Of particular interest was the finding that significant differences
exist between the facets of extraversion that influence SWB for men and for
women. The two most influential facets for women were positive emotion
and warmth, whereas for men the two facets included assertiveness and
gregariousness (Herringer, 1998). Adding significance to this finding was
the discovery that the influential facets for women had no correlation with
the SWB level of men, and vice versa. Therefore, although extraversion as
a general factor is related to high SWB, this research shows that the
personality factors may not exert their influence on SWB in the same way
across gender (Herringer, 1998).
Despite potential gender differences, personality is considered a
substantial predictor of SWB. Extraversion correlates with SWB between
.35 and .49, and neuroticism correlates with SWB between -.31 to -.57
(Costa & McCrae, 1980; Francis, 1999; Lu & Shih, 1997). Furthermore, the
dynamic equilibrium model (Heady & Wearing, 1989) and the
temperamental predisposition model (Diener, Suh, Lucas & Smith, 1999),
depicts SWB levels as regulated to levels predetermined by stable
personality traits (neuroticism and extraversion). These theories assume
that individuals are predisposed to experience life events as positive or
negative relative to their personality ‘makeup’ or inherited genetic
dispositions.
22
Subjective Wellbeing and Homeostasis
It is thought that genetic variance accounts for about 40% of the
variance in positive emotionality and 55% of the variance in negative
emotionality, and that overall, 80% of long-term SWB is heritable
(Tellegen, Lykken, Bouchard, Wilcox, Segal & Rich, 1988). Furthermore,
monozygotic twins reared apart have been found to be more alike in regards
to SWB scores than dizygotic twins reared separately or reared together
(Tellegen et al., 1988). Although these figures tend to lend great support to
the theory of SWB disposition and hereditability, Diener and colleagues
(1999) stated that the estimates for the size of this influence has varied
widely throughout the literature, from 27% to 80%, depending on the
environment, the particular component of SWB being considered, and the
methodology of the research.
Longitudinal research has shown that there is strong evidence of the
stability of personality dispositions (Costa & McCrae, 1986), and that
wellbeing seems to be a disposition rather than merely an indicator of an
individual’s current situation, as personality traits are strong predictors of
SWB (Costa & McCrae, 1980). Other researchers concur, stating that the
level of SWB experienced is caused by the interaction between the
individual (personality and affective characteristics) and the environment
(life events, etc) (Lu, 1999). Personality traits are significantly correlated
with SWB, and positive life events are positively correlated with SWB,
whereas negative life events are negatively correlated with SWB (Lu, 1999).
“It seems that extraversion not only predisposes people to encounter certain
types of life situations but also serves to retain the stability of SWB rather
23
Subjective Wellbeing and Homeostasis
than depressing or inflating it to correspond with these various life
situations” (Lu, 1999:79).
Personality not only contributes significantly to the variance in SWB
levels, but also predicts SWB 20 years later (Costa & McCrae, 1980), and
these findings are generalizable to diverse populations (Pavot, Diener &
Fujita, 1990). Regarding the three less-influential personality dimensions,
correlations between Conscientiousness, Openness to experience and
Agreeableness with SWB tend to be low (Watson & Clark, 1992). Caution
is warranted, however, in assuming the Big-Five personality factors account
for all there is to say about personality. Not every personality factor can be
mapped distinctly within the popular personality model (Funder, 2001).
Furthermore, the importance of extraversion to SWB may have been
overemphasized as neuroticism has a stronger correlation with SWB than
extraversion (DeNeve & Cooper, 1995; Costa & McCrae, 1980; Headey &
Wearing, 1989; Heaven, 1989; Schmutte & Ryff, 1997). Therefore, despite
the high correlation between neuroticism and extraversion and SWB,
researchers also note that SWB cannot be solely explained in terms of
personality factors (DeNeve, 1999; DeNeve & Cooper, 1998).
Some critics have called into question what SWB inventories are
really measuring, by stating that these assessments are confounded by the
influence of personality factors (Muldoon, Barder, Flory and Manuck,
1998). They state that “subjective quality of life indices ideally should not
be influenced by patient characteristics that are outside the domain of
disease and health care” (Muldoon et al., 1998:544), suggesting that the
SWB measures are limited as they inadvertently measure enduring
24
Subjective Wellbeing and Homeostasis
dispositional personality characteristics. This line of argument, and the
suggestion that personality factors confound the measurement of SWB,
seems unlikely to be of value. Factors such as personality, self-esteem and
cognitive biases act as the very filter through which we perceive our world.
These factors both contribute to, and are necessary for, the maintenance of
SWB, and seeking to remove this influence would change its very nature.
The influence of personality or other factors cannot be removed from the
measurement of SWB, as they do not just correlate with SWB, but they are
actually an integrated and inseparable part of SWB – that is to say that if
these factors were changed somehow, ones appraisal of their life (SWB)
would also change.
Personality and affect influence our disposition to experience
emotion and partly determines the set-point level of SWB (Headey and
Wearing, 1989), while cognitive buffers cause the effects of extreme life
events to be moderated, thus regulating SWB levels (Cummins, 2002). The
role of affect in the SWB homeostatic system is now considered.
3.2
Affective Factors
The conceptualization of SWB involves the two-factor model of positive
and negative affectivity, wherein the level of “happiness” results from the
balance between positive and negative affect (Shmotkin, 1998). Positive
affect represents the extent to which a person avows a zest for life and is
enthusiastic and optimistic, whereas negative affect is the extent to which a
person feels unpleasantly aroused (Watson & Tellegen, 1985). Individuals
high in positive affect are said to be consistently more enthusiastic,
25
Subjective Wellbeing and Homeostasis
confident, excited and positive in self-regard than those low in positive
affect; individuals high in negative affect are said to be more guilty, fearful,
nervous and negative in self-regard than those with low negative affect
(Berry & Hansen, 1996; Watson & Clark, 1984). Affect is considered to be
based on genuine subjective feelings and moods rather than thoughts or
cognitions about specific events (Russell & Carroll, 1999).
The conceptual structure of affect has been the topic of much debate
among researchers in this area. Before these debates are explored, some
issues regarding the measurement of affect must be noted.
3.2.1
Affect Measurement Issues
The way in which affect is measurement can vastly effect
conclusions drawn about the structure of affect. Firstly, measurement error
associated with different affect scales can influence whether the construct of
affect fits a bipolar or independent model (which will be discussed in the
following section). The particular affect scale used, and the measurement
error associated with it, impacts on the correlation found between the two
dimensions of positive and negative affect (Egloff, 1998; Green & Salovey,
1999). Furthermore, the independence or bipolarity of affect depends on the
precise definition of independence (Cacioppo & Berntson, 1994), the degree
to which terms are antonyms (Green & Salovey, 1999), the measures used
(Egloff, 1998; Green & Salovey, 1999), and the time period over which it is
measured (Egloff, 1998; Veenhoven, 1998).
Studies using the original positive affect and negative affect scales
designed by Bradburn in 1969 were expected to find affect to be bipolar,
26
Subjective Wellbeing and Homeostasis
however their results forced researchers to question the assumption of
bipolarity (Russell & Carroll, 1999). These scales have been found to have
relatively poor reliability, and the more unreliable two scales are, the more
independent they appear (Russell & Carroll, 1999). This suggests either that
there was significant error within the methodological process that led to the
masking of true bipolarity, or that the positive and negative affective factors
are indeed independent of each other (Russell & Carroll, 1999). In fact,
much of the argument regarding the bipolarity versus the independence of
affect is based on the existence of measurement error.
Secondly, the effect of time on affect must be considered
(Schimmack, 2001). People’s affect can change significantly in short
spaces of time, such that it is possible to be sad in one moment and happy in
the next. The time period must be specified clearly for the reliable
measurement of affect. Thirdly, as affect is extremely multidimensional in
nature, it is important that the measurement of affect is based on clear
definitions of positive and negative affect.
With these measurement issues in mind, the debate regarding the
independence versus the bipolarity of affect will be explored.
3.2.2
Independence Versus Bipolarity of Affect
The still unresolved issue regarding the independence of affect
involves the argument that on one hand, negative affect and positive affect
are bipolar opposites, and that on the other they are independent constructs
only weakly correlated with each other (Russell & Carroll, 1999). The term
‘affective bipolarity’ means that affective space is bipolar, and therefore
27
Subjective Wellbeing and Homeostasis
opposite feelings move in opposite directions in proportional magnitude
(Green, Salovey & Truax, 1999). For instance, some view the relationship
between positive and negative affectivity as analogous to temperature, in
that cold is the polar opposite of hot. Temperature can be measured as a
bipolar variable as cold signifies the absence of heat.
The model of affective independence depicts positive and negative
affect as two separate and independent constructs. See Figure 3 for a
comparison of the two models. The assumption of bipolarity is not
necessarily applicable to psychological concepts such as affect, as one
dimension of affect may co-exist with the other (Huelsman, Nemanick &
Munz, 1998). For instance, one factor may not increase at the same rate as
the other decreases. While the states such as hot and cold or short and tall
are mutually exclusive, pleasure and displeasure can be viewed as two
distinct qualities, just as hunger and thirst are two distinct feelings that can
be experienced concurrently (Cacipoop & Bernston, 1994; Schimmack,
2001). Using an example iterated by Huelsman and colleagues (1998), as
tiredness (an element of negative affect) decreases, one’s level of energy (an
element of positive affect) may not increase at the same rate, leaving
individuals neither tired nor energetic, but neutral.
28
Subjective Wellbeing and Homeostasis
PA
NA
Low
PA
High
Low
NA
High
Bipolar model
of affect
Independence
model of
affect
Figure 3 Comparison of Bipolar and Independence Models of Affect
3.2.3
The Evolution of Affect Conceptualization
As was shown in the previous example, the semantic terminology we
use to describe related phenomenon are antonyms (happy vs. sad; depressed
vs. elated), and we hold the belief that the presence of one such state is
contingent upon the absence of the other (Barrett & Russell, 1998). Due to
the opposing semantic quality of affect terminology, it is common to assume
that affect is bipolar in nature (Russell & Carroll, 1999).
Beginning with Nowlis (Nowlis & Nowlis, 1956, in Russel, 1980),
investigators have typically concluded that there are between six and twelve
independent monopolar factors of affect, such as sadness, anxiety, elation
and tension. Research has since concluded, however, that affect states are
related to each other in a highly systematic fashion.
29
Subjective Wellbeing and Homeostasis
The view that affective states are systematically related is illustrated
in Schlosberg’s (1952, in Russell, 1980) proposal that emotions are
organized in a circular arrangement, which means that they are adequately
represented by two bipolar dimensions rather than six to twelve monopolar
ones. The two dimensions that invariably emerge are pleasantness/
unpleasantness and arousal/activation (Watson & Tellegen, 1985).
While affect was initially believed to be bipolar in nature, the
pioneering writers of Nowlis and Nowlis (1956), Bradburn (1969), and
Costa and McCrae (1980) made the counter-intuitive and controversial
claim that opposing states of affect are in fact independent. These writers
questioned the bipolarity of affect, arguing that one’s level of positive affect
(happiness) did not predict one’s level of negative affect (sadness), even at
the same point in time (Barrett & Russell, 1998; Russell & Carroll, 1999).
These conclusions were based on psychometric evidence that what had been
assumed to be bipolar opposites in fact correlated only weakly, and
therefore are better represented as independent of one another.
The two dimensions of valence and activation were represented in a
model created by Russell in 1980, see Figure 4. When plotted against this
dimensional conceptualization, the myriad of affect terms used in his
research fell meaningfully around the perimeter of the space defined by the
axes.
30
Subjective Wellbeing and Homeostasis
AROUSAL
High NA
(Distress)
MISERY
Low NA
(Depression)
High PA
(Excitement)
Activation
Valence
SLEEP
PLEASURE
Low PA
(Relaxation)
Figure 4 Russell’s (1980) Original Cirumplex Model of Affect
In 1985, Watson and Tellegen suggested that positive affect and
negative affect appeared as two separate factors of the construct ‘affect’
using a Varimax rotated orthogonal factor analysis. They presented a model
in which positive and negative affect are two separate dimensions within
affective space that lie at 90 of one another, thus being “descriptively
bipolar but affectively unipolar dimensions” (Zevon & Tellegen, 1982, p
112) which are in fact independent and uncorrelated (Watson & Tellegen,
1985). This model is similar to that of Russell (1980), however the two
major dimensions are positive and negative affect, and the activation of
affect is implied rather than represented as a separate axis (see Figure 5).
This model was used as the theoretical framework for Watson & Tellegen’s
(1985) Positive and Negative Affect Schedule (PANAS) (Huelsman,
Nemanick & Munz, 1998).
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Subjective Wellbeing and Homeostasis
High NA
Engagement
Unpleasantness
Low PA
High PA
Pleasantness
Disengagement
Low NA
Figure 5 Watson & Tellegen’s (1985) Model of Affect
The exclusion of the activation dimension in Watson & Tellegen’s
(1985) model (and indeed in the PANAS) has been criticized by several
authors (Huelsman et al., 1998; Bunk, Brief, George, Roberson & Webster,
1989). These critics suggest that the construct of mood is best measured as
four monopolar dimensions rather than two bipolar dimensions, so the low
poles of mood can be adequately represented. Their rationale is that the low
poles of affect must be measured along with the high poles (the PANAS
measures only high negative and high positive affect), as increases in high
positive affect are not necessarily contingent upon decreases in low negative
affect (Huelsman et al., 1998).
Although there is still disagreement in the field, some consensus is
emerging. Affect appears to be best represented by a circumplex similar to
that proposed by Russell (1980), wherein positive and negative affect is not
merely composed of a set of oppositely-worded mood items, but represents
the correlated but independent interplay between affect direction (positive or
negative) and activation (high or low). Yik, Russell and Feldman Barrett
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Subjective Wellbeing and Homeostasis
(1999) sought to integrate the various conceptual circumplex structures of
affect, including Russell’s (1980) circumplex, Watson & Tellegen’s (1985)
positive and negative affect and Larsen & Diener’s eight combinations of
pleasantness and activation, thereby taking a large step towards unifying
dimensional approaches to affect. They argued that in previous affect
research, the emphasis of affect measurement has been either on valence
(subjective pleasure-displeasure) or activation (also known as arousal or
energy) (Yik, Russell & Barrett, 1999).
This approach to conceptualizing affect, representing both the
valence and the activation of affect as two dimensions within a circumplex,
has become commonly accepted. This model, rather than measuring
positive affect and negative affect with two bipolar dimensions, uses a twodimensional model of affect, covering high and low positive affect (positive
energy and tiredness) and high and low negative affect (negative arousal and
relaxation).
While several authors have valiantly argued their various viewpoints
of affective structure and tested these through complex statistical analyses, it
may be that a simple argument for the circumplex model can be drawn from
the perspective of a psychologist. While affect certainly has a bipolar
nature, such that a person who is happy is generally not sad, the human
psyche and our propensity to experience emotion is undoubtedly more
complex. Emotion cannot be likened to a hypothetical ‘box’ in which only
one affective reaction can fit at one time; in fact there is no evidence to
suggest that a person could not experience every emotion we can label at
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Subjective Wellbeing and Homeostasis
once. The argument for the circumplex can be demonstrated with a simple
example:
A man has been working around the clock and all
through the weekend to finish an important project at work
before taking long service leave. Although exhausted, he is
now able to relax and enjoy his time. The hospital calls, and
tells him his wife and his mother are there. His wife has just
given birth to a beautiful healthy baby boy. His mother was
involved in a burglary and has died. How does he feel?
This vignette illustrates that while affect is descriptively bipolar, the
experience of emotion is best accounted for when measured as four separate
unipolar dimensions.
In concluding the bipolarity versus independence of affect argument,
Barrett and Russell (1998) stated that one can argue for both perspectives,
and that we need not choose one above the other, but that a consensus must
be reached in order to look beyond this theoretical block. It is now accepted
that affect is best represented by a circumplex model, as this most accurately
reflects the properties and true structure of affect through the dimensions of
valence and activation (Russell & Carrol, 1999; Yik, Russell & Barrett,
1999). The circumplex model of affect is explored in greater detail in
Section 2, Chapter 9.
3.2.4
Affect as a Trait or a State
A second long-standing argument between researchers has
concerned the property of ‘happiness’ as a trait or a state (Veenhoven,
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Subjective Wellbeing and Homeostasis
1998). This issue parallels the contentious debate between top-down versus
bottom-up theories of SWB (Diener, 1984). The perspective that happiness
is a trait suggests that happiness and the propensity to feel happy is
predisposed, fitting with the top-down approach (Diener, 1984). The
bottom-up approach suggests that a happy person is one with many happy
moments (Diener, 1984), and that the strength of positive emotion
experienced is equal to their collective positive or negative experiences.
Although there is very interesting and abundant literature on the
trait-state issue (Stones, Hadjistavopolous, Tuuko & Kozma, 1995;
Veenhoven, 1994, 1998), my response is to dissolve this argument with a revisitation of the nomenclature. The term ‘happiness’ has been criticized, as
this term refers to a subjective state, and fails to meet the three criteria of a
trait, being 1) temporal stability, 2) cross-situational consistency, and 3)
inner causation (Veenhoven, 1994). Thus, by viewing ‘happiness’ as
dependent on one’s current affective condition, it is logical and consistent
with research findings that happiness has state-like properties. However,
subjective wellbeing is viewed from a long-term perspective and has close
ties with personality variables, therefore SWB is trait-like in nature.
Therefore, using the term SWB instead of happiness removes some of the
semantic ambiguity and controversy regarding the trait/state argument. The
property of affect as a trait or state can also be clearly investigated, as much
of the literature on the debate stems from this area of research.
It would seem that, as similar affective states seem related over short
periods of time and independent over long periods, affect is a state rather
than a trait (Veenhoven, 1994). Furthermore, the argument regarding the
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Subjective Wellbeing and Homeostasis
independence of affective states may in fact be an argument of whether
affect is a trait or a state, as positive affect and negative affect can appear
state-like or trait-like depending on the timeframe of measurement and
across situations (Stones et al., 1995). Adding complexity to this
assumption, longitudinal research investigating positive affect and negative
affect over the lifespan seems to be fairly uniform in finding that there is a
general decrease in negative affect over time (as people become older) and
that positive affect remains relatively stable in older adults (Charles,
Reynolds & Gatz, 2001; Mroczek & Kolarz, 1998). Therefore, a conclusion
could suggest that it may be possible that individuals have a pure and
momentary state of affect, whilst also having a dispositional and stable
affect level trait (Kozma, Stone, Stones, Hannah & McNeil, 1990).
In agreement with this conclusion, Kammann and Flet (1983)
commented on the state-like and trait-like properties of general happiness as
measured by the Affectometer 2. They stated that happiness is both a
rapidly changing state and a lasting stable trait, with scores reflecting both
short-term and long-term components of SWB. Across multiple studies, the
relationship between positive affect and extraversion, and between negative
affect and neuroticism, has been both at the state level (David, Green,
Martin & Suls, 1997) and at the trait level (Costa & McCrae, 1980, Watson
& Clark, 1984, 1992). Diener (1984, p.550) qualified that “happiness can
be considered both a trait and a state. The trait is a predisposition to
experience certain levels of affect. Such a trait should be measured as
independently from current mood as possible”.
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Subjective Wellbeing and Homeostasis
Up to this point, affect and personality have been discussed
separately and treated as discrete constructs, as they are in much of the
literature. However, emotion and personality are linked in so many ways
and have such similar effects on SWB, it is important to also consider how
they interact and exert their effect on SWB.
3.3
Link Between Personality and Affect
Commonality is noticeable between extraversion and neuroticism
traits, and positive affect and negative affect descriptors (Watson & Clark,
1992). The boundary between the definition and measurement of
personality and affect is certainly blurred. Some affect and personality
items on scales are nearly identical (McCrae & Costa, 1992), for example,
both the neuroticism and the negative affect scales have items that represent
depression, anxiety and distress (Schmutte & Ryff, 1997). Extraversion is
characterized by positive affect, and neuroticism is very closely linked with
negative affectivity (Ryan & Deci, 2001). Affect and personality have been
described as an emotional state and an emotional trait respectively
(Schmutte & Ryff, 1997). The distinction between them thus lies in the
current experience of affect verses enduring tendencies to experience affect
(Schmutte & Ryff, 1997).
Negative affect has repeatedly been found to correlate with
neuroticism but not with extraversion, and likewise positive affect has been
found to correlate with extraversion but not with neuroticism (Berry &
Hansen, 1996; Furnham & Brewin, 1990;Watson & Clark, 1992). Watson
and Clark (1992) attribute this finding to the theory that neuroticism and
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Subjective Wellbeing and Homeostasis
extraversion are personality dimensions that reflect the propensity of the
individual to experience negative and positive affect respectively. Thus,
there seems to be independence between the positive and negative
personality and affective factors (Berry & Hansen, 1996; Rusting & Larsen,
1997; Watson & Clark, 1992). Extraversion and neuroticism most likely
play direct roles in fostering positive and negative affect, whereas the three
other personality traits (conscientiousness, agreeableness and openness to
experience) most likely play an indirect role in influencing SWB (McCrae
& Costa, 1980). Hence, people who experience positive affectivity are
generally high on extraversion, and those who experience negative
affectivity are generally high on neuroticism. The high correlation between
personality measures and affect has been explained with the rationalization
that both ultimately reflect the same common underlying constructs (Watson
& Clark, 1992).
Personality and affect measures are intimately linked, as they are
highly correlated, they relate to individual differences that influence our
outlook on life, and their measurement is often based on very similar items.
The major discriminating factor, however, is that affect refers to an
emotional or mood-based and highly subjective state, whereas personality
refers to a more stable trait. Whilst these constructs have many
commonalities, their separation is important in the investigation of their
influence on SWB.
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Subjective Wellbeing and Homeostasis
3.4
Influence of Personality and Affect on SWB
Many researchers concur that there is an interaction between
personality and affect with regard to their role in determining SWB levels.
While the presence of an interaction between personality, affect and SWB is
unchallenged, much of the data investigating these variables are
correlational, that therefore the direction of influence is unclear. A model of
SWB proposed by Cummins, Gallone and Lau (2002) depicts the
personality dispositions of extraversion and neuroticism directly influencing
the level of positive affect and negative affect, where the balance of these
personality and affective factors determines the set-point level of SWB.
These authors consider that personality is substantially linked to affect, and
both are linked to SWB. This is consistent with the finding that people with
high SWB are generally high on extraversion and people with low SWB are
generally high on neuroticism (Emmons & Diener, 1985; Headey &
Wearing, 1989). The predisposition to neuroticism seems to enhance the
negative mood experienced, thus resulting in decreased SWB levels.
As SWB is influenced by both trait factors (personality) and state
factors (affect), SWB is likely to have both stable and changeable
components. The appraisal of ongoing life events can change, and therefore
hedonic level can change. Emotion, however, returns to an average baseline
level, which is set by trait characteristics (Diener, 1993).
The trait characteristics of extraversion and neuroticism appear
influential in baseline SWB levels, and thus far, only these two main
personality dimensions have been the focus. The remaining three
personality factors in the Big-Five model, Conscientiousness, Agreeableness
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Subjective Wellbeing and Homeostasis
and Openness to experience, do not command as much research focus as the
primary two, however, these factors have also shown a significant positive
relationship with positive affect (Watson & Clark, 1992). People high on
Openness tend to be more susceptible to experience the good and the bad
more intensely; therefore Openness correlates with both positive affect and
negative affect and is unrelated to differences in SWB (McCrae & Costa,
1980). Agreeableness and Conscientiousness both increase positive affect
and decrease negative affect, therefore having a SWB-enhancing effect
(McCrae & Costa, 1980). McCrae and Costa (1980) concluded that
although there is substantially more evidence of the influential nature of
neuroticism and extraversion in SWB than for agreeableness and
conscientiousness, there are both theoretical and empirical grounds for the
inclusion of all five personality measures in studies on personality and SWB
(McCrae & Costa, 1980).
Although extraversion and neuroticism have consistently been linked
to SWB levels by many researchers, it is possible that this link has been
over-interpreted. One hypothesis that potentially complicates the perceived
link between extraversion and SWB, states that the higher levels of SWB of
extraverts is due to higher amounts of social interaction, rather than withinperson, temperamental differences (Pavot, Diener & Fujita, 1990). This
hypothesis, however, does not seem to provide an adequate alternative
explanation as, in the absence of social interaction, extraverts tend to find
other rewards in the environment, hence their high SWB seems more due to
a personality disposition that causes them to experience more happiness,
regardless of the social features of the situation. As the positive affect
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Subjective Wellbeing and Homeostasis
between extraverts and neurotics in non-social environments did not differ
significantly, Pavot and colleagues concluded that time spent in social
situations is not accountable for the increased positive affect in extraverts.
If personality is accepted as a stable individual difference variable unrelated
to situational or behavioural factors, an interesting conundrum concerning
the relationship between personality and affect with regard to their
respective roles in SWB is then revealed.
Are affect and personality essentially the same traits, being
measured and labelled differently, or are indeed separate constructs
involving distinct processes (Nemanick & Munz, 1997)? Research
conducted by Nemanick and Munz (1997) demonstrated a mediational
relationship between trait mood and personality, whereby personality is the
most basic unit of explaining behaviour, and affect is closer to the state
level. These findings suggest that affect and personality are in fact
conceptually distinct, existing at different hierarchical levels.
On the premise that affect and personality are in fact distinct, there
has also been some controversy as to which is the higher order construct, or
which construct influences the other. For instance, Watson and Walker
(1996) argue that personality factors influence affectivity, whereas Tellegen
(1985) suggests that affective factors are higher order traits that either
replace or influence neuroticism and extraversion. Nemanick and Munz
(1997) reported that there is a mediational relationship between trait mood
(affectivity), personality traits (extraversion and neuroticism) and state
mood. They found support for their hypothesis that trait mood and
personality factors exist at different levels, whereby it is through trait mood
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Subjective Wellbeing and Homeostasis
that extraversion and neuroticism have their relationship with an
individual’s state, and that therefore trait mood is a mediator between
personality and current mood. Other researchers perceive affect and
personality to be grouped together, combining their effect on SWB
(Cummins, Gullone & Lau, 2002). Further research is required to confirm
the direction of this relationship.
To conclude, personality factors are known to influence SWB and
explain more variance in SWB than demographic variables (Myers &
Diener, 1995). Similarly, affect, both as a momentary state and as an
enduring trait, is also known to influence SWB levels (Diener, 1984).
Current research takes these findings one step further in proposing that both
personality and affective factors are involved in determining individual setpoint levels of SWB. Although this line of research is still in progress, the
conclusion can be reached that if personality and affective factors determine
SWB levels, then they must likewise influence the predisposition to chronic
lowered SWB, or depression.
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Chapter 4
2nd Order Determinants of Subjective Wellbeing
4.1
Cognitive Buffers
People tend to view their life positively and rate themselves as
‘above average’, possessing what Headey and Wearing (1986) labeled the
“human sense of relative superiority”. This positive perception has been
called ‘positive cognitive bias’ or ‘positive illusions’. Positive cognitive
bias is the general term used to describe the phenomenon of holding a
positive view toward life, however this phenomenon is defined more
specifically as relating to three factors that comprise the cognitive buffers
hypothesized by Cummins and Nistico (2002).
Although personality and affect act as first order determinants of
SWB and determine the set-point of individual normative level of SWB
experienced, it is clear that the maintenance of SWB involves more than
simply these factors. Perceived SWB is a product of cognition as well as
affect, and thus the regulatory cognitive processes involved in SWB
homeostasis should be identified in models of SWB maintenance. The
second order determinants of SWB depicted by Cummins and Nistico
(2002) incorporate the three separate variables of self-esteem, control and
optimism, otherwise known as cognitive buffers.
Positive cognitive biases have been associated with SWB, and
specifically, research suggests that self-esteem, control, and optimism
contribute to the maintenance of SWB. In the model of SWB homeostasis,
the ‘mechanism’ or SWB-maintaining factors proposed by Cummins and
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Nistico (2002) incorporate a tri-partate system of cognitive buffers that
involve self-esteem, perceived control and optimism. These cognitive
buffers will now be considered individually.
4.1.1
Self-Esteem
Self-esteem is an extensively researched area (Cast & Burke, 2002;
Lucas, et al.., 1996; Rosenberg, 1979), and academics and laymen alike are
becoming increasingly aware of the importance of this factor in the healthy
development of individuals. Due to the popularity of this area of study,
there are many definitions and conceptualizations of the construct available.
Self-esteem has been investigated as an outcome (focusing on processes that
produce or inhibit self-esteem), a self-motive (in which people behave in
ways that maintain positive evaluations of the self), and as a buffer
(providing protection from experiences that are harmful) (Cast & Burke,
2002). For the purposes of this study, self-esteem is identified as a
cognitive buffer. It is generally agreed that self-esteem is a cognition, and
can be defined as “liking and respect for oneself” (Rosenberg, 1979:45), and
is composed of the evaluations of both competence and worth. The
competence dimension, or efficacy-based self-esteem, refers to the degree to
which people see themselves as capable, whereas the worth dimension
refers to the degree to which individuals feel they are of value (Cast &
Burke, 2002).
Cummins and Nistico (2002) have extensively researched the area of
self-esteem and satisfaction with the self, and propose that such positive
cognitive reference to the self generates a feeling of satisfaction.
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Subjective Wellbeing and Homeostasis
Furthermore, self-esteem has been found to have a strong positive
correlation with SWB (Boschen, 1996; Hong & Giannakopoulos, 1994;
Lucas, et al.., 1996), and may be the strongest predictor of SWB overall
(Cummins & Nistico, 2002). People with high self-esteem display a
pervasive tendency to cast themselves in more positive and less negative
terms than they portray other people (Brown, 1986), and identify
significantly more with positive characteristics and reject or dismiss
negative characteristics (Taylor & Brown, 1988). In contrast, individuals
with low self-esteem (or moderately depressed) are more ‘even-handed’ or
realistic, and make accurate judgments of their positive and negative
personal characteristics (Coyne & Gotlieb, 1983; Taylor & Brown, 1988;
Watson & Clark, 1984). Although individuals with high self-esteem can be
seen as having an unrealistically positive bias toward the self, Taylor and
Brown (1988) claim that such positive biases are necessary in order to
become a happy and well-adjusted person, and hence necessary for
psychological wellbeing.
There is an emerging consensus that a high degree of self-esteem
variability is associated with negative psychological consequences, as
compared with low-variability or more stable levels of self-esteem
(Oosterwegel, Field, Hart & Anderson, 2001). It has been hypothesized that
individuals with highly variable self-esteem may experience large plunges
in self-esteem rather than small decrements, in response to social cues of
problematic social interaction. The substantial negative affect triggered by a
plunge in self-esteem may overwhelm the system of internal regulation
(SWB homeostasis) leading to avoidance of social interactions
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Subjective Wellbeing and Homeostasis
(Oosterwegel, et al., 2001). These authors suggest that individuals with
highly variable self-esteem experience episodes where the self is sometimes
judged very highly and also sometimes very negatively, where the negative
episodes lead to extremely negative self evaluations, producing the patterns
of cognitions associated with depression and prolonged periods of
dysphoria. In contrast, an individual who experiences low variability of
self-esteem is less likely to experience the episodes of profoundly negative
self-appraisals that create the susceptibility to depressogenic thoughts
(Oosterwegel, et al., 2001). Thus, self-esteem is firstly related to SWB
regulation, and furthermore low or unstable self-esteem appears linked to
(with direction of causality unknown) a breakdown of SWB homeostasis,
leaving the individual vulnerable in a depressogenic state.
The consistently high correlation between self-esteem and SWB
(Diener & Diener, 1995) can lead to two conclusions. Firstly, it could be
that having high SWB (based on satisfaction in different life areas such as
work and relationships) leads individuals to viewing the self in a positive
light, giving rise to high self-esteem. Alternatively, it may be that being in a
state of high self-esteem induces a more positive view of life, and motivates
individuals to engage more actively and positively with life. These different
perspectives argue the direction of causality between self-esteem and SWB.
Self-esteem is most often perceived as a determinant of SWB (Cummins &
Nistico, 2002; Pugliesi, 1988), as self-esteem has been found to be the
strongest single predictor of SWB.
Self-esteem tends to act both as a buffer and as a resource, as it
buffers the effect of negative life events thereby acting to enforce the
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Subjective Wellbeing and Homeostasis
homeostasis of SWB (Cummins & Nistico, 2002), and can be depleted and
thus lead to lowered SWB or satisfaction (Cast & Burke, 2002).
Viewing self-esteem, alongside perceived control and optimism, as a
part of the SWB homeostasis mechanism suggests that the failure or
decrease of self-esteem may coincide with homeostasis breakdown, which
would have important consequences for SWB and depression levels. In line
with this speculation, Cast and Burke (2002) have found that whilst positive
self-esteem increases feelings of competency and worth, disruption of selfesteem has negative emotional consequences, such as distress, in the form of
depression and anxiety. “Self-esteem can buffer the individual from such
negative effects both directly and indirectly. Not only should self-esteem be
associated with higher levels of well-being (direct buffering effect), but selfesteem should also moderate the effects of a lack of self-verification
(indirect buffering effect)” (Cast & Burke, 2002, p1048).
Self-esteem not only gives rise to positive evaluations of the self, but
also acts as part of a system that maintains the homeostasis of SWB. Thus,
self-esteem can be viewed as both an important individual difference, and as
a buffer of SWB that influences resilience to negative life events. In the
model of SWB homeostasis, Cummins and Nistico (2002) depict selfesteem as one of the three cognitive buffers, along with control and
optimism, that regulate SWB levels within a predictable range. Control will
now be discussed.
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Subjective Wellbeing and Homeostasis
4.1.2
Control
The topic of ‘control’ spans a broad conceptual area, and as such,
can be defined in a number of different ways. Control can be depicted as a
positive cognitive bias that takes the form of a generalized belief in personal
competence (Golin, Terrell & Johnson, 1977), a situational appraisal of
personal competence (Folkman, 1984), and as a coping mechanism once a
stressful encounter has taken place (Folkman, 1984). These three functions
of control are now discussed in more detail.
Control as a generalized belief in personal competence can be
thought of as a belief concerning the extent to which outcomes of
importance can be controlled. In a specific stressful encounter, control can
also be thought of as a situational appraisal of the possibilities for control.
From the first perspective, the belief in control exists when a person’s
expectancy of success in a chance-determined event is higher than would be
warranted by the objective probabilities associated with that event (Golin, et
al., 1977). Existing as a belief, control does not need to be exercised for it
to be effective and control does not need to be real, just perceived, for it to
influence the impact of a stressful encounter (Thompson, 1981). Originally
labeled cognitive illusions of control, in recent years it has been found that
unrealistically positive beliefs of control are associated with mental health,
whereas realistic and low belief in personal control is associated with
depression (Cummins & Nistico, 2002; Golin et al., 1977; Golin, Terrell,
Weitz & Drost, 1979).
In exploring the second perspective, control as a situational appraisal
of personal competence can be thought of as comprising two forms;
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Subjective Wellbeing and Homeostasis
“primary appraisal, through which the person evaluates the significance of a
specific transaction with respect to wellbeing, and secondary appraisal,
through which the person evaluates coping resources and options. Primary
and secondary appraisals converge to shape the meaning of every
encounter” (Folkman, 1984:840). Thus, generalized beliefs and situational
appraisal of control affect whether situations are perceived as controllable or
uncontrollable, and thus as positively challenging, or threatening and
stressful.
In the final perspective, control as a coping mechanism can be
viewed as a cognitive mediator of a stressful transaction and its adaptational
outcome (Folkman, 1984). Thus, once a situation is appraised as stressful,
coping efforts mediate the effect of this stressor on subjective quality of life.
Coping can be defined as “constantly changing cognitive and behavioural
efforts to manage specific external and or internal demands that are
appraised as taxing or exceeding the resources of the person” (Lazarus &
Folkman, 1984:141). Previous research has identified many different ways
that individuals cope with stressful situations. Various typologies have been
proposed to describe the functioning of control, including the process of
primary control and secondary control.
Primary control can be generally defined as a belief that one can
influence existing realities so that they fit the needs of the self (Rothbaum,
Weisz & Snyder, 1982), and it is akin to the aforementioned positive
cognitive bias of control. An example of this is the belief that one will lose
weight by exercising. Secondary control, on the other hand, can be defined
as accepting or adjusting to one’s situation, such as when one is having
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Subjective Wellbeing and Homeostasis
trouble loosing weight. Secondary control involves cognitive strategies
such as goal disengagement and social comparison. For example, a
secondary control approach may involve telling oneself that loosing weight
is not important, or comparing oneself to heavier individuals.
A sense of personal control is integral to the self-concept and selfesteem of an individual (Taylor & Brown, 1988), and thus it follows that
people with depression are less vulnerable to the illusion of control
(Abramson & Alloy, 1981). They also show less optimism for outcome
success when they are in control of circumstances and more optimism for
outcome success when circumstances are controlled by chance (Golin,
Terrell & Johnson, 1977).
4.1.3
Optimism
Optimism refers to a positive belief regarding oneself in the future.
Most people believe that the present is better than the past and that the
future will be even better (Brickman, Coates & Janoff-Bullman, 1978).
Robinson and Ryff (1999) claim that perceptions of future wellbeing, or
optimism, are particularly subject to self-enhancement biases, and that selfdeception is greatest under conditions of information uncertainty and high
motivation. Thus, relatively concrete information about the future will
serve to minimize such enhancement. An absence of relatively concrete
information about the future provides an ideal opportunity for envisioning
the best possibilities for oneself.
As well as envisioning positive external circumstances and events,
optimists may also be motivated by their ‘possible selves’, or the vision of
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the achievement of their ideal self that encapsulates the person they would
like to become (Markus & Nurius, 1986). Possible selves are important
because they function as incentives for future behaviour and provide an
evaluative and interpretative context for the current view of self.
Optimism can be viewed as an artifact of personality. Scheier and
Carver (1992) studied a personality variable they identified as dispositional
optimism; the global expectation that good things will be plentiful in the
future and bad things scarce. In another perspective on optimism, Seligman
and his colleagues approached optimism in terms of an individual’s
explanatory style. Consistent with Beck’s (Beck, Rush, Shaw & Emery,
1979) cognitive theory, negative events are attributed to external, unstable
and specific causes by optimists, and attributed to internal, stable and global
causes by pessimists. A final perspective on optimism involves the process
of little optimism and big optimism. ‘Little’ (specific) optimism subsumes
specific expectations about positive outcomes, such as the expectation that a
convenient parking space will be found quickly, whereas ‘big’ (global)
optimism refers to larger and less specific expectations, such as the
expectation that the human race will achieve world peace (Peterson, 2000).
Research has documented diverse benefits of optimism. Optimism
has been negatively related to depression (Alloy & Ahrens, 1987, Brown,
1985), and positively related to adjustment and wellbeing in people with
coronary heart disease (Scheier et al., 1989) and other people affected by
chronic illness (Aspinwall & Taylor, 1992). It has been proposed that
optimism exerts its influence both directly on wellbeing (Aspinwall &
Taylor, 1992), and through coping efforts. In relation to this, optimism has
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Subjective Wellbeing and Homeostasis
most commonly been associated with a higher use of primary control
strategies (Aspinwall & Taylor, 1992; Friedman et al., 1992; Scheier et al.,
1989, Strutton & Lumpkin, 1992). Thus, optimists adopt adaptive coping
strategies dependent on the controllability of the situation. Consistent with
this, Scheier, Weintraub and Carver (1986) found that optimists tend to
adopt strategies that are more effective when coping with stress.
Although optimism is correlated with the absence of depression,
failure and illness, and pessimism with their presence (Peterson, 2000),
psychological wellbeing cannot be simply defined as the absence of distress
any more than physical health is the absence of disease. Optimism has,
however, been linked to SWB and other signs of positive psychological
status (Scheier & Carver, 1992). For instance, when people are optimistic,
they experience positive affect, and pessimistic views give rise to negative
affect (Scheier & Carver, 1992). Optimism is negatively correlated with
several measures of poor psychological wellbeing such as neuroticism and
anxiety, as one might expect, and optimism is also positively correlated with
measures of positive psychological health, such as self-mastery, internal
locus of control and self-esteem (Scheier & Carver, 1992), as well as
positive evaluations of an individual’s expected self (Carver, Reynolds &
Scheier, 1994). Therefore optimism is not merely based on unjustified
beliefs about future outcomes, but can be viewed as a vital mechanism of
positive mental health that aids the regulation of SWB.
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Subjective Wellbeing and Homeostasis
4.2
The Adaptive Outcomes of Positive Cognitive Biases
The three factors that form the cognitive buffers that are
hypothesized to regulate SWB according to the SWB homeostasis theory
(Cummins & Nistico, 2002) have been found to have a positive relationship
with SWB. Furthermore, studies on positive cognitive biases in general
have found that this instinctive positive perception fosters mental health
(Taylor & Brown, 1988; 1994). Thus, it seems that an unrealistically
positive perception of life is necessary for psychological wellbeing, and that
perhaps a realistic, unbiased view of life would cause people to dwell on the
negative aspects, proving detrimental to our wellbeing.
While research discussed above has demonstrated the benefits of
positive cognitive biases, Colvin and Block (1994) argue that there is
insufficient evidence that cognitive biases such as unrealistic optimism are
positively related to mental health. They claim that cognitive distortions
about oneself and one’s social surroundings cannot result in adaptive
behaviour over long periods of time in a world that provides feedback or
reacts on the individual.
Baumeister (1989) suggested a compromise between these
seemingly incommensurable views in his ‘Optimal Margin Hypothesis’. He
proposed that optimal psychological functioning is associated with a slight
to moderate degree of distortion in one’s perception of the self and the
world, such that there is an optimal range for positively biased cognitions.
Provided that cognitive biases are maintained within some homeostatic
range that prohibits the emergence of delusions (defined as being beyond
the normal adaptive range and severely incongruent with reality), they
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constitute an adaptive mechanism for the maintenance of subjective quality
of life. It is possible, therefore, that such positively biased cognitions
constitute an adaptive mechanism that creates and maintains mean
population life satisfaction in the range of 50-100% Scale Maximum. In
summary, positive cognitive biases in self-esteem, primary control, and
optimism have been proposed to constitute an adaptive mechanism for the
maintenance of subjective wellbeing.
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Chapter 5
Depression
Clinical depressive disorders appear in the Diagnostic and Statistical
Manual of Mental Disorders (DSM-IV, 1994) within the category of Mood
Disorder. This category also includes disorders that relate to mania,
however for the purpose of this paper, only the two conditions specifically
implicating depression, being Major Depressive Disorder and Dysthymic
Disorder, will be discussed.
Dysthymic Disorder and Major Depressive Disorder are
differentiated on the basis of their severity, chronicity and persistence
(APA, 1994). The diagnosis of Major Depressive Disorder is contingent
upon the presence of a depressed state for most of the day, nearly every day,
for a period of at least two weeks. In contrast, a diagnosis of Dysthymic
Disorder specifies that symptoms must be present for more days than not
over a period of at least two years (APA, 1994). Major Depressive
Disorders are usually differentiated from Dysthymia in that they involve a
discrete major depressive episode that can be distinguished from a person’s
usual functioning, whereas dysthymia indicates the presence of a less severe
but more pervasive depression that is more typical of the person’s usual
behaviour. In fact, in dysthymic disorder, complaints of depression may
become such a fixture of people’s lives that is seems to be intertwined with
their personality structures (Klein, Taylor, Dickstein & Harding, 1988).
Depression is currently diagnosed according to the presence of signs,
symptoms and functional impairment (APA, 1994). The symptomatology
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Subjective Wellbeing and Homeostasis
of dysthymia and a major depressive episode are fairly similar, including
criteria such as markedly diminished interest or pleasure in almost all
activities, appetite and sleeping pattern changes, feelings of worthlessness,
low self-esteem and recurrent suicidal ideation (APA, 1994).
5.1
Prevalence
According to the lifetime prevalence statistics for major depression,
10% to 25% of women and 5% to 12 % of men are affected by the
condition, with up to 15% of individuals dying by suicide (DMS-IV, 1994).
A consistent finding in the depression literature is the preponderance of
depression in females, which can be as significant as 2:1 (Amenson &
Lewinsohn, 1981). Furthermore, women who have suffered from
depression in the past are more susceptible to becoming depressed again
(22%) than men with a similar history (13%). The artefact hypothesis
explains the sex difference in depression by stating that the actual
prevalence of depression is equal among the sexes, but that women
perceive, acknowledge, report and seek help for depression more freely than
men (Amenson & Lewinsohn, 1981). If this is indeed the case, the true
prevalence of depression may be higher due to unreported incidents of
depression within the community.
Depression is the most common mental disorder in industrialised
societies (Flynn & Cappeliez, 1993). Depression is, in fact, about ten times
more prevalent than it was 50 years ago, as shown by lifetime prevalence
studies and the relative absence of depression in pre-modern cultures.
These facts point to the conclusion that there seems to be something about
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modern life that creates fertile soil for depression (Seligman, 1990). Thus, it
is imperative that we, as a community, be aware of the most effective
methods of diagnosing and treating these individuals if we are to make a
significant impact on the overall SWB of our society.
5.2
Depression Assessment
The fundamental tool of psychological assessment is the clinical
interview. The clinical interview can be structured or unstructured, and
serves the purpose of gathering information such as behavioural
observations, idiosyncratic features of the client, and the person’s reaction
to his or her current life situation (Groth-Marnat, 1999). It is also a means
for building rapport and can serve as a check against the meaning and
validity of test results.
The Schedule for Affective Disorders and Schizophrenia (SADS,
Endicott & Spitzer, 1978) is an extensive semi-structured interview that is
commonly used in a clinical setting for the diagnosis of depression and
other affective disorders. The adult version of the SADS contains over 200
items, which fall into eight summary scales including: mood and ideation;
depressive-associated features; and suicidal ideation and behaviour.
The clinical interview is often used in conjunction with self-rated
questionnaire inventories. These inventories allow for confirmation of
conclusions drawn from clinical interviews, and also provide a means of
brief psychological evaluation in situations where clinical assessment is
time limited or not available (i.e., for the purpose of research, etc).
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Subjective Wellbeing and Homeostasis
The Beck Depression Inventory (BDI, Beck, Ward, Mendelson,
Mock & Erbaugh, 1961) and the Hospital Anxiety and Depression Scale
(HAD, Zigmond & Snaith, 1983) are two popular self-rated inventories of
depression. These inventories comprise a set of items supposedly reflective
of depression symptomatology.
The BDI has been widely used for the assessment of cognitions and
physiological symptoms associated with depression, and has been found to
do so just as effectively as longer and more costly structured interviews
(Stukenberg, Dura & Kiecolt-Glasser, 1990). The HAD scale is a
questionnaire designed to detect the presence and severity of depression and
anxiety in medical outpatients and the general community. The items in this
scale make no reference to physical problems, ensuring that scores are
independent of physical illness (Bowling, 2001).
Three criticisms of depression inventories follow. Firstly, only
negative items are listed. Therefore, inventories have a disease-focus rather
than representing a continuum of depression through to happiness, therefore
a low score is merely indicative of a lack of depressive symptoms.
Furthermore, responding to a list of negatively worded leading items may
have the effect of depressing the subjects further than their previous baseline
level, rendering the score less valid.
Secondly, a proportion of the items on many depression inventories
are representative of physical symptoms of depression, such as sleeping and
eating patterns. Although these items do reflect depression symptoms and
address DSM-IV (1994) criteria, they blur the boundary between physical
and psychological aspects of depression. The HAD scale is one of the few
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inventories that has addressed this limitation by separating the psychological
from the physical symptoms of depression, and only measures the
psychological aspects.
Finally, the items in depression questionnaires are not focused on
different areas of life. Thus the scores gained in the diagnostic process from
these inventories only indicate a level of depression, generally being none,
mild, moderate and severe. Without the specification of the area of life in
which a person is depressed or experiences the highest level of depression,
the inventories merely assist the diagnostic process rather than guide
treatment.
5.3
Models of Depression
To ensure that this review of depression is as brief and relevant to
the thesis topic as possible, only the biological and cognitive aetiological
framework and diathesis-stress perspective will be covered.
The biological model of depression states that abnormal genetic or
biochemical processes predispose some individuals to depression. Genetic
factors appear to play a substantial role in depression, however psychosocial
and environmental factors, such as exposure to stressful life events, may
play an equal or even greater role than genetics in determining the risk of
the disorder (Kendler, Neal, Kessler & Heath, 1993). The dominant
biological model of depression during the past 30 years involves the
Catecholamine Hypothesis (Schildkraut, 1965), and focuses on the role of
norepinephrine. The catecholamine hypothesis proposes that depression
results from deficiencies in one of the catecholamines, norepinephrine, and
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Subjective Wellbeing and Homeostasis
as such, antidepressant medication acts on depression by increasing brain
levels of neurotransmitters such as norepinephrine and serotonin.
In contrast, the cognitive perspective purports that the way people
typically interpret or understand events in their lives (i.e., their cognitive
styles) has an important effect on their vulnerability to depression (Alloy,
Abramson & Francis, 1999). The two major cognitive models of depression
are Beck’s theory (1967; 1987) and the Hoplessness theory (Abramson,
Metalksy & Alloy, 1989; 1995; Alloy, Abramson, Metalsky & Hartlage,
1998).
Beck’s Theory of Depression
According to Beck’s theory of depression, three sets of cognitive
concepts explain aspects of depression: 1) maladaptive self-schemata; 2)
cognitive distortions; and 3) the cognitive triad (negative views about the
self, the world and the future) (Alloy & Abramson, 1999; Coyne & Gotlib,
1983).
The first major ingredient in Beck’s (Beck et al., 1979) cognitive
model consists of the concept of schemas. This concept is used to explain
why a depressed patient maintains pain-inducing and self-defeating attitudes
despite objective evidence of positive factors in life (Beck et al., 1979).
Schemas constitute the basis for screening out, differentiating and coding
the stimuli that confront the individual. In this theory, maladaptive
schemata containing dysfunctional beliefs involving themes of loss,
inadequacy, failure and worthlessness, constitute the cognitive vulnerability
to depression. When these hypothesised depressogenic self-schemata are
activated by the occurrence of negative life events (stress), they generate
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specific negative cognitions (cognitive distortions) that take for form of
overly pessimistic views of oneself, the world, and the future (Alloy &
Abramson, 1999). In the absence of activation by negative events, however,
the depressogenic self-schemata remain latent and less accessible to
awareness, and do not directly lead to negative automatic thoughts or
depressive mood and symptoms (Beck et al., 1979).
Cognitive distortions are systematic errors in thinking that maintain
the patient’s belief in the validity of negative concepts despite the presence
of contradictory evidence. Specific negative thought patterns include (Beck
et al., 1979, p14):
1. Arbitrary inference - refers to the process of drawing a
specific conclusion in the absence of evidence to support the
conclusion
2. Selective abstraction – consists of focusing on a detail taken
out of context, ignoring other more salient features of the
situation and conceptualising the whole experience on the
basis of this fragment
3. Overgeneralisation – refers to the pattern of drawing a
general rule or conclusion on the basis of one or more
isolated incidents and applying the concept across the board
to related and unrelated situations.
4. Magnification and minimisation – are reflected in errors in
evaluating the significance or magnitude of an event that are
so gross as to constitute a distortion
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5. Personalisation – refers to the patient’s proclivity to relate
external events to themself when there is no basis for making
such a connection
6. Absolutistic, dichotomous thinking – is manifested in the
tendency to place all experiences in one of two opposite
categories; for example, flawless or defective, immaculate or
filthy, saint or sinner. In describing himself, the patient
selects the extreme negative categorisation.
The cognitive triad consists of three major cognitive patterns that
induce the patient to regard themself, their future, and their experiences in
an idiosyncratic manner. The first component of the triad revolves around
the patient’s negative view of himself. They see themself as defective,
inadequate or deprived. They tend to attribute his unpleasant experiences to
a psychological, moral or physical defect in themself (Beck et al., 1979).
The second component relates to the patient viewing the world as making
exorbitant demands on them and presenting insuperable obstacles to
reaching their life goals. The third component consists of a negative view
of the future. As the depressed person makes long-range projections, they
anticipate that their current difficulties or suffering will continue
indefinitely. They expect unremitting hardship, frustration and deprivation.
When they consider undertaking a specific task in the immediate future,
they expect to fail (Beck et al., 1979).
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The Learned Hopelessness Model of Depression
The learned hopelessness model of depression (Alloy & Abramson,
1999), offers the prediction that depressed individuals deny responsibility
for positive outcomes, but see themselves as accountable for negative
outcomes (Ruehlman, West & Pasahow, 1985). According to this theory,
the expectiation that highly desired outcomes will not occur and that highly
aversive outcomes will occur, and that one cannot change this situation –
hopelessness – is a proximal sufficient cause of the symptoms of depression
(Alloy & Abramson, 1999).
This theory purports that negative life events (or the non-occurrence
of positive life events) set the occasion for people to become hopeless.
“Hopelessness and, in turn, depressive symptoms are likely to occur when
negative life events are attributed to stable (ie., enduring) and global (ie.,
likely to affect many outcomes) causes and viewed as important, or likely to
lead to other negative consequences, or as implying that the person is
unworthy or deficient” (Alloy & Abramson, 1999, p228). In the absence of
negative life events, it is predicted that people exhibiting the depressogenic
inferential style should be no more likely to develop hopelessness and, in
turn, depressive symptoms, than people not exhibiting this style.
While the two major cognitive models of depression differ in the
specific components that describe the relationship between negative life
events, cognitive style and depression, they have essentially the same
underlying conceptualisation. “While the focus of each of these models
varies, they all share a primary emphasis on cognitive factors that play a
role in vulnerability to depression and/or in the maintenance of depression”
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(Ruehlman et al., 1985, p47). Beck’s theory and the learned hopelessness
theory both state that negative cognitions play a causal role in depression.
This contrasts with more traditional conceptualisations, that consider
negative thinking a symptom or consequence of depressed affect (Coyne &
Gotlib, 1983). Both models emphasise the importance of idiosyncratic
causal schemata in the development of an expectation of hopelessness,
which makes individuals vulnerable to depression (Alloy & Ahrens, 1987).
Diathesis-Stress Perspective
These contemporary cognitive perspectives of depression are
articulated in terms of diathesis-stress models (Flynn & Cappeliez, 1993;
Simons, Angell, Monroe & Thase, 1993). The major depression models
(Beck, 1964; Abramson, Metalsky & Alloy, 1989) are built on the basic
notion that depression can occur when a specific cognitive vulnerability
(diathesis) interacts with a specific negative life event (stress) that matches
the domain of the cognitive vulnerability (Simons, Angell, Monroe &
Thase, 1993).
According to the diathesis-stress model, depression is only a likely
outcome if the diathesis (cognitive vulnerability) and stress (life event)
match. This presupposition was articulated by Simons and colleagues
(1993, p584), who stated that “Neither the presence of the cognitive
vulnerability factor in the absence of a domain-congruent life stress nor the
occurrence of negative life events in the absence of a cognitive vulnerability
in the same domain are deemed sufficient to initiate an episode of
depression”.
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Cognitions may influence the perception of stress and experience of
SWB/depression in a variety of ways. Firstly, cognitive factors, such as
dysfunctional attitudes and attributional style, may influence the definition
of negative life events by lowering the threshold for defining an experience
as stressful. Second, cognitive factors may influence the rating or
evaluation of stress (Simons, Angell, Monroe & Thase, 1993). This was
illustrated with the example that individuals with strong beliefs about
performance as an index of self-worth may be more likely to rate workrelated events as very stressful (Simons, Angell, Monroe & Thase, 1993).
Some evidence has been found for the cognitive model of depression
(Alloy et al., 1999). Firstly, Alloy and colleagues (1999) found that
negative cognitive styles indicated risk for both first onset and recurrences
of clinically significant depression. Furthermore, the association between
cognitive vulnerability and the development of suicidality was completely
mediated by hopelessness, as only those participants who became hopeless
about their futures developed suicidality (Alloy et al., 1999).
It is important to note, however, that the authors did not establish a
causal link between cognitive style and depression. However, cognitive
therapy, which aims to change the cognitions and irrational beliefs held by
depressed clients, has been found to be an effective treatment for depression
(DeRubeis & Hollon, 1995). This alludes to a causal mechanism whereby
altering (improving) cognitions results in reduced depression.
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5.4
SWB and Depression
There is substantial evidence that SWB is inversely related to
depression (Barge-Schaapveld, Nicolson, Berkhof & deVries, 1999; de
Leval, 1999; Hansson, 2002; Holloway & Carson, 1999; Kammann & Flett,
1983; Koivumaa-Honkanen, Honkanen, Hintikka, & Honkalampi, 2001;
Pyne, Patterson, Kaplan, Gillin, Koch & Grant, 1997). SWB levels are
markedly reduced during a current state of depression (Pyne et al., 1997),
and increase concurrently as symptoms of depression subside (Hansson,
2002; Holloway & Carson, 1999; Koivumaa-Honkanen et al., 2001).
There is a strong link between SWB and depression, such that
people with depression have a worse SWB than people with other
psychiatric disorders, and depression accounts for greater reductions in
SWB than common physical illnesses such as hypertension and cardiac
diseases (Hansson, 2002). In fact, depression accounts for approximately
21% of the variance in SWB (Holloway & Carson, 1999).
The relationship between a self-rated four-item SWB scale and the
Beck Depression Inventory was tested by Koivumaa and colleagues (2001).
The four items of their SWB scale comprised:
Do you feel that your life at present is:
1) very interesting (1), fairly interesting (2), fairly boring (4) or very
boring (5),?
2) very happy (1), fairly happy (2), fairly sad (4), or very sad (5),?
3) very easy (1), fairly easy (2), fairly hard (4), or very hard (5),?
Do you feel at the present moment you are:
4) very lonely (5), fairly lonely (4), or not at all lonely (1)?
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They found that this SWB measure correlated strongly with the
widely used 21-item BDI, as well as the Hamilton Depression Rating Scale.
Secondly, SWB improved concurrently with recovery from depression,
regardless of the severity of depression. SWB scores remained low in those
people whose depression scores did not improve, however SWB showed a
pronounced improvement and reached the normal general population level
in those whose depression scores improved (Koivumma-Honkanen et al.,
2001).
As reflected both in epidemiological and clinical studies it is quite
evident that persons with major depression have a substantially lower SWB
than those with no depression, and that SWB is severely affected in a
number of life domains (Hansson, 2002).
While the onset of an episode of depression co-occurs with
pronounced decreases in SWB (Barge-Schaapveld, et al. 1999), the
direction of causality between lowered SWB and depression is as yet
unknown. Thus, there is some controversy regarding the extent to which
satisfaction is the opposite of depression. Although they are negatively
correlated, it is still unknown to what extent these states are due to different
causes (Argyle & Martin, 1991).
Overall, depressed individuals have a reduced SWB (BargeSchaapveld et al., 1999; de Leval, 1999). SWB scores are negatively
correlated with depression scores (de Leval, 1999; Pyne et al., 1997), and
depression has been found to significantly predict low SWB (BargeSchaapveld et al., 1999; Hong & Giannakopoulos, 1994; Valiant, 1993),
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however the direction of causality between depression and SWB cannot be
determined from the observed high correlations.
5.5
Theory of SWB Homeostasis and Depression
The theory of SWB homeostasis (Cummins, 1998) was discussed in
earlier chapters, and refers to the systematic regulation of SWB that is
maintained by 1st order and 2nd order determinants, being personality and
affect, and cognitive buffers respectively (model presented in Figure 2). As
SWB has a strong relationship with depression, it is important to investigate
the relationship between depression and factors that are proposed to regulate
SWB. Therefore, this section of the chapter will do this and, moreover,
present a model of depression based on the SWB theory, and rationale for
the use of SWB inventories in depression diagnosis and treatment.
5.5.1
Personality, Affect and Depression
There is robust evidence of the related but separable relationships
between SWB and positive and negative affect (Huebner & Dew, 1996).
Likewise, there is evidence that personality and affective factors similarly
influence depression (Huebner & Dew, 1996). The interplay between
depression and SWB has been primarily studied through the correlation
between personality, positive affect and negative affect (Lewinsohn, Redner
& Seeley, 1991).
Studies have found evidence of the relationship between personality
and depression. From the original works of Eysenck (1981), high
neuroticism scores have been linked with depression, and low extraversion
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with dysthymia. The personality factors of high neuroticism and low
extraversion appear to correlate with, and predict depression (Valiant,
1993). Thus, while there is not a depressed personality style as such, people
who are high on neuroticism and low on extraversion tend to perceive the
world more negatively, and may be more susceptible to depression.
Likewise, there is a relationship between affect and depression.
People high in duration and intensity of negative affect will often experience
depression, whereas people high in duration and low in intensity of negative
affect will be better characterized as melancholic or dysthymic most of the
time (Diener, 1984; Elliott, Marmarosh & Pickelman, 1994). Affectivity is
also individually associated with depression (Roberts & Kassel, 1996).
High negative affect appears to be associated with both depression and
anxiety, whereas low positive affect is uniquely associated with depression,
and represents anhedonia (Roberts & Kassel, 1996). Thus, depression may
result from a combination of high negative and low positive affect. The
combination of high negative affect and low positive affect may also be
potentially associated with vulnerability to depression (Roberts & Kassel,
1996).
5.5.2
Cognitive Buffers and Depression
Depression coincides with a failure of the cognitive buffers,
including: diminished levels of perceived control (Abramson & Alloy,
1981; Alloy, Abramson & Viscusi, 1981; Benassi & Mahler, 1985; Golin,
Terrell & Johnson, 1977; Golin, Terrell, Weitz & Drost, 1979; Schwartz,
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Subjective Wellbeing and Homeostasis
1981); decreased self-esteem (Alloy & Ahrens, 1987); and decreased
optimism (Pyszczynski, Holt & Greenberg, 1987).
Lowered self-esteem, lowered control and lowered optimism are all
implicated in people with depression (Alloy & Ahrens, 1987), which lends
support to the SWB homeostasis theory, which depicts these three factors as
part of the tri-partite cognitive buffer system. Cognitive factors may also
influence the generation of negative life events. Dysfunctional styles may
shape behaviour thus creating circumstances in which negative life events
are more likely to occur (Simons, Angell, Monroe & Thase, 1993).
5.5.3
Hypothesised Model of SWB Homeostasis Breakdown
According to the SWB homeostasis theory, SWB is managed, for
each individual, within a set-point range (Cummins, 2000). The SWB level
for population mean scores lie between 70-80%SM, and it is estimated that
the threshold for each individual is 10%SM around their set point. The
threshold is proposed to exist at the margins of the set-point-range
(Cummins et al., 2002). As SWB approaches these margins, the
homeostatic system resists further change, and if the threshold is exceeded,
attempts to bring SWB levels back within the normal range for the
individual person (Cummins et al., 2002).
The theory predicts that people who suffer some event that depresses
their SWB below threshold should experience increases in their SWB levels
over time until homeostasis is regained. However, all homeostatic systems
have their limitations. Severe or chronic conditions to which a person
cannot adapt, such as extreme poverty, can precipitate homeostatic defeat.
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Subjective Wellbeing and Homeostasis
Thus, provided that the homeostatic system is not overly challenged, the
system will adapt to the presence of negative circumstances with little
discernable influence on SWB. However, as the strength of an extrinsic
influence increases, at some value it will exceed the adaptive capacity of the
homeostatic system and will be defeated (Cummins et al., 2002). This
curvilinear relationship between the strength of the extrinsic agent and the
value of SWB is depicted in Figure 6.
Dominant Source of SWB Control
Extrinsic Conditions
Homeostasis
Extrinsic Conditions
High
SWB
Lower
Threshold
Low
Strong Negative
Upper
Threshold
Neutral
Strong Positive
Strength of the Extrinsic Conditions
Figure 6 The Relationship Between SWB and Extrinsic Conditions
(Cummins et al., 2002)
The theoretical understanding of the relationship between extrinsic
conditions and SWB homeostasis allows the following specific predictions,
as presented by Cummins Gullone & Lau (2002):
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Subjective Wellbeing and Homeostasis
1. Under maintenance conditions, where no threat to
homeostasis can be recognised, there should be no systematic
relationship between the objective circumstances of people’s
lives and their SWB. This is because homeostasis, not the
extrinsic conditions, are controlling levels of SWB.
2. Under non-maintenance conditions, where the homeostatic
system is facing defeat, the relative strength of the
relationship between extrinsic condition and SWB changes.
Here, the extrinsic conditions are the dominating force,
defeating homeostasis, and thereby, wresting control of SWB
by causing it to rise or fall. Under these conditions, the
correlation between SWB and the extrinsic condition is much
enhanced.
3. There will be a law of diminishing returns in the ability of
improved objective conditions to cause an increase in SWB.
That is, in conditions of marked deficit, many of the
objective indicators will have the power to control SWB. For
example, chronic poverty, friendlessness, lack of safety, etc.
However, if such circumstances are improved to the point
that they are no longer instrumental in causing homeostatic
defeat, further improvements are predicted to have little
further effect on SWB for two reasons. First, control has
been returned to the homeostatic system, and so further
improvements will be absorbed by the system, effectively
holding the SWB output constant. Second, if a sudden,
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Subjective Wellbeing and Homeostasis
marked improvement occurs that exceeds the upper
threshold, the processes of adaptation will quite rapidly
diminish the impact of this new experience, and, once again,
return control to homeostasis.
The first and second predictions are evidenced with the finding that
SWB scores tended to be independent of events, whereas depression was
more closely related to events (Valiant, 1993). This finding can be related
to the hypothesised model of SWB homeostasis breakdown in the following
manner: SWB is generally stable and resistant to fluctuations caused by
external events due to the effect of the cognitive buffers that moderate the
impact of events. However, if depression is accepted as a state of
homeostatic breakdown, depression may be more related to external events
because the cognitive buffers are unable to operate in conditions of
homeostatic breakdown.
Likewise, evidence has been found for the third prediction. The
stress associated with chronic poverty (involving life conditions such as
inadequate housing, inability to meet basic needs, and financial uncertainty)
can be hypothesised to be a significant risk factor for SWB homeostasis
breakdown, which could lead to depression. Poverty is, in fact, one of the
most consistent correlations of depression. High levels of depression are
common amongst those with low incomes (Belle, Longfellow & Makosky,
1982; Kaplan, Roberts, Camacho & Coyne, 1987), and in fact rates of
depression have been estimated to be twice as high amongst low income
earners and homeless individuals.
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Subjective Wellbeing and Homeostasis
One of the most consistent findings regarding the onset of
depression is that stressors precede an episode of depression (Lewinsohn et
al., 1988). In fact, even ‘microstressors’ or minor daily unpleasant events
have been associated with depression. The SWB homeostasis model
predicts that the level of one’s set-point range relates to the extent to which
an individual is robust to negative life events, in that a high set point range
indicates a high level of resistance, and a low set point range indicates that
an individual has a fragile homeostatic system and is therefore prone to
depression. Consistent with the finding that neuroticism and negative affect
are correlated to dysthymia and depression, individuals with high levels of
these factors can be expected to have a predisposition toward homeostatic
failure, and therefore to suffer depression.
The occurrence of depression can be conceptualised within the SWB
homeostasis framework, and is depicted in the hypothesised model in Figure
7. The original model of SWB homeostasis (Cummins, 1998) depicts
personality and affect as 1st order determinants, and cognitive buffers as 2nd
order determinants, of SWB. The hypothesised model builds on the original
by indicating the direction of influence both from the cognitive buffers to
SWB (note: depression is also acknowledged here as a form of
psychological wellbeing), and also from SWB to the cognitive buffers. This
identifies the cyclic or bi-directional relationship between our cognitions
and our level of wellbeing.
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Subjective Wellbeing and Homeostasis
Positive
Events
Personality
Cognitive
SWB
Affect
Biases
Depression
Negative
Events
Figure 7 Hypothesised Model of Depression Based on the SWB
Homeostasis Theory
As the feedback arrows between SWB and the cognitive biases is the
only change from the original model of SWB homeostasis, it is necessary to
discuss the reasoning for this addition. According to Beck’s Cognitive
Model of Depression, core beliefs or schemata, in combination with the
extrinsic events we experience, influences our tendency to generate negative
automatic thoughts, which may lead to a vulnerability to depression. These
negative automatic thoughts then influence our emotional and behavioural
reactions, which reinforce the negative cognitive bias and the generation of
negative automatic thoughts. This process is illustrated in Figure 8 adapted
from Beck (1995).
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Subjective Wellbeing and Homeostasis
‘Schemas’ / Core Beliefs
Incident
Automatic thoughts
Physiological response
Emotional reaction
Anxious / Depressed
Behavioural response
Figure 8 Beck’s Negative Thought Cycle
Furthermore, there is a bi-directional relationship between
cognitions and psychological wellbeing, in that our cognitive evaluations of
specific events or domains of life can influence our overall level of SWB (or
lack of SWB), and our general feeling of happiness and satisfaction with our
lives (or lack of), influences our specific cognitive evaluations.
The bi-directional nature of SWB and potential relationship between
SWB and depression is illustrated with the statement by Weerasinghe &
Tepperman (1994, p202): “Often, when we are satisfied with life as a whole,
we feel satisfied with particular domains of life. At other times, when we
feel dissatisfied with a particular domain of life, we come to feel dissatisfied
with life as a whole. Our feelings about one domain ‘spill over’ into our
feelings about life and we may even come to feel like everything is falling
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Subjective Wellbeing and Homeostasis
apart”. This statement emphasises the reciprocal process between domainbased satisfaction and the general feeling of happiness. It also infers the
potential of unhappiness in one (or several) domain/s to incite a general
feeling of unhappiness, which could potentially lead to depression.
As this is the case, the arrows indicating a relationship between
SWB/depression and the cognitive biases were included to represent the
feedback loop between the overall level of psychological wellbeing and
cognitive evaluation of events. This model of depression causality appears
more robust, though it is yet to be tested, as it accounts for the individuality
of each person regarding their sensitivity and resilience to homeostatic
failure. As the mean SWB level for individuals is 75% out of 100% and
normal SWB set-point range spans between 50% and 80%, it can be
hypothesized that depression occurs within the lower range of SWB. Due to
the lack of testing, it is not known precisely at what level depression is
indicated, however it can be assumed that most people with a SWB level
below 50% would be depressed. It is on this premise that the conclusion is
made that measures of SWB could be used to indicate depression.
5.5.4
Rationale for the use of SWB indicators in depression
diagnosis and treatment evaluation
The main application of subjective wellbeing scales in the literature
to date has been around social indicators research and population
comparative studies, whereby results have been used to make broad
statements about national or sub-population wellbeing levels. While this
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Subjective Wellbeing and Homeostasis
literature has provided invaluable information about different populations
and cultures in relation to SWB, there is justification to steer SWB research
to a new direction, wherein the focus is on the individual rather than
populations.
There is a recognised need for a continuous measure of happiness
and depression to assess mood, that is, for a measure to span the breadth of
emotional state from deep depression to high subjective wellbeing
(Kammann & Flett, 1983; Pyne et al., 1997). Depression inventories used
to indicate and monitor depression do not adequately address quality of life
issues (Pyne et al., 1997). A carefully constructed measure of SWB is
advantageous over traditional measures of depression because it reflects
affective experience along both ends of the continuum, including positive
feelings and life enjoyment (Kammann & Flett, 1983). Although clinical
and medical research aims to describe and classify sickness, psychologists
and social scientists are often more interested in quantifying a continuum of
affect (McGreal & Joseph, 1993). Depression inventories have the ability to
indicate the level of depression, but not the level of happiness or SWB.
Traditionally, depression rating scales have indicated high levels of
depression with high scores, and low levels of depression with low scores.
The lowest possible score, 0, represents merely the absence of depression,
but not the level of SWB. Although this was precisely the purpose for
which these tests were designed, we now understand that health and
wellbeing do not merely consist of the absence of illness (Ryan & Deci,
2001). An absence of depression could occur at different levels of positive
wellbeing (Kammann & Flett, 1983:261), and the absence of psychological
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Subjective Wellbeing and Homeostasis
symptoms does not guarantee happiness at all (Lu & Shih, 1997).
Therefore, it is both the level of depression and the level of SWB that is of
interest to practitioners. It should be stated that the hypothesis that
depression is the opposite of SWB is not supported in this paper. It is
instead assumed that happiness is not merely the absence of depression, or
vice versa, but that these states are distinctly different experiences from
each other (Valiant, 1993). However, it is argued herein that practitioners
need to recognise and measure both depression and SWB in order to gain a
holistic perception of, and effective treatment plan for, the individual.
In further recognition of the need to evaluate both depression and
SWB levels, researchers have discovered that domain compensation may
assist the homeostasis of SWB (Best, Cummins & Lo, 2000). This means
that it is possible for an individual to have low SWB in certain domains of
life, with high SWB levels in other areas, thereby maintaining the overall
SWB level in the normal range. A measure of SWB based on judgments of
satisfaction within different life domains would thus be able to detect
depression as well as detecting the areas of life that are associated with an
individual’s depression. With this consequence at hand, it would be
possible to distinguish between a general depression indicated by depressed
levels of SWB in all domains of life, and a specific depression in which few
life domains are implicated. Best, Cummins and Lo’s (2000) hypothesis of
domain compensation could be tested through analysis of depression and
SWB levels in each domain.
An inventory created by McGreal and Joseph (1993), the
Depression-Happiness Scale, compensates for the conceptual division
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Subjective Wellbeing and Homeostasis
between depression and SWB by adding reverse-scored inverse items
mirroring the Beck Depression Inventory items, thus quantifying the
continuum of affect. However, a measure such as the DepressionHappiness scale can only provide researchers with a participant’s overall
score. Although this is useful for gaining an overall indication of an
individual’s level of satisfaction with their life, it does not indicate in which
area the individual is satisfied/depressed. The research proposed for the
current thesis extends McGreal and Joseph’s (1993) investigations. SWB
inventories that measure satisfaction in particular life domains, as does the
Personal Wellbeing Index (Cummins, 2002), may be sensitive to important
differences among depressed individuals that are overlooked by the usually
employed depression measures (De Leval, 1999). The Personal Wellbeing
Index could therefore be expected to screen for and assign a score of SWB,
and levels of SWB lower than a certain point could be found to be indicative
of depression. Furthermore, the scale could also indicate the life domains
that are associated with the depression. This information would be
extremely beneficial for the selection of specific treatment options for the
individual.
An implication of the proposed research is that SWB measures used
to screen for depression could lead to better understanding of the nature of
depression in an individual, therefore enabling more specific and efficacious
treatment options to be devised. SWB measures could be used in clinical
settings to ensure that the focus is placed on the patient and their
ideographic and phenomenological experience of their condition (whether
physical or psychological), rather than on the disease or condition itself
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Subjective Wellbeing and Homeostasis
(Higginson & Carr, 2001). Some of the recognised advantages of SWB
measures used in clinical practice include the ability to prioritise problems
and treatment preferences, and monitor SWB changes in response to
treatment (Higginson & Carr, 2001).
In a study conducted by Rudolf and Priebe (1999), depressed women
expressed dissatisfaction both with life as a whole and in four out of eight
specific life domains. Their results indicated clinical utility of using SWB
measures with clients with depression, both for diagnostic specificity and to
evaluate symptom improvement over time (Rudolf & Priebe, 1999).
The efficacy of SWB inventories used as both a depression
screening tool and basis for treatment was tested by Frisch (1994). The
theoretical conceptualisation of ‘dissatisfaction depression’, an etiologically
distinct subtype of clinical depression, is caused by a combination of
negative self-evaluation and hopelessness, which in turn are based on
repeated failures to fulfil aspirations and meet personal standards in highly
valued areas of life (Grant, Salcedo, Hynan, Frisch & Puster, 1995). Based
on this hypothesis, Frisch and colleagues (Frisch, 1992, 1994; Frisch,
Cornell, Villanueva & Retzlaff, 1992) devised an integrative, cognitivebehaviourally based ‘Quality of Life Therapy’. This depression therapy
offers specific treatment strategies for each life domain assessed by the
Quality of Life Inventory, including health, self-esteem, relatives, home,
neighbourhood, love, community and others.
This therapy was 75% effective in reducing depression, clearly
exceeding the approximate 50% improvement rate associated with
antidepressant medication (Grant et al., 1995). These results suggest that
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Subjective Wellbeing and Homeostasis
depression treatment may be more effective for the individual if their
depression is examined in the context of specific life domains, enabling
treatment to target the most affected areas. Following from this research, it
is foreseeable that the concurrent analysis of depression and SWB may lead
to the assessment of depression within specific life domains, which may
then lead to the development of specialised treatments for each depression
classification.
As well as being potentially used as a depression screening tool,
significant evidence suggests that SWB inventories may be used to evaluate
the efficacy of depression treatment and the alleviation of depression
symptoms (Koivumma-Honkanen et al., 2001). “Recognition of the burden
that depression creates in major life domains has led to increased efforts to
assess QoL in clinical research. In addition to clarifying the impact of the
illness on patients’ lives, QoL instruments provide a way of determining the
value of new and existing therapies, beyond their ability to relieve
depressive symptoms” (Barge-Schaapveld et al., 1999, p174).
5.6
Summary
Depression and dysthymia, both clinical disorders within the
category of mood disorder in the Diagnostic and Statistical Manual of
Mental Disorders (DSM-IV, 1994), significantly impact our society, with up
to 25% of women and 12% of men affected by these conditions.
Self-rated depression inventories such as the Beck Depression
Inventory (Beck et al., 1961) and Hospital Anxiety and Depression Scale
(Zigmond & Snaith, 1983) are commonly used in depression assessment.
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Subjective Wellbeing and Homeostasis
While self-rated inventories are effective indicators of depression, several
criticisms can be made about this method of assessment. The inventories
follow a medical model and are disease-focused, thus they only measure the
presence or absence of depressive symptoms rather than the continuum of
psychological wellbeing. Many of these inventories contain items that
measure physical symptoms of depression, thereby blurring the boundary
between the physical consequence and the psychological experience of
depression. Moreover, depression inventories yield a general score that is
indicative of overall depression severity but does not indicate depression
levels within specific areas of life.
The most common psychological treatment of depression involves
cognitive therapy. This therapy is based on theoretical conceptualisations of
depression including Beck’s Cognitive Theory of Depression (Beck et al.,
1979) and the Learned Helplessness Model of Depression (Alloy &
Abramson, 1999). These perspectives are both essentially diathesis-stress
models, whereby depression results from the interaction between stress,
being negative life events (or the lack of positive life events), and the
diathesis, or cognitive vulnerability to depression.
Research has confirmed the relationship between SWB and
depression, as well as the contribution of the stable factors of affect and
personality, and the transient cognitive factors of optimism, self-esteem and
control, in relation to SWB and depression. The relationship between these
individual difference factors and depression can be depicted through the
model of SWB homeostasis. The level at which SWB is normally
maintained, one’s set-point range, is determined by personality and affective
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Subjective Wellbeing and Homeostasis
factors. People with high neuroticism and negative affectivity may have a
depressogenic attributional style, making them prone to homeostatic
breakdown resulting in depression. While SWB is normally regulated to a
set-point-range around 75%SM, chronic or severe negative extrinsic events
may cause a breakdown of the homeostatic system, thereby leaving an
individual vulnerable to depression. As this is the case, extrinsic events
tend to be uncorrelated with high SWB scores, but significantly correlated
with low SWB and depression scores. This indicates that the homeostatic
system that buffers the effect of extrinsic events is not able to do so below a
theoretical threshold point that indicates homeostatic failure.
As depression can be conceptualised within the model of SWB
homeostasis, the efficacy and possible utility of SWB inventories within the
depression field was considered. It is apparent that SWB inventories may
serve a threefold function, in: screening for depression along a continuum of
psychological wellbeing within specific domains of life; evaluating
depression treatments and indicating the alleviation of depression
symptomatology, and potentially guiding treatment for depression within
specific life domains.
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Subjective Wellbeing and Homeostasis
Chapter 6
Study 1 Methodology
6.1
Aim
Many studies within the subjective wellbeing and quality of life
literature have demonstrated a relationship between SWB and psychological
variables such as personality and depression, as was reviewed in Chapter 3.
Therefore, this first study was designed to test the relationship between
subjective wellbeing and the psychological variables of depression, anxiety
and personality. Based on the research reviewed previously, it is expected
that subjective wellbeing will correlate positively with extraversion, and
negatively with neuroticism, depression and anxiety.
Additionally, the study aimed to discover which of the psychological
variables is the strongest predictor of SWB. Previous research has found
high negative correlations between depression and SWB, and neuroticism
tends to have a stronger correlation with SWB than extraversion.
Furthermore, anxiety correlates highly with depression, but is not expected
to correlate highly with SWB, as it is possible to be satisfied as well as
anxious (Headey, Kelley & Wearing, 1993). Therefore, it is expected that
depression will be the strongest predictor of SWB, followed by neuroticism.
The study also aims to map the interaction between SWB and
depression in order to generate hypotheses about the ability of SWB
inventories to be used as indicators of depression. The scales used in this
first study include the Personal Wellbeing Index (Cummins, Eckersley,
Pallant, Van Vugt & Misagon, 2003), the Hospital Anxiety and Depression
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Subjective Wellbeing and Homeostasis
Scale (Zigmond & Snaith, 1983), and the neuroticism and extraversion
subscales of the NEO Five Factor Inventory (Costa & McCrae, 1992).
6.2
Research Hypotheses
The hypotheses for study 1 were:
1. That depression, neuroticism and anxiety would negatively
correlate with SWB, whereas extraversion would positively
correlate with SWB.
2. That depression and neuroticism will be the strongest
predictors of SWB.
3. That a curvilinear relationship, such as displayed in Figure 9,
exists between SWB and depression, in that SWB levels
remain relatively stable with increasing depression, until
SWB levels drop sharply.
Figure 9 - Curvilinear Relationship Between SWB and Depression
Threshold
Subjective
Wellbeing
Depression
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Subjective Wellbeing and Homeostasis
6.3
6.3.1
Method
Materials
Approval to conduct this research was sought and approved by the
Deakin University Ethics Committee. The letter of ethics approval is
attached in Appendix A. Participants completed a questionnaire package
(see Appendix B) that was mailed to their residential address. This package
included a plain language statement, see Appendix B, and consent was
assumed on return of the questionnaire. Detailed descriptions of the
instruments, reliability statistics and rationale for the use of each instrument
are reported below.
6.4.2
Demographic Data
The participants were asked to tick one of the five boxes of the
income bracket in which their current household income falls. The income
brackets included <15,000; 16,000-30,000; 31,000-60,000, 61,000-90,000
and >91,000. The demographic data for gender and age were collected by
matching the identification code with the information on the Australian
Unity database that had been collected on the original telephone interview.
Specific details relating to the demographic data are reported in the
following data analysis chapter.
6.3.3
Australian Unity Wellbeing Index
The Australian Unity Wellbeing Index (Cummins et al., 2003) is a
multilevel measure of wellbeing in Australia, and comprises the Personal
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Subjective Wellbeing and Homeostasis
Wellbeing Index and the National Wellbeing Index. The Personal
Wellbeing Index (PWI) is based on the Comprehensive Quality of Life
Scale (Cummins, 1997), and comprises seven domains of personal
wellbeing. Satisfaction levels in each of the seven life domains are
measured individually, and then averaged to give the Personal Wellbeing
Index score. The seven domain of life measured within the PWI include
Standard of Living, Health, Achievements in Life, Personal Relationships,
How Safe You Feel, Community Connectedness, and Future Security. The
respondents rate their satisfaction on an 11-point Likert scale, ranging from
0 (extremely dissatisfied) to 10 (extremely satisfied). The National
Wellbeing Index, which comprises six individual measures of national
wellbeing, was not analysed within the current research.
A factor analysis conducted by Cummins and colleagues (2003)
indicated that the Australian Unity Wellbeing Index yielded two clear
factors consistent with the personal and national sub-scales. The seven
items of the PWI loaded .51 to .72 on the PWI factor and explained 38.3
percent of the variance. In order to validate the domains of the PWI, the
seven domains were regressed against the variable ‘life as a whole’. The
Standard of Living domain made the largest unique contribution to the
prediction of life as a whole, followed by Achievements in Life and
Relationships. All other domains made a significant contribution of unique
variance with the exception of Safety (Cummins et al., 2003).
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Subjective Wellbeing and Homeostasis
6.3.4
Hospital Anxiety and Depression Scale
This scale is designed to detect anxiety and depression in general
medical outpatient populations. Despite the use of the word ‘hospital’, the
scale is valid for use with community samples. The Hospital Anxiety and
Depression (HAD, Zigmond & Snaith, 1983) scale consists of seven
depression items and seven anxiety items. These items measure
psychological symptoms of anxiety and depression, and do not measure
physical symptoms of these disorders. These items were selected to
distinguish the effects of physical illness from mood disorders, and so
symptoms likely to be present in both (e.g. headaches) were not included.
Only psychological symptoms of depression and anxiety were included in
the scale, so as to separate the detection of mood disorder from physical
health problems. This is important in a blind survey such as this, as the
health status of the participants is not known to the researcher and could
otherwise confound results.
The 14 items in the HAD scale are usually rated on a four-point
Likert scale, ranging from the absence of a symptom (‘not at all’; scoring 0)
to maximal symptomatology or the absence of positive features (‘most of
the time’; scoring 3). Permission from the publishers, The NFER-NELSON
Publishing Company Ltd, was sought and granted for the use of the scale,
and to alter the rating format to an 11-point Likert scale. This alteration
broadens the response possibilities and thus allows for a more sensitive
measurement of symptom severity. Due to clerical oversight, the response
format was also altered from a frequency rating (‘not at all’ to ‘most of the
time’) to the more standard ‘Strongly Agree’ to ‘Strongly Disagree’.
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Subjective Wellbeing and Homeostasis
Therefore, the data collected reports only the degree to which a participant
agrees that depression and anxiety symptomatology applies to them, and
cannot be used to detect clinical anxiety or depression levels.
The internal consistency of the HAD scale is good, ranging between
0.76 and 0.41 for the anxiety items, and between 0.60 and 0.30 for the
depression items. Zigmond and Snaith (1983) reported good results for
initial tests of reliability and validity, and scale scores were not affected by
the presence of physical illness. There was evidence to suggest that the
anxiety and depression items were tapping different dimensions. The
correlations between the HAD scale and other well-known depression and
anxiety scales range from 0.67 to 0.77. The HAD scale was chosen for this
study because it measures depression as well as anxiety, both of which can
be expected to influence SWB levels, and because this scale separates out
the physical symptoms of these conditions and focuses on the psychological
symptomatology.
6.3.5
NEO Five-Factor Inventory
This scale is based upon the five-factor model of personality
discussed in Chapter 3, which has been a consistently popular taxonomy of
personality. The NEO Five-Factor Inventory (Costa & McCrae, 1992) is an
abridged version of the longer NEO Personality Inventory- Revised (NEOPI-R), and consists of 12 items in each of the five personality domains,
Neuroticism, Extraversion, Openness to experience, Agreeableness and
Conscientiousness.
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Subjective Wellbeing and Homeostasis
The original item format of the NEO-FFI consists of a five-point
Likert scale that ranges from Strongly Disagree to Strongly Agree.
Permission was sought and granted from the publisher, Psychological
Assessment Resources Inc, to use the scale for research purposes, and to
alter the rating format to an 11-point Likert scale (ranging from 0 to 10,
Strongly Disagree to Strongly Agree).
The NEO-FFI was developed as a short form of the NEOPersonality Inventory-Revised. The original sample of 983 men and women
from the 1986 administration of the NEO-FFI provided data for item
selection. For each domain, the 12 items with the highest positive or
negative loadings were selected, and some substitutions were made to
diversify items content. The NEO-FFI correlated with the NEO-PI-R
between .75 and .89 across the five subscales. Internal consistency for the
NEO-FFI Neuroticism and Extraversion subscales was .92 and .90
respectively. As is true in all cases where abbreviated scales are formed,
some precision is traded for speed and convenience, however, the NEO-FFI
is psychometrically sound, widely used and favourably received by
participants. The Neuroticism and Extraversion subscales of this scale were
used in the present study due to its brevity, and popularity of its theory and
use in the field of subjective wellbeing.
6.4
Procedure
Two samples of participants agreed to participate in this study by
completing self-administered questionnaires sent to their residential
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Subjective Wellbeing and Homeostasis
addresses. Reply-paid envelopes were included in the questionnaire
package for the participant’s convenience.
6.4.1
Recruitment of Participants
The study was integrated into the follow-up phase of a collaborative
study between Australian Unity and Deakin University that has led to the
development of the Australian Unity Wellbeing Index. In this original
study, data collection occurred over a period of three weeks during April
and May of 2001. A firm was contracted to provide 15,000 names and
telephone numbers that collectively represented the national population on a
geographically proportional basis. A call centre at Australian Unity was
then used to ring people drawn randomly from the supplied list until the
target of 2,000 respondents was reached. These participants were asked if
they would like to participate in further studies, and if they answered in the
affirmative, they were mailed the survey for the current study in late 2001.
The data for the current study originated from two separate
questionnaire mailouts. The first mailout for study 1 took place in
November 2001, and was sent to 1,000 participants with 247 returned (25%
return rate). Due to an error, the identification codes were not transferred to
the questionnaires, so the returned questionnaires could not be linked to the
demographic information of participants in the Australian Unity database.
Therefore, a second mailout occurred and the questionnaire (with
identification code) was sent to 800 participants from the database with 253
returned (32% return rate). Thus, the results refer to both Data Set A and
Data Set B, but demographic information is available only for Data Set B.
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Subjective Wellbeing and Homeostasis
Chapter 7
Study 1 - Data Analysis
The two data sets were screened using the FREQUENCIES option in
SPSS before commencing analysis. Three clerical errors in Set B were
located and corrected. Three cases were deleted due to the dichotomous
nature of the data. These cases had scores of perfect 10 for all of the
satisfaction domains, and had scores of either 0 or 10 for most of the
remaining items of the questionnaire. As the sample sizes are fairly large, a
further eight variables from Set A and three variables from Set B were
deleted rather than substituted, due to incomplete Personal Wellbeing Index
data. Overall, eleven cases were deleted from Set A (leaving N=236) and
seven cases were deleted from Set B (leaving N=246).
Each of the seven personal domains, the six societal domains, and
the composite PWI scores in both data sets were converted to percent scale
maximum (%SM) scores by creating syntax to compute the score of each
variable multiplied by 10. For example, the syntax read: COMPUTE
sma1eco = a1eco * 10. This procedure re-coded the data from 0 to 100.
Several NEO Five-Factor Inventory items were recoded as they are
reverse-scored (items 1,7,13 & 19 of the Neuroticism and items 6, 12, 18 &
24 of the Extraversion scales), and then ‘total neuroticism’ and ‘total
extraversion’ variables were created by computing the addition of the
neuroticism and extraversion scores. These total scores then have a possible
range of 0-120. Similarly, the reverse-scored items of the HAD scale were
recoded (item 7 of anxiety and items 2, 4, 6, 12 and 14 of depression), and
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Subjective Wellbeing and Homeostasis
‘total anxiety’ and ‘total depression’ variables were created by computing
the addition of the anxiety and depression scores. These total scores have a
possible range of 0-70.
It was expected that many of the assumptions of normality would not
be met for several reasons. The PWI scores are predicted to be negatively
skewed, as multiple studies have found that PWI scores are most common
between 70% and 100%. Also, the HAD scale scores are expected to be
positively skewed as the majority of the population do not suffer from
anxiety or depression symptoms.
7.1
7.1.1
Assumptions of Normality Testing
Demographic Data
The only demographic information relating to Data Set A is income.
The annual household income of the 232 participants from Data Set A was
categorised into five groups as previously described. The distribution of
income was normal on inspection of the histogram and normal and
detrended plots. The significance of skew and kurtosis statistics were
within acceptable ranges, suggesting the income variable meets the
assumption of normality.
The proportion of males to females in Data Set B (N=235) was
somewhat uneven, with 84 males (36%) and 151 females (64%). Age
ranged from 18 to 82 years (M=50, SD=15). The skewness statistics for the
gender and age variables were within the acceptable range of –4 to +4, and
the histograms show a roughly normal distribution pattern, indicating that
these variables meet the assumption of normality.
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Subjective Wellbeing and Homeostasis
The majority of the 246 participants within Data Set B had a
household income between $15,000 and $60,000. The distribution of
income across these groups was normal, with no skew or kurtosis.
7.1.2
Personal Wellbeing Index
Within the seven PWI domains of both sets, ‘Satisfaction with
Personal Relationships’ had the most non-normal curve, and other domains
had either normal or only slightly non-normal distributions. The
‘Satisfaction with Personal Relationships’ variable was found to have
negative skew and positive kurtosis (S=-8.46, K=5.4), suggesting that most
people are very satisfied with the quality of their personal relationships (M =
75.0 %SM).
Five of the seven PWI domains within Set A displayed significant
negative skew. These were ‘standard of living’, ‘health’, ‘achievements in
life’ ‘personal relationships’ and ‘how safe you feel’. The Personal
Wellbeing Index (%SM) score was also negatively skewed. Only the
‘achievements in life’ and ‘personal relationships’ domains were
significantly leptokurtic. The skewness of these variables was within the
acceptable range, and they were not significantly influenced by outliers. In
Data Set B, all seven of the PWI domains were negatively skewed.
On inspection of the boxplots, the variables ‘Standard of Living’ and
‘Achievements is Life’ in Data Set B both contained one extreme outlier.
On inspection of the z-scores, all values were less than 3.29, and thus not
significantly influenced by outliers. Therefore, no cases were deleted.
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Subjective Wellbeing and Homeostasis
The Personal Wellbeing Index (PWI) score is the average of the
seven PWI domain scores. The overall PWI scores were found to have a
significantly negatively skewed distribution (S=-4.17) with a mean score of
71.11 %SM in Data Set A, while Data Set B had a negative skewed
distribution (S=-3.39) with a mean score of 69.31 %SM. Table 1 contains
the mean and standard deviation statistics for both data sets.
Table 1 - Mean and Standard Deviation of PWI Variables in Data Sets
A and B
M
Data Set A
N=236
SD
Data Set B
n=246
M
SD
St. of Living
73.77
18.56
71.71
20.09
Health
68.60
20.36
65.69
22.68
Achievements
71.31
17.49
70.12
19.64
Personal R’ships
75.59
21.58
74.43
22.19
Safety
75.25
17.85
74.84
18.13
Community
67.71
19.13
64.59
21.51
Future Security
65.51
21.20
63.78
23.40
PWI
71.11
13.60
69.31
14.58
All statistics calculated from the Scale Maximum PWI scores
7.1.3
NEO Five-Factor Inventory
The majority of the NEO-FFI variables in Data Set A produced
normally distributed results. Only three of the 24 variables displayed a
significant level of skewness; items 4, 21 and 23; “I laugh easily” (S=-6.1),
“I often feel helpless and want someone else to solve my problems” (S=6.9)
and “At times I have been so ashamed I just want to hide” (S=12.65,
K=11.56) respectively.
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Item 23 had the most significant skew of the NEO-FFI variables in
both sets. Observation of the boxplot for this item in Data Set A revealed an
extreme outlier, and contained a case with a z-score of 4.04, which is in
excess of the acceptable limit of 3.29. This case was deleted from the data
set. In Data Set B, this variable had no extreme outliers, and the z-scores
were acceptable. Overall, the NEO-FFI variables show fairly normal
distribution within both data sets.
The 12 Neuroticism items and 12 Extraversion items were each
added to form ‘Total Neuroticism’ and ‘Total Extraversion’ scores
respectively. The significance of skew and kurtosis statistics were
acceptable for both sets, the z-scores were acceptable, the boxplots revealed
no extreme outliers, and the normal and detrended pots were normal. In
addition, the Kolmogorov-Smirnov statistics were non-significant for these
variables. Therefore, the total neuroticism and total extraversion variables
meet the assumptions of normality.
7.1.4
HAD Scale
Observations of HAD scale histograms from both sets revealed
varying levels of skew and non-normality of normal and detrended plots in
some variables. Twelve of the 14 HAD scale variables in Data Set A had
significant skew, and ten of the 14 variables were skewed in Data Set B.
However most had a fairly low level of skew, with significance statistics
from 4.6 to 9.6. In both sets, item 14 (‘I can enjoy a good book or radio or
TV programme’), was extremely negatively skewed (S= -12.7, K= 16.3).
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Subjective Wellbeing and Homeostasis
On examination of the HAD scale boxplots in Data Set A, one
extreme outlier was observed within HAD item 4, and three extreme outliers
were observed within item 14 (‘I can laugh and see the funny side of things’
and ‘I can enjoy a good book or radio or TV programme’). Only item 14
contained extreme outliers in Data Set B.
Four of the 14 HAD variables in Data Set A produced z-scores in
excess of 3.29 (items 2, 6, 13 & 14), three of these being depression items,
and one an anxiety item. In Data Set B three depression variables (items 4,
12 & 14) produced z-scores in excess of 3.29. As expected, the only item
with a significantly deviant z-score was item 14 (z=4.6). As this scale is
used to indicate depression and anxiety, it was expected that these variables
would appear skewed with some outliers. Removal or transformation would
make the important outlying cases less detectable and interpretation less
sensitive, thus none was carried out. Both the Total Anxiety and Total
Depression scores for both sets met the assumptions of normality, with a
bell-shaped distribution and no skew or kurtosis.
7.2
General Data Set Comparison
The two data sets were compared so that a prediction could be made
as to whether the variables should behave the same in the analyses to
follow.
The number of participants in both data sets is similar (236 in A, 246
in B). Differences between the mean scores for each of the five
psychological variables (subjective wellbeing, neuroticism, extraversion,
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Subjective Wellbeing and Homeostasis
anxiety and depression) and the demographic variable income between the
data sets were tested using independent samples t tests.
The mean Personal Wellbeing Index score in set A was 71.1, and
69.3 in set B. The homogeneity of variance assumption was met (Levene’s
statistic p<.05), and the independent samples t test found that this difference
was not statistically significant, p (480) = .162. However, it is important to
note that the Set B PWI mean falls under the theoretically significant
threshold of 70%SM, whereas the PWI mean for Set A is over this
threshold. The normative SWB range for Western populations is 7080%SM, and the 70%SM point represents a lower threshold, beyond which
point it is hypothesised that homeostasis failure occurs. Thus, while the
mean SWB scores of the data sets in this study were not significantly
different statistically, it is possible that statistical analyses of difference
underestimate the implications of this difference as the Set B SWB mean
falls under the 70%SM threshold.
Extraversion means did not differ, 68.7 and 68.1. Likewise, income
was not found to be significantly different, p (474) = .546. The mean
depression scores between the data sets differed (17.9 and 19.8) and was
significant, p (466) = .038. Although the same test found the variable
neuroticism to be significantly higher in Set B, p (464) = .027, the
homogeneity of variance assumption was violated. Therefore, the nonparametric equivalent of the independent samples test was conducted. The
Mann-Whitney U test revealed that the mean neuroticism scores are not
significantly different, z=-1.85, p=.065. Once again the assumption of equal
variance was violated when the independent samples test was applied to the
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anxiety variable. The Mann-Whitney U test found a significant difference
between mean anxiety scores, z=-2.50, p=.012, with the higher value again
being found in Set B.
The significantly higher mean scores for anxiety and depression in
Data Set B are consistent with the lower, albeit non-significant, mean PWI
score in this set. Therefore, these samples appear to be different, as Set B
seems to be a more depressed and anxious, and slightly less satisfied
sample. Thus, these samples may behave differently and lead to different
results when analysed.
7.3
Exploring Relationships
The effects of the demographic variables gender, age and income,
will be presented for Data Set B. The data from Set A will be used to
validate the findings where this is possible.
7.3.1
Pearson Product-Moment Correlation – Data Set B
One of the aims of the study was to investigate the relationship
between subjective wellbeing, personality, depression and anxiety.
Specifically, it was hypothesised that scores on the Personal Wellbeing
Index would correlate positively with extraversion, and negatively with
neuroticism, depression and anxiety. It was further hypothesised that the
observed relationships between the variables would remain significant after
accounting for the influence of gender.
These relationships were investigated using Pearson productmoment correlation coefficient. Preliminary analyses of scatter plots were
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Subjective Wellbeing and Homeostasis
performed to ensure no violation of the assumptions of normality, linearity
and homoscedasticity.
Table 2 - Pearson Product-Moment Correlations Between Measures of
PWI and Neuroticism, Extraversion, Anxiety and Depression (Set A
N=224; Set B N=240)
PWI
Neuroticism Extraversion Anxiety Depression
PWI
Set A r
Set B r
1
1
-.553***
-.512***
.346***
.439***
-.498***
-.472***
-.575***
-.533***
Neuroticism
Set A r
Set B r
-
1
1
-.309***
-.239***
.672***
.778***
.558***
.560***
Extraversion
Set A r
Set B r
-
-
1
1
-.271***
-153*
-.552***
-.519***
Anxiety
Set A r
Set B r
-
-
-
1
1
.611***
.536***
71.11
69.31
38.96
43.03
68.67
68.07
20.46
24.12
17.86
19.79
13.60
14.58
18.13
21.31
15.63
15.95
12.02
14.34
9.64
9.67
Mean
Set A
Set B
Standard
Deviation
Set A
Set B
*
***
Correlation is significant at the .05 level
Correlation is significant at the .001 level
There were strong negative correlations between PWI scores and
both depression and neuroticism, with high PWI scores associated with low
levels of depression and neuroticism. The correlation matrix also revealed
that neuroticism and anxiety are highly positively correlated with
depression, and extraversion is highly negatively correlated with depression,
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with these variables contributing between 27% and 31% of shared variance.
The highest correlation between the five entered variables occurred between
neuroticism and anxiety (r=.78, n=236 p=<.001), with 60.5% of shared
variance.
A comparison of the same correlation coefficients by gender was
performed in order to investigate the possibility of gender differences. The
significance of the difference between the correlation coefficients with and
without gender was calculated. This was done using a process whereby the
r scores are converted to z scores using the observed value of z (zobs value),
wherein scores falling between –1.96 and +1.96 are not statistically
different (Pallant, 2001). None of the correlation coefficients were found to
be statistically different, indicating there were no gender differences.
7.3.2
Standard Multiple Regression – Data Sets A & B
A second aim of the study was to determine which of the variables is
most predictive of PWI scores. It was hypothesised that depression scores
would be the strongest predictors of PWI scores, followed by neuroticism
scores as the second most predictive variable.
A standard multiple regression was performed between Personal
Wellbeing Index scores as the dependent variable, and Neuroticism,
Extraversion, Depression and Anxiety as independent variables. Analysis
was performed using SPSS REGRESSION and SPSS FREQUENCIES for
evaluation of assumptions.
With at least 232 respondents in each of the two data sets and four
IVs, the number of cases is well above the minimum requirement of 108
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Subjective Wellbeing and Homeostasis
(104 + 4, Tabachnick & Fidell, 2001:117) for testing individual predictors in
standard multiple regression. The standardised residual values scatterplots
were examined, and revealed no outliers. The normal regression plots
showed no deviations from linearity, and the residual values on the
scatterplot were distributed in a central and roughly rectangular fashion,
indicating that the assumptions of normality, homoscedasticity and
independence of residuals were met. Multivariate outliers were sought
using the IVs as a part of an SPSS REGRESSION run in which the
Malhalanobis distance of each case to the centroid of all cases is computed.
Malhalanobis distance is distributed as a chi-square (χ2) variable, with
degrees of freedom equal to the number of IVs. To determine which cases
were multivariate outliers, the critical χ2 was calculated at the most stringent
alpha level for four degrees of freedom (χ2 at α= .001 for 4df = 18.467). No
cases were identified as multivariate outliers.
The correlations between the variables in the regression model all
show a substantial relationship (at least .3) with the dependent variable,
Personal Wellbeing Index scores (See Table 3). The collinearity tolerance
diagnostics are quite respectable (ranging from .37 to .71), indicating that
the assumption of multicollinearity has been met.
Table 3 displays the unstandardized regression coefficients (B) and
intercept, the standardized regression coefficients (β) and R2 for both data
sets. The R2 for regression was significantly different from zero, F(4, 218)
= 36.83, p<.0005 for Data Set A, and F(4, 230) = 38.99, p<.0005 for Data
Set B. For the three regression coefficients that differed significantly from
zero, the 95% confidence limits were calculated.
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Table 3 - Standard Multiple Regression of Extraversion, Neuroticism,
Depression and Anxiety on PWI Scores
Variables
Extraversion
Set A
Set B
Neuroticism
Set A
Set B
Depression
Set A
Set B
Anxiety
Set A
Set B
PWI
(DV)
Extrav
Neu
Dep
.37
.44
-.57
-.51
-.31
-.23
-.55
-.53
-.55
-.51
.56
.55
-.47
-.48
-.27
-.15
.67
.78
.61
.53
2
R
Adjusted R2
B
β
.08
.25
.09
.27***
-.28
-.16
-.37***
-.22*
-.38
-.29
-.27***
-.19*
-.03
-.17
-.03
-.16
Set A
.40
.39
Set B
.40
.39
*
Correlation is significant at the .05 level
Correlation is significant at the .01 level
***
Correlation is significant at the .001 level
**
Three of the IVs made significant unique contributions to the
prediction of PWI levels, however only two of these were consistent
predictors in both sets of results, and there were some interesting differences
in the predictive ability of the four independent variables across the data
sets.
In Set A, depression was the most significant predictor of PWI
scores, followed by Neuroticism. Neither Anxiety nor Extraversion were
found to provide unique predictive ability.
In contrast, the results from Set B show Extraversion as the most
significant predictor (at the p<.001 level), followed by Neuroticism (p<.05)
and Depression (p<.05). Although Depression correlated most highly with
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PWI scores, Extraversion and Neuroticism were found to be better
predictors. Therefore it can be assumed that (in this set) Depression does
not make as strong a unique contribution to PWI scores. The influence of
depression on the PWI may be mediated by the relationship between
extraversion and neuroticism.
In conclusion, anxiety was not found to be independently predictive
of PWI scores in either data set. Depression and neuroticism were
significant predictors in both data sets, and fairly close in standardized beta
weights and significance levels. The most confounding finding was that
extraversion, whilst not providing any unique contribution in Data Set A,
was the most significant predictor in Set B. The possible reason for this
discrepancy will be analysed within Chapter 8, the discussion of Study 1.
7.3.3
Hierarchical Multiple Regression – Data Sets A & B
Hierarchical regression was employed to determine whether the
addition of demographic details (age, gender and income) improved
prediction of PWI beyond the levels shown in 7.3.2. Income is the only
demographic variable in Data Set A. After step 1, income was found to be a
non-significant predictor, explaining only 1.6% of PWI scores, F(1,
221)=3.52, p=.062.
A hierarchical regression was also performed with Data Set B,
controlling for the variables age, gender and income in the first step, see
Table 4.
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Table 4 - Hierarchical Regression Equations Predicting PWI Scores
Controlling for Demographic Variables
Predictors
Step 1
Income
Set A
Set B
Gender (Set B)
Age
(Set B)
Step 2
Income
Set A
Set B
Gender (Set B)
Age
(Set B)
Depression
Set A
Set B
Extraversion
Set A
Set B
Neuroticism
Set A
Set B
β
Beta
1.51
1.72
2.56
.12
1.25
.15*
.08
.12
.74
1.3
2.0
.02
.06
.12*
.07
.02
-.54
-.29
-.37***
-.19**
.03
.22
.03
.24***
-.26
-.24
-.33***
-.35***
Step 1
R2
Adjusted R2
Step 2
R2
Adjusted R2
Set A
Set B
.02
.01
.04
.03
.41
.40
.41
.39
*
Correlation is significant at the .05 level
Correlation is significant at the .01 level
***
Correlation is significant at the .001 level
**
As with the previous regression, there were some inconsistencies
between the results of Set A and Set B. In Data Set B, model 1 was found
to be significant, R2 =.05, F(3, 211) = 3.02, p=.031. Age and gender were
not found to influence PWI scores, however income was a significant
predictor, β=.15, p<.05. After step 2, with the three psychological variables
entered, the model explains 41% of variance, R2 =.41, F(6, 208) = 24.06,
p=<.0005. Four variables entered in step 2 made a significant unique
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Subjective Wellbeing and Homeostasis
contribution, including Neuroticism (β=-.35, p<.0005), Extraversion (β=.24,
p<.0005), Depression (β=-.19, p<.009), and finally income (β=.11, p<.039)
respectively. Interestingly, extraversion was the most predictive variable in
the initial standard multiple regression, but it becomes the second most
predictive variable, after neuroticism, when the influence of income is
controlled for.
Interesting differences between Data Sets A and B have been
identified. Not only is extraversion a unique predictor of PWI score in Set
B and not in Set A, but income significantly explains variance in PWI
scores in Set B, but not in Set A. Furthermore, when income is statistically
controlled for, extraversion becomes less predictive of PWI scores than
neuroticism. These findings will be discussed in the following chapter.
7.4
Testing the Homeostatic Regulation of SWB – The
Relationship Between SWB and Depression
Finally, the current study aimed to test the hypothesis of SWB
homeostasis. This hypothesis states that SWB is maintained within an
idiosyncratic range controlled by homeostasis, until the degree of challenge
or negative load placed upon the system exceeds an individual’s threshold
for effective management. Therefore, depression ratings on the HAD scale
may not significantly decrease SWB unless the depression is strong enough
to overwhelm the capacity of the homeostatic system. If this hypothesis
were to be supported, a curvilinear relationship between SWB and
depression would be found, wherein SWB levels remain stable with
increasing depression scores, until a certain level of depression is reached
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Subjective Wellbeing and Homeostasis
(signifying homeostatic breakdown), whereupon SWB levels would sharply
drop.
This hypothesis was tested by splitting the participant’s depression
scores into groups, then calculating the mean PWI scores for each
depression group, and graphing these PWI scores. The depression scores in
both sets ranged from 0-47. The two data sets were pooled, and depression
groups were formed on the basis of incrementing scores of 5 (see Table 5).
The two highest depression groups (36-40 and 41-44) were combined to
increase reliability due to the low numbers in these groups. The PWI means
for each depression group were then calculated using SPSS COMPARE
MEANS command, and these values were plotted graphically, as seen in
Figure 10.
Table 5 - Means and Standard Deviations of PWI Scores of Nine
Groups of Increasing Depression Scores
Depression
Group
0-5
6-10
11-15
16-20
21-25
26-30
31-35
36-44
Mean
N
81.05
79.11
74.30
70.78
70.11
64.29
55.71
50.18
Total N
42
48
90
97
80
44
40
23
Std.
Deviation
10.63
10.05
12.25
12.01
11.90
11.28
13.53
13.12
485
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Subjective Wellbeing and Homeostasis
Subjective Wellbeing Homeostasis
Data Sets A & B Combined
85
10.63
80
75
10.05
12.25
12.01
11.90
PWI
70
11.28
65
60
55
50
45
40
* SD value above data points
13.53
13.12
2
R = 0.9844
1
2
3
4
5
6
7
8
Depression
Figure 10 Mean PWI Scores for Depression in Incrementing Groups of
Ten
In Figure 10, there is a fairly linear relationship between SWB and
depression as PWI scores range from around 81% to 70%SM. A plateau
occurs at the 70%SM level, where an increase in depression produces no
drop in PWI score. Finally, further increases in depression precipitate a
steeper drop in PWI scores. The mean PWI scores for the first five
depression groups (including scores from 0-25, representing no depression
to moderate depressive symptomatology) varies only by 10.95%SM. In
contrast, the mean PWI scores for the last four depression groups (indicating
scores of 26-44 and severe depressive symptomatology) varies by
19.93%SM.
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Subjective Wellbeing and Homeostasis
A third order polynomial curvilinear trendline was added to the
graph (in red). This curvilinear model (r2=.98) fit the data slightly better
than a linear model trendline (not pictured, r2=.95).
These results suggest two implications; firstly that scores below the
PWI 70%SM threshold point may indeed be indicative of depression, and
secondly, PWI scores are more sensitive to depression than the depression
inventory itself. Depression scores that ranged from an absence of
depression symptoms to moderate depression scores only affected SWB to a
small degree, however at the 70%SM threshold point, the more severe
depression scores overwhelms the SWB homeostatic mechanism, and a
rapid decline in PWI scores is seen. While these figures present only
tentative evidence of a curvilinear relationship between SWB and
depression, there is enough evidence within these findings to argue that the
lower 70%SM point of the normative SWB range is the crucial point of
homeostatic failure. The unexpected finding that the relationship between
PWI scores and depression is not well explained by a curvilinear model will
be discussed in the following chapter.
7.5
Summary
To briefly summarize this chapter:

The data sets A and B differed on several variable means. Set A was
higher in PWI scores, whereas set B was higher in neuroticism,
anxiety and depression scores, with the latter two variables differing
significantly from those in set A. These differences were not
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Subjective Wellbeing and Homeostasis
explained by differences in income or the number of participants in
each set.

Both data sets displayed high correlations between anxiety and
neuroticism, anxiety and depression, and PWI and depression scores
respectively

A standard multiple regression indicated that depression and
neuroticism were the strongest predictors of PWI scores in Set A,
whereas extraversion, neuroticism and depression were the strongest
predictors in Set B.

A hierarchical regression indicated that depression and neuroticism
remained the strongest predictors of PWI in Set A after the addition
of demographic details. In comparison, neuroticism, extraversion,
depression and income were the strongest predictors of PWI scores
in Set B respectively.

The relationship between depression and PWI scores was plotted.
This graph indicated a slightly linear relationship between the
factors, with a shallow slope from 80-70%SM, a plateau at 70%SM,
and a steeper slope between 70-50%SM.
These results will be discussed further in the following chapter.
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Chapter 8
Discussion
8.1
Data Set Characteristics
Arguably, one of the most interesting findings within the data
analysis of Study 1 was that the data sets differed significantly on mean
PWI and psychological variable scores (Neuroticism, Anxiety and
Depression). Although the data sets contained a similar number of
participants and were collected using the same methodology, the
participants in Data Set B can generally be describes as less satisfied, more
depressed, more anxious and higher in neuroticism.
The analyses established that only depression and anxiety differed
significantly between the data sets. The PWI mean score was lower in Data
Set B, but this difference did not reach statistical significance. It is possible
to argue, however, that the slight deviation observed between these scores
does in fact reach clinical significance. While the PWI scores only differed
by 1.7% (Data Set A = 71.11, Data Set B = 69.31), the lower score falls
under the theoretical threshold point of 70%SM (Cummins et al., 2002).
While we do not as yet know what a 1.7% mean score difference means in
relation to the psychological functioning or wellbeing of a population, the
significant discrepancies in variable scores of the two data sets does lead to
the conclusion that slight deviations in PWI mean scores may hold clinical
importance.
From the many subjective wellbeing research studies that have been
conducted in Western populations, a consistent finding is that the level of
satisfaction of these populations falls within the range of 70-80% of the
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Subjective Wellbeing and Homeostasis
satisfaction Scale Maximum (Campbell, Converse & Rodgers, 1976;
Cummins, 1995). This range is based on a calculation of two standard
deviations on either side of the mean, 75 ± 2.5%SM (Cummins, 1995).
Therefore, it appears that floor and ceiling effects operate to regulate the
experience of population-wide satisfaction levels within 70 and 80% of
Scale Maximum. The mean PWI score for Set A fell within the 70-80%SM
normative range, whereas the mean score for Set B fell just under the
70%SM threshold point, as depicted in Figure 11.
Mean PWI
Data Set B
Mean PWI
Data Set A
Lower and
upper
thresholds
for general
population
samples
F
r
e
q
u
e
n
c
y
0
10
20
30
40
50
60
70
80
90
100
Population Mean
Percentage of Scale Maximum (%SM)
Figure 11 The normal distribution of subjective quality of life with
reference to mean satisfaction scores for Data Sets A and B
The theory of SWB homeostasis holds that SWB levels below the
normal range indicate an increased probability of homeostatic failure
(Cummins et al., 2002). The finding that participants in Set B have a mean
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Subjective Wellbeing and Homeostasis
satisfaction score only slightly below the 70% threshold and significantly
higher scores on depression and anxiety is evidence that SWB homeostasis
appears to be very sensitive, with slight deviations below the norm resulting
in exponential reductions in psychological wellbeing. Moreover, given
these critical sample differences, the variables may behave differently in
further statistical analysis.
8.2
Relationship Between Variables
The Pearsons product moment correlation coefficients revealed that,
as predicted, neuroticism, anxiety and depression were negatively correlated
with PWI scores (-.55, -.50, and -.58 respectively for Set A; -.51, -.47 and .53 respectively for Set B), and extraversion was positively correlated with
PWI scores (.35 in Set A, .44 in Set B). The correlation coefficients for
both data sets were very close, with both data sets ordering the variables
depression, neuroticism, anxiety and extraversion according to correlation
strength.
The largest discrepancy between the data sets occurred between
extraversion and PWI correlation scores, (.44 for Data Set B as opposed to
.35 for Data Set A). While the higher correlation between extraversion and
PWI scores in Data Set B seems fairly insignificant at this point as the
discrepancy is not very large, the importance of the correlation discrepancy
becomes more evident with the interpretation of further results.
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Subjective Wellbeing and Homeostasis
8.3
Prediction of SWB
Multiple regression was used to test the ability of the four
psychological variables to predict PWI scores. This analysis revealed some
initially confounding discrepancies between Set A and Set B results.
Neuroticism and depression were found to be the respective
strongest predictors of PWI scores within Set A, with no unique variance
contributed by extraversion or anxiety (meeting the initial hypothesis).
These findings support previous research that indicates that neuroticism
correlates with SWB more strongly than extraversion (Costa & McCrae,
1980; DeNeve & Cooper, 1995; Headey & Wearing, 1989; Heaven, 1989;
Schmutte & Ryff, 1997), and that depression is inversely related to SWB
(Barge-Schaapveld et al., 1999; de Leval, 1999; Hansson, 2002; Holloway
& Carson, 1999; Kammann & Flett, 1983; Koivumaa-Honkanen et al.,
2001; Pyne et al., 1997).
However, extraversion was found to be the strongest predictor for
Set B, followed by neuroticism and depression. Therefore, despite the fact
that extraversion was the least correlated with PWI scores for both sets, it
was the best predictor of satisfaction for participants in Set B. Extraversion
was more highly correlated with PWI in Set B rather than Set A, however
extraversion still had the lowest correlation coefficient of the four variables
in Set B.
In order to interpret these unexpected results, it is necessary to relate
back to the fundamental differences between the two data sets. Firstly, the
mean extraversion scores for the two data sets were near identical (68.6 in
Set A, 68.1 in Set B). Secondly, extraversion had a higher correlation with
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Subjective Wellbeing and Homeostasis
PWI scores in set B than set A. Finally, extraversion was the least
correlated with PWI scores of the four psychological variables in both sets.
Therefore it is assumed that extraversion is not the strongest predictor of
PWI scores per se, but rather it seems that extraversion may be an important
determinant of satisfaction in Set B because this is a more depressed,
anxious and neurotic group than the other.
It may be that when measures of psychological ill-health (such as
depression and anxiety) are higher or unstable, positive psychological
measures such as extraversion perform a stronger predictive role. For
instance, within a group of normally satisfied people, measures of positive
psychological indicators may not provide much discriminative ability,
whereas measures of psychological ill-health, due to their infrequency,
provides a more sensitive measure of satisfaction. Alternatively, within a
less satisfied population group with higher depression etc, measures of
psychological ill-health could be expected to be less sensitive and reliable in
satisfaction prediction, whereas positive psychological measures could be
more sensitive and discriminative.
While this is a hypothetical explanation, it seems to fit with the
finding that extraversion moves from being nearly the least predictive
variable in samples with PWI scores within the normal range, to the most
predictive variable when used with populations with lower than normal
SWB.
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Subjective Wellbeing and Homeostasis
8.3.1
Influence of Demographic Data
When a hierarchical multiple regression was performed to control
for the demographic variables of income, age and gender, it was found that
income was not a significant predictor of PWI scores in Set A, but was in
Set B. This discrepancy can be interpreted with the explanation that income
is not a significant predictor of satisfaction when PWI scores are within the
normative range, but income is predictive of PWI scores at lower levels of
satisfaction. That is, income is a significant factor for people that have a
lower level of satisfaction.
The relationship of income to SWB was explored by Cummins
(2000), who stated that personal income is an important element in the
maintenance of SWB, particularly for people who are experiencing
homeostatic defeat. The conclusion from his research indicated that income
is generally unrelated to the level of SWB provided that people have
sufficient money to purchase the resources required for adaptation to their
life circumstances. However, this relationship becomes more significant
when the financial resources become insufficient to support adaptation
(Cummins, 2000).
These conclusions support the third prediction proposed by
Cummins et al (2002, predictions presented in Chapter 5.5.3) in relation to
the theory of SWB homeostasis. This prediction purports that in conditions
of marked deficit, objective indicators such as chronic poverty will have the
power to control SWB. Moreover, if marked improvements in objective
conditions, such as increases in wealth, occur that exceed the upper
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Subjective Wellbeing and Homeostasis
threshold of SWB, the process of adaptation will diminish the impact of this
experience and return control to homeostasis.
With these theoretical hypotheses in mind, the results from the Study
1 hierarchical regression would have made sense if the mean income scores
of Set A and Set B had been significantly different. This would indicate that
income does not predict SWB within the normal, homeostatically regulated
range, but is predictive of SWB scores below the 70%SM threshold.
However, the mean income scores of Set A and Set B were not significantly
different, so these conclusions cannot be drawn.
As the income level of participants in both sets was comparable (not
significantly different), but the predictive ability of income differed across
data sets, the above discrepancy produces and interesting hypothesis. It was
once believed that wealthier people are happier, and that objective measures
of wellbeing that focus on material wealth were adequate measures of
quality of life. However, these data suggest that wealth does not predict the
subjective wellbeing of people who are satisfied (whose PWI levels are
within a normal range), but wealth can predict the SWB of people who are
less satisfied that normal. This statement does not mean that lower SWB is
caused by income, however, as the mean income level is the same across
both data sets. It may simply be that income is more relevant or influential
to people with lower satisfaction, whereas wealth is not important to
someone who is happier and whose homeostatic mechanisms are regulating
a high subjective wellbeing.
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Subjective Wellbeing and Homeostasis
8.4
The Homeostatic Mechanism
Finally, the theory of subjective wellbeing was tested. The
hypothesis states that SWB is maintained within an idiosyncratic range
controlled by homeostasis, until the degree of challenge or negative load
placed upon the system exceeds an individual’s threshold for effective
management. Thus, homeostasis serves a dual function, in maintaining
SWB levels within a high positive range (which is normally within 7080%SM for Western populations), and serving as a buffer against negative
life conditions.
While SWB is normally regulated within a narrow range, severe or
chronic negative life conditions can cause homeostatic mechanisms to fail.
Therefore, the experience of depression may not significantly decrease
SWB levels unless the depression is strong enough to overwhelm the
functioning of the homeostatic system. Thus, when depression scores were
plotted against PWI scores for participants in both data sets, it was expected
that a negative curvilinear relationship would be found. A linear negative
relationship between depression and SWB would simply indicate that as
depression increases, SWB decreases by a consistent degree. In contrast, a
curvilinear negative relationship would indicate the presence of a threshold.
The graph displaying the plotted PWI and depression scores showed
evidence of a non-linear relationship. PWI scores decreased at a slower rate
with increasing depression at first (between the 81-70%SM range), then the
PWI scores remained stable as depression scores increased (at 71-70%SM),
followed by a more rapid decrease in PWI scores with increasing
depression.
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Subjective Wellbeing and Homeostasis
Homeostasis predicts that SWB levels remain stable with increasing
depression until the threshold (70%SM) is reached, with sharp decreases in
SWB coinciding with higher depression rates, thus forming a curvilinear
relationship. The graph generated from the data did not indicate a stable
level of SWB above the 70%SM threshold point, but instead indicated that
SWB decreases contingent upon increasing depression symptomatology.
This finding does not lend much support to the hypothesis that a curvilinear
relationship (as depicted in Figure 9) exists between SWB and depression.
Still, the occurrence of a plateau at the 70%SM threshold point
followed by a sharp decline in PWI scores suggests that the homeostatic
mechanisms attempt to regulate SWB and buffer against depression, but at a
certain level of stress, homeostasis is defeated. This finding supports the
theory of SWB homeostasis, which predicts that as SWB approaches the
lower threshold (of 70%SM), the homeostatic system resists further change
and works to bring SWB levels back within the normal range.
Furthermore, the graph provides support to the hypothesised lower
threshold of 70%SM, beyond which homeostatic mechanisms cannot
maintain SWB levels. SWB levels decline steeply from this lower threshold
point as depression scores rise, indicating that people with PWI scores
below 70%SM are at risk of homeostatic failure.
There are several possible explanations for the failure to find strong
evidence of a curvilinear relationship between PWI and depression scores.
Firstly, the mean PWI scores, particularly for set B, were under the
normative mean score of 75%SM expected of Western populations. It is not
known whether this difference may impact on the relationship between PWI
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Subjective Wellbeing and Homeostasis
scores and depression. Similarly, the participants in set B were described as
higher in depression, anxiety and neuroticism than the other, and this may
have affected the PWI-depression relationship. Finally, the depression
measurement format was altered from a 0-4 point Likert scale to a 0-10
point response format. It may be possible that the addition of response
options results in a more gradual slope between PWI and depression scores,
thus making the relationship appear more linear.
The clinical implication of the above finding is that subjective
wellbeing measures be a better measure of depression than depression
indicators. If depression indicates a loss of SWB, then necessarily SWB is a
better measure than are they symptoms of SWB loss, which is what
depression scales measure. For example, a client may receive a depression
score indicative of moderate clinical severity, but their PWI score may be
well within the normal range, indicating that they are currently coping and
able to manage their depressive symptomatology. Conversely, it may be
that another person with very mild depression has a PWI score well under
the normal range, and is not able to cope.
The findings discussed above lead to the potential hypothesis that
the PWI could be used as a sensitive measure of psychological wellbeing, in
that slight deviations in satisfaction below the normative population range
of 70-80%SM represent a dramatic increase in the probability of poor
psychological wellbeing. Furthermore, the PWI could be effectively
employed to evaluate depression treatment. Research suggests that SWB is
sensitive to change in depression symptomatology, whereby SWB decreases
with the onset of depression and increases to the normal, pre-morbid level,
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Subjective Wellbeing and Homeostasis
with the alleviation of depression (Barge-Schappveld et al., 1999;
(Koivumma-Honkanen et al., 2001). The finding that PWI scores related to
depression scores, and that there was evidence of a 70%SM lower threshold
point, indicates that the Personal Wellbeing Index would indeed be an
efficacious evaluation tool used in conjunction with depression therapy.
The relationship between depression and SWB will be more
thoroughly explored in Study 3. This study will be based on an integration
of the SWB Homeostasis theory and the Conservation of Resources theory
(Hobfoll, 1988), allowing for an investigation of the impact of personal and
environmental resources that influence the ability to manage stress and
therefore avoid homeostatic failure and depression.
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Section Two – The Circumplex Model of Affect
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The Circumplex Model of Affect
Section Two Overview
There has been tremendous growth in research on the construct of
affect, and much evolution of its conceptualisation. Historically viewed as a
bipolar construct, affect was believed to involve the mutually exclusive
states of positive affect and negative affect. These were typically measured
with inventories such as the popular and longstanding Positive and Negative
Affect Schedule (Watson & Tellegen, 1985).
In 1980, Russell made a major contribution to the literature by
proposing that the dimensions of positive and negative affect define a
circumplex, in which mood description can be systematically arranged
around the perimeter of a circle. This model depicts affect valence and
activation as orthogonal constructs, and measures the four unipolar
dimensions of high activation positive affect, low activation positive affect,
high activation negative affect, and low activation negative affect.
Huelsman, Nemanick and Munz (1998) developed their 4Dimensional Mood Scale in order to operationalise the circumplex model by
augmenting the Positive and Negative Affect Schedule with a set of items
from the Affective Lexicon (Clore, Ortony & Foss, 1987). This scale
contains four subscales representing the quadrants of the affective
circumplex.
The aim of this study was to replicate the factor analysis for the 4Dimensional Mood Scale conducted by Huelsman and colleagues (1998),
and to evaluate the appropriateness of the items in each factors used therein
to describe affective experience. While the replication was successful and
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The Circumplex Model of Affect
the factors were strong and reliable within the Australian sample, there was
concern that the items used in the scale do not broadly define the quadrants
of the circumplex model.
Therefore, this study tested the hypothesis that alternative items,
chosen for their statistical strength and ability to represent the circumplex
model, would prove equally as statistically powerful in factor analysis as
those in the original 4-DMS.
Alternative items were used to represent the high activation positive
and negative subscales. The original low activation positive affect items
from the 4-DMS were retained. Alternative items for the fourth factor,
which was originally ‘Tiredness’ in the 4-DMS, were sought, however the
high and low activation negative factors could not be reliably separated
through factor analysis when low activation negative affect was broadly
defined.
The subscale reliability of the two revised subscales were
comparable with the original subscales, with Cronbach’s Alpha statistics of
.88 and .89, as compared to .91 and.87 for the high activation negative and
positive subscales respectively. It was concluded that a four-factor model
best represents the circumplex model, acknowledging the conceptual
limitations of the fourth factor. Affect measurement should be
supplemented with a brief depression inventory, such as the Beck
Depression Inventory, in order to encapsulate the breadth of the low
activation negative affect quadrant.
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The Circumplex Model of Affect
Chapter 9
Measurement of the Circumplex
9.1
Revisiting the Circumplex Model
In the past two decades, there has been tremendous growth in
research on the construct of affect. Over this time, the conceptualisation of
affect has evolved from a bipolar model to the currently accepted
circumplex model, as was discussed in Chapter 3.
The circumplex, as presented in Russell’s (1980) original model,
made a major contribution to the literature by proposing that affect can be
represented by valence (positive and negative affect) and activation, with
mood descriptors systematically arranged around the perimeter of a circle
(Watson, Weise, Vaidya & Tellegen, 1999).
In a circumplex model, affect valence and activation are orthogonal
dimensions. While each dimension is bipolar, spanning positive to negative
affect and high to low activation with each dimension, they are measured as
four independent, unipolar dimensions. Therefore, the circumplex measures
high activation positive affect (e.g. active, energetic, strong), high activation
negative affect (e.g. hostile, nervous, guilty), low activation positive affect
(e.g. relaxed, calm, serene), and low activation negative affect (e.g. tired,
drained, drowsy).
A circumplex structure is typically derived from a dimensional
analysis (e.g. factor analysis or multidimensional scaling) of proximity
ratings for a set of stimuli (e.g. affect terms) (Feldman Barrett & Fossum,
2001; Remington, Fabrigar & Visser, 2000). When plotted against this
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The Circumplex Model of Affect
dimensional conceptualisation, affect terms are visually represented
meaningfully around the perimeter of the space defined by the axes, where
‘like’ terms (such as anger and agitation) fall close together, and dissimilar
terms fall in opposing quadrants of the circumplex.
The affective circumplex is highly robust and emerges whenever
individuals label or communicate their own or others’ affective experiences
(Feldman Barrett and Fossum, 2001). Data clearly demonstrate that the
circumplex schematic captures important properties of mood ratings,
however the extent to which the data fits the model is dependent on factors
such as the timeframe of affective judgement and ambiguity of affective
states (Remington, Fabrigar & Visser, 2000; Watson et al., 1999).
9.2
Early Affect Measurement
The most widely utilised affect measure is the Positive and Negative
Affect Schedule (PANAS), which was devised by Watson and Tellegen
(1985), and contains 20 adjectives, with ten each for both the positive affect
and negative affect factors.
Although an extremely popular affect measure, the major limitation
of the PANAS is that it contains adjectives only relating to the high poles of
each construct. This measure thus falls short of fully measuring Watson and
Tellegen’s (1985) model, depicted in Figure 5, which implies affective
activation by describing positive and negative affect as two orthogonal
bipolar dimensions.
The decision to include only high pole markers of each construct
was based on the assumption that low poles of affect are adequately
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The Circumplex Model of Affect
measured by low ratings on the high pole items. This assumption has
independently been questioned. Authors such as Diener, Smith and Fujita
(1995) and Burke, Brief, George, Roberson and Webster (1989) have
presented compelling evidence that positive affect and negative are separate
constructs with weak negative relationships, indicating that they are largely
independent.
Moreover, the PANAS has been widely criticised for its inability to
represent the entirety of experienced affect due to its measurement of only
highly activated affect. It cannot be assumed, for instance, that the absence
of the highly activated negative mood anger indicates low activated mood
(such as relaxation for low activated positive mood, or tiredness for low
activated negative mood), as these are relatively unrelated. Thus, while
affect is descriptively bipolar (with positive affect being the semantic
bipolar opposite of negative affect), it must be measured as independent
unipolar dimensions.
9.3
The 4-Dimensional Mood Scale
Huelsman, Nemanick and Munz (1998) created an alternative affect
scale to address the limitations of the PANAS. They created a scale based
on the PANAS with additional affect terms added to measure low pole
positive and negative affect, thus meeting the assumptions of a circumplex
structure. This scale, known as the 4-Dimensional Mood Scale, consists of
20 affect items that fall within the four unipolar dimensions of the
circumplex. The items for each dimension comprise:
1. Positive Energy (High PA) – Active, Energetic, Lively, Vigorous
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The Circumplex Model of Affect
2. High Negative Affect (High NA) – Aggravated, Agitated,
Hostile, Irritable, Upset, Uptight
3. Relaxation (Low NA) - Calm, Peaceful, Relaxed, Serene,
Tranquil
4. Tiredness (Low PA) - Exhausted, Fatigued, Tired, Weary, Worn
Out
A confirmatory factor analysis revealed that positive energy was
negatively related to tiredness, and that high negative affect was strongly
negatively related to relaxation and positively related to tiredness (low
negative affect).
Two major criticisms of the 4-Dimensional Mood Scale (Huelsman,
Nemanick & Munz, 1998) are apparent. Firstly, their scale is based on
Watson and Tellegen’s (1985) conceptualisation of the circumplex, rather
than Russell’s (1980), wherein activation is implied, and positive and
negative affect are two separate dimensions. In contrast, Russell’s model
depicts affect (positive and negative) as one dimension of the circumplex,
and activation as the other. The impact of this difference is seen in the
description of the factor ‘Tiredness’ as low activation positive affect, and
‘Relaxation’ as low activation negative affect. This implies that tiredness
indicates the absence of positive affect, and relaxation indicates the absence
of negative affect. In a description of affect fitting a truly circumplex
model, however, positive affect is either high or low in activation, but
remains positive nonetheless, thus ‘energetic’ is high in activation, and
‘serene’ is low in activation.
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The Circumplex Model of Affect
The second criticism of the 4-Dimensional Mood Scale involves the
appropriateness of the items used to represent a circumplex model. The
items used by Huelsman, Nemanick and Munz (1998) were selected on the
basis of statistical strength through factor analysis. Therefore, while these
items form four psychometrically strong and reliable factors, it may be
argued that this does not necessarily mean that the factors adequately
represent the circumplex theory. For instance, the five items within the
‘tiredness’ factor have near identical meanings, and thus could be expected
to clump together closely when plotted with multidimensional scaling
techniques. Items adequately representing circumplex theory would be
expected to fall evenly around the perimeter of each quadrant of the model.
9.4
Conclusions
The circumplex model, which comprises the two orthogonal
dimensions of valence and activation, is the currently accepted
conceptualisation of affect. This model assumes that the experience of
affect can be categorized within the four independent unipolar dimensions
of high activation positive affect, high activation negative affect, low
activation positive affect and low activation negative affect.
Early measures of affect focused on the high poles of mood, but
neglected measurement of the low poles, thus falling short of a
comprehensive assessment of affective experience. Huelsman, Nemanick
and Munz (1998) compensated for this limitation by expanding the original
PANAS with additional affect items to measure the dimension of low
activation. Their 4-Dimensional Mood Scale marks an evolution of affect
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The Circumplex Model of Affect
measurement by operationalising the circumplex model, however it is not
without its limitations. In particular, the items used to represent each of the
quadrants of the cirumplex may be improved in therms of their ability to
represent the content of each quadrant.
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The Circumplex Model of Affect
Chapter 10
Study 2 Methodology
10.1
Aim
This study aims to replicate the factor analysis conducted by
Huelsman, Nemanick and Munz (1998) in order to validate the 4Dimensional Mood Scale for an Australian population. Furthermore, this
study aims to conduct an experimental factor analysis with alternative affect
items that represent the circumplex model of affect, in order to test the
reliability of the new subscales.
10.2
Rationale
It is expected that the 20 items of the 4-Dimensional Mood Scale
will be explained by four constructs via factor analysis, and that these will
be consistent with those extracted by Huelsman, Nemanick and Munz
(1998). As the scale is based on the Positive and Negative Affect Schedule,
a highly reliable and replicable measure, it predicted that a replication of the
factor analysis conducted by Huelsman and colleagues (1998) will yield
similar results in an Australian community sample.
This study also aims to test whether the 4-Dimensional Mood Scale
can be improved to more comprehensively represent the circumplex model
of affect by selecting alternative items to represent the four dimensions.
Four factors will be extracted from a pool of 60 affect items, including those
in the 4-DMS, in an experimental factor analysis. Alternative items will be
selected for the four constructs on the basis of both psychometric strength
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The Circumplex Model of Affect
and ability to represent the circumplex model. The reliability of these
revised subscales will then be tested. It is expected that the four revised
subscales representing high activation PA, high NA, low PA and low NA
will be as reliable as those in the 4-Dimensional Mood Scale. Moreover, it
is predicted that these sub-scales will more comprehensively represent the
circumplex model of affect.
10.3
Research Hypotheses
The hypotheses for the Study 2 include:
1. That the factor analysis for the 4DMS will be successfully
replicated,
2. That alternative items will prove equally as statistically
powerful in the factor analysis,
3. That the alternative items used to create the new subscales
will better fit the theory of circumplex affect than those
originally used in the 4DMS.
10.4 - Method
10.4.1 Materials
The questionnaire package used in Study 1 contained the 20 items of
the Four Dimensional Mood Scale (Huelsman, Nemanick & Munz, 1998),
along with 40 additional mood items. The alternative affect items were
taken from the Affective Lexicon (Clore, Ortony, & Foss, 1987). The
additional items included:
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The Circumplex Model of Affect
Alert
Drained
Anxious
Aroused
Drowsy
At ease
Determin
ed
Lethargic
Eager
Ashamed
Placid
Inspired
Sluggish
Scared
Strong
Pleased
Interested
Spent
Restful
Tense
Attentive
Dull
Contente
d
Enthusias
tic
Distresse
d
Quiet
Numb
Jittery
Solemn
Still
Bored
Afraid
Nonchala
nt
Lazy
Guilty
Excited
Sleepy
Nervous
Proud
Untroubl
ed
These 60 affect items were divided into four sections within the
Questionnaire package and measured on an 11-point Likert scale ranging
from 0 (strongly disagree that the word represents how you feel) to 10
(strongly agree that the word represents how you feel), see Appendix B.
10.4.2 4-Dimensional Mood Scale
The 4-Dimensional Mood Scale is a 20-item measure of trait mood,
based on the circumplex model of affect. This scale measures the valence
and activation of affect as four monopolar dimensions. The 20 items of the
scale were selected from an initial item pool of 60, with 15 items
representing each dimension. These items were selected from the PANAS
(Watson et al., 1988), the Job Affect Scale (Burke et al., 1989), the
Activation-Deactivation Adjective Check List (Thayer, 1989) and the
Affective Lexicon (Clore, Ortony, & Foss, 1987).
Four separate principal component analyses were performed on each
of the mood dimensions. An oblique rotation was employed, and only items
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The Circumplex Model of Affect
with structure coefficients greater than or equal to .71 were considered part
of a factor. After rotation, the items in each factor were as follows:
Factor 1
Factor 2
Factor 3
Factor 4
High PA
Low PA
High NA
Low NA
(Positive
(Tiredness)
(Negative
Energy)
Active
(Relaxation)
Arousal)
Exhauste
Aggravat
Calm
d
ed
Energetic
Fatigued
Agitated
Peaceful
Lively
Tired
Hostile
Relaxed
Vigorous
Weary
Irritable
Serene
Worn
Upset
Tranquil
Out
Uptight
The internal consistency reliability coefficients for these factors
ranged from .87 to .93 (Huelsman, Nemanick and Munz, 1998).
10.5
Procedure
10.5.1 Recruitment of Participants
Study 1 and study 2 results are based on the data from the same
group of participants. Participant recruitment details and demographic data
are outlined in Chapter 6.4.1.
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The Circumplex Model of Affect
Chapter 11
Study 2 Data Analysis
11.1 – Study 2 - Assumptions of Normality Testing
It is expected that many of the 4-Dimensional Mood Scale word
items will have overall scores that are positively or negatively skewed, as it
is predicted that the majority of the sample will score highly on positive
word/moods (such as ‘active’, ‘proud’ and ‘contented’) and low on negative
word/moods (such as ‘scared’, ‘ashamed’ and ‘agitated’).
To investigate the statistical significance of the degree of skewness
and kurtosis affecting the 60 mood variables within the Questionnaire, the
skewness and kurtosis statistics were divided by the standard error of
skewness and kurtosis respectively. Tabachnick and Fidell (2001) suggest
that significance of skewness and kurtosis statistics between –4 and +4 are
acceptable for large samples, and that variables falling outside this range
have non-normal curves. Many of the 4-Dimensional Mood Scale variables
in both data sets showed significant levels of skew and kurtosis. Within
Data Set A, 61% of the 4-DMS variables had some degree of skewness or
kurtosis outside the acceptable –4 to +4 range, and in Data Set B, 53.3% of
the variables had some degree of skewness or kurtosis. However, the
skewness values were only just outside the acceptable range in the majority
of variables. In both sets, only five of the 60 variables showed skewness
and/or kurtosis statistics that were extremely deviant: these were
‘Ashamed’, ‘Guilty’, ‘Hostile’, ‘Scared’, and ‘Numb’. Although these
variables displayed extreme positive skewness (S=11.91 to 14.9) and
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The Circumplex Model of Affect
extreme positive (leptokurtic) kurtosis (K=11.87 to 22.2), this is an
anticipated and logical finding.
In Set A, 16 of the 60 of the 4-DMS variables contained extreme
outliers according to the boxplots, however only six of these had three or
more extreme values; these were the variables ‘ashamed’, ‘guilty’ ‘hostile’,
‘numb’, ‘scared’ and ‘afraid’. The means for these items were very low
(between 9%SM and 13%SM), and these variables were also highly
leptokurtic, suggesting that there is a low level of variability between scores
from the participants. Therefore, any case with slightly higher scores would
be identified as an extreme outlier. To investigate the significance of the
univariate outliers, the standardised scores (z scores) were analysed.
Tabachnick and Fidell (2001) suggest that z scores in excess of 3.29 are
potential outliers, but that a few standardised scores in excess of 3.29 are
expected in large samples. Six of these variables were deemed to have
significant outliers (with z scores over 3.9). These included the 4-DMS
items previously discussed, ‘Ashamed’ (z = 4.7), ‘Guilty’ (z = 4.6) and
‘Hostile’ (z = 3.98), as well as ‘Numb’ (z =4.5), ‘Jittery’ (z =4.0) and
‘Scared’ (z =4.4).
In Data Set B, an examination of the boxplots of the 4-DMS
revealed the above five variables as having more than one extreme value.
There were six variables with standardised scores (z scores) in excess of
3.29 indicating that they are potential outliers (the items ‘Dull’, ‘Guilty’,
‘Hostile’, ‘Numb’, ‘Jittery’ and ‘Scared’) however none of the z-scores were
over 3.86.
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The Circumplex Model of Affect
No extreme cases were deleted from the data sets as this would be
detrimental to the investigation of the relationship between PWI and other
variables in individuals with high negative mood. Although the nonnormality of the variables with extreme outliers could be reduced with
transformations, these will not be performed as transforming the data would
render them less interpretable. As an alternative means of compensating for
data non-normality, significance will be accepted at p=.01. Additionally,
the possible limitations of retaining the non-normal cases will be
summarised in the general discussion, Chapter 18.5.2.
11.2
Replicated Principle Components Analysis with Varimax
Rotation
The first aim was to replicate the factor structure and reliability of 60
affect items chosen by Huelsman, Nemanick and Munz (1998) representing
the dimensions of high positive affectivity, low positive affectivity, high
negative affectivity, and low negative affectivity.
Tabachnick and Fidell (2001) suggest that it is comforting to have at
least 300 cases, so the current sample size of 482 (with 236 in Data Set A
and 246 in Data Set B) may be considered adequate for this analysis. The
factorability of the matrix was confirmed though an inspection of the
correlation matrix, which showed that all items correlated >.3 with at least
one other item. Furthermore, Bartlett’s test of Sphericity statistic was
significant (Bartlett’s = p <.001) and the Kaiser-Meyer-Olkin measure of
sampling adequacy was .89.
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The Circumplex Model of Affect
The initial extraction yielded 11 factors with eigenvalues >1.
However, there was a considerable statistical distance between the fourth
factor (with eigenvalue of 2.9) and the fifth factor (with eigenvalue of 1.7)
considering the eigenvalues of factors five to eleven decreased by roughly
.2. Furthermore, inspection of the scree plot clearly suggests that a fourfactor solution accounts for the majority of variance (59.87%).
The factor analysis was repeated with the specification of a fourfactor solution. An initial inspection of the pattern matrix showed a
complex structure with some affect items cross-loading between .31 and .60
across more than one factor. The cross-loading items made meaningful
interpretation of the factors difficult. In addition, the reliability of any items
loading <.4 in a factor analysis is poor (Tabacknick & Fidell, 2001).
Therefore, in order to achieve a simple structure, the solution was rotated
using SPSS VARIMAX, and loadings <.4 were omitted from the
interpretation of the factors. This procedure led to a more simple structure
in the factor matrix that greatly improved interpretability and understanding
of the factors.
Factor 1 accounted for approximately 17% of the total variance in
affect scores, and comprised negative items such as tense, nervous, hostile,
guilty and distressed. These items not only reflected negative mood states,
but also inferred a high level of activation, thus Factor 1 may be best
described as a high activation negative affect. Factor 2 accounts for
approximately 16% of the total variance, and comprised positive items such
as eager, energetic, excited and interested. These items reflect both positive
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The Circumplex Model of Affect
mood state and high activation, thus Factor 2 is described as a high
activation positive affect.
Factor 3 accounted for approximately 13.7% of the total variance in
the initial solution. It comprised negative items such as tired, worn out,
sluggish, and exhausted. These items reflect negative mood states, and are
low in activation. Therefore, factor 3 is described as a low (passive)
negative affectivity factor. Factor 4 accounts for 12.3% of the total
variance, and comprised positive items such as serene, tranquil, relaxed and
calm. These items reflect both a positive mood state and a low level of
activation, and as such, factor 4 reflects low activation positive affect. All
factors contained at least seven pure items that load only onto one factor,
and all factors accounted for an approximately equal and significant amount
of shared variance (12.5 to 17%), making all factors stable and interpretable
(Tabachnick & Fidell, 2001).
Each of the 20 items used by Huelsman, Nemanick and Munz in
1998 as a part of their 4-Dimensional Mood Scale loaded on the appropriate
factor, see Table 6. Therefore, it was decided to analyse these 20 affect
variables by specifying four factors in a Varimax rotation for ease of
interpretability.
11.3
Final Solution and Reliability Analysis
Factor loadings for the specified four-factor solution were derived
from a principle components analysis of the 20 items identified within the 4Dimensional Mood Scale (Huelsman, Nemanick & Munz, 1998). The
matrix was considered factorable as indicated by a significant Bartlett’s test
of Sphericity statistic (χ2 = 3384.25, p<.001) and the Kaiser-Meyer-Olkin
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The Circumplex Model of Affect
Measure of Sampling Adequacy was above .8 (KMO = .89). Varimax
rotation (SPSS VARIMAX) of the solution was conducted to enhance
simple factor structure and interpretability.
Table 6 shows the results of the specified four-factor solution with
five items loading significantly onto the Tiredness factor (Factor 1), six
items leading on the Negative Arousal factor (Factor 2), five items loading
on the Relaxation factor (Factor 3), and four items loading on the Positive
Energy factor (Factor 4). Crossloadings above .4 are listed in the table.
Table 6 - Affect factors and items loadings in the replication of the 4DMS
Tired
Weary
Worn Out
Fatigued
Exhausted
Agitated
Aggravated
Hostile
Upset
Irritable
Uptight
Serene
Tranquil
Relaxed
Peaceful
Calm
Energetic
Vigorous
Lively
Active
1
Eigenv.=8.71
.867
.856
.830
.820
.755
.416
Factor
2
3
Eigenv.=2.58 Eigenv.=2.30
.859
.840
.742
.708
.681
.634
-.435
4
Eigenv.=1.34
-.337
-.376
.869
.820
.814
.781
.684
.840
.817
.812
.796
While two items crossload on two factors, ‘upset’ and ‘calm’, the
loading is significantly higher on the appropriate factor than the other.
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The Circumplex Model of Affect
The reliability analysis showed item-total correlations for all items
were good (above .6). Item-total correlations overall were very high,
ranging from .60 to .85. The internal consistency (Cronbach’s Alpha) for all
factors was high (α = .93 for Factor 1, α = .92 for Factor 2, α = .91 for
Factor 3 and α = .88 for Factor 4.
The subscale correlations of the replicated 4-DMS are reported in
Table 7. The highest correlation was between Negative Arousal and
Relaxation, and Negative Arousal and Tiredness respectively. Equal
correlations were found between Tiredness and the Relaxation and Positive
Energy subscales. The lowest correlation was between Negative Arousal
and Positive Energy. All correlations were significant to the .01 alpha level.
Table 7 Subscale Correlations of the Replicated 4-DMS
1
1 Negative Arousal
2 Tiredness
2
3
4
.55**
-.58**
-.24**
-.39**
-.39**
.39**
3 Relaxation
4 Positive Energy
**
Correlation is significant at the .01 level
11.4
Revised Affect Sub-Scales
This replication of the 4-Dimensional Mood Scale was successful.
Four factors were indeed extracted from the initial 60 items, and each factor
was composed of items that appropriately represented each of the
hypothesised four dimensions of affect, being valence (positive and
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The Circumplex Model of Affect
negative) and activation (high and low). Furthermore, each item within the
4DMS loaded highly on the intended factor, and the internal consistency
coefficients were high for each factor.
As the psychometric properties of the 4DMS were strong, the
argument for the intended supplementary subscales is based on conceptual
rather than methodological issues. A discussion of each factor and the
respective changes will follow.
11.4.1 High Negative Affect (Negative Arousal)
Huelsman, Nemanick and Munz (1998) illustrated the
conceptualisation of negatively valenced and highly active affect with the
items ‘aggravated’ ‘agitated’, ‘hostile’, ‘irritable’, ‘upset’ and ‘uptight’, in a
factor they labelled Negative Arousal. These adjectives certainly are
indicative of negative and highly active mood states, however the meaning
underlying these words is quite specific. For example, the words
‘aggravated’, ‘agitated’, ‘hostile’, and ‘irritable’ all relate to the emotional
state of anger. Whilst they allude to varying degrees of anger (where
irritation may lead to anger whereas hostility may be displayed in verbal and
physical behaviours as a result of anger), these four items are bound
together by a related concept. The remaining variables within this factor,
‘upset’ and ‘uptight’, are rather ambiguous in terms of the emotion or
experience underlying these states. Although somewhat ambiguous, these
two items were classified as affective conditions by Clore, Ortory and Foss
(1987) on the foundations of the Affective Lexicon, as were the other four
items within the factor.
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The Circumplex Model of Affect
Thus, the negative arousal factor proposed by Huelsman and
colleagues (1998) measures aspects of anger and the subjective feelings of
being upset and uptight. Although this certainly comprises negative
affective experiences, the question to be raised is whether or not these items
alone can adequately encapsulate the breadth of high negative affect. Being
angry and feeling upset are surely not the only possibilities in the realm of
highly negative human emotion. On revisiting the original 60 variables
entered into the initial factor analysis, some of the items within this factor
not used in the 4DMS include ‘nervous’, ‘scared’, ‘afraid’, ‘distressed’, and
‘ashamed’ (all with factor loadings above .63). It is thus argued that the
factor of Negative Arousal presented by Huelsman and colleagues (1998) is
too narrowly defined as a result of the conformity of the items used.
Therefore, a different factor consisting of alternative items is proposed.
The items within the high negative affectivity factor to be used in
this exploratory research include ‘agitated’, ‘hostile’, ‘anxious’, ‘nervous’,
‘scared’ and ‘distressed’. It is clear that these items cover a range of highly
activated negative mood states, including anger, anxiety and fear, as well as
the more general and ambiguous state of distress. These adjectives broaden
the measurement of high negative affectivity. Although it could be stated
that broadening the measurement of the factor will render it less defined and
specific, so that it becomes uncertain which emotions are indicated by a
high score on this factor, it is counter argued that we are aiming to measure
highly activated negative affect rather than specific emotion. The
conceptualisation of this circumplex quadrant is broad, and so too must be
its measurement.
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The Circumplex Model of Affect
11.4.2 High Positive Affectivity (Positive Energy)
Huelsman, Nemanick and Munz (1998) illustrated the
conceptualisation of positively valenced, highly active affect, with the items
‘active’ ‘energetic’, ‘lively’ and ‘vigorous’, in a factor they labelled Positive
Energy. These adjectives are quite specific in relating to the feeling of
energy, which is largely based on the subjective and cognitive perception of
physical state (Clore, Ortony, & Foss, 1987). The meaning of the words is
very similar according to dictionary and thesaurus definitions, and these
adjectives, particularly ‘active’, ‘energetic’ and ‘lively’, are commonly used
interchangeably.
As with the previous factor, it is clear that these items adequately
represent the factor, positive energy, but the extent to which the factor of
positive energy represents the intended circumplex conceptualisation of
high activation positive affect is dubious. Some of the 15 items from the
initial factor analysis that load onto the high positive factor not used in the
4DMS include ‘eager’, ‘enthusiastic’, ‘strong’, ‘determined’, and ‘inspired’
(all with factor loadings above .66). Hence, It is argued that the factor of
Positive Energy presented by Huelsman and colleagues (1998) is too
narrowly defined as a result of the conformity of the items used. Thus, once
again, a different factor comprising alternative items is proposed.
The items within the high negative affectivity factor to be used in
this exploratory research include ‘enthusiastic’, ‘eager’, ‘determined’,
‘inspired’ and ‘excited’. This item set is intended as a supplementary factor
rather than a replacement factor. It is believed that positive energy (with a
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The Circumplex Model of Affect
somewhat physical component) is an important and necessary aspect of high
positive affectivity. The proposed items thus provide a measure of affective
states that is more cognitive (with a somewhat psychological emphasis), so
broadening the scope of the factor. Therefore, the original physiological
‘positive energy’ subscale and the new psychological ‘high positive affect’
scale could effectively be used together to broaden measurement of affect
experience.
The five proposed items are classified as Affective Conditions
(exited, determined, inspired) and Cognitive Conditions (eager, enthusiastic)
within the affective lexicon (Clore, Ortony and Foss, 1987). These items
along with those in the 4DMS Positive Energy factor are expected to
provide a broad measurement of high pole positive affect.
11.4.3 Low Negative Affectivity (Tiredness)
Huelsman, Nemanick and Munz (1998) illustrated the
conceptualisation of low activation negative affect with the items
‘exhausted’ ‘fatigued’, ‘tired’, ‘weary’ and ‘worn out’, in a factor they
labelled Tiredness. According to Ortony, Clore and Foss (1987), all of these
low activation negative affect items come from a subgroup called (internal
conditions- nonmental) physical and bodily states. These adjectives are
quite specific in relating to the feeling of tiredness, based on the subjective
perception of physical state. The meaning of the words is extremely similar,
to the point that only one or two could be used as effectively to explain this
factor. The scale reliability analysis of Huelsman Nemanick and Munz’s
(1998) Tiredness factor was very high (α=.93), due to the very high
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The Circumplex Model of Affect
correlations between the items (wherein all items correlated between .82 and
.86 with at least one other item), which suggests that these items are
essentially measuring the same state.
On examining the remaining items from the original 60-item pool
that load on this factor not used in Huelsman et al’s (1998) 4DMS, it was
clear that all of them relate to tiredness (‘drained’, ‘sleepy’, ‘sluggish’,
‘drowsy’ etc), and that this factor would still only represent the
physiological state of tiredness if these alternative items were used, due to
their similarity. The five items used within Huelsman et al’s Tiredness
factor indeed provide a good measurement of the subjective state of
tiredness, but this is a very specific affective state, and one that cannot be
said to represent low activation negative affect as a whole. As this is the
case, it is believed that alternative (and broader) items need to be introduced
into the item pool for this factor, and a subsequent factor analysis
conducted. Therefore, ten new adjectives will be taken from the Affective
Lexicon (Clore, Ortony & Foss, 1987) in replication of the process
undertaken by Huelsman, Nemanick and Munz (1998).
In contemplating the meaning and breadth of low negative affect, it
was considered that this factor would best be measured across affective
states including tiredness as well as symptoms of depression and
dissatisfaction. Therefore, a list of words from the Affective Lexicon that
fit these criteria were extracted. Along with two of the ‘tiredness’ adjectives
that will be retained, the new words selected from the Affective Lexicon
include: bored, sad, sleepy, depressed, gloomy, upset, exhausted, flat, and
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The Circumplex Model of Affect
discontented. These words will be added into a new affect items pool
within the questionnaire for Study 3, and tested in a separate factor analysis.
11.4.4 Low Positive Affectivity (Relaxation)
The items chosen by Huelsman, Nemanick and Munz (1998) to
represent the low activation positive factor (serene, tranquil, relaxed,
peaceful and calm) seem to suggest physical as well as psychological states
and feelings of wellbeing, and suggest a fairly broad conceptualisation of
the factor. Therefore, there are no recommended changes to this factor.
11.5
Factor Analysis and Reliability Analysis of Revised
Subscales
Factor analyses were initially undertaken with Data Sets A and B of
Study 1 separately, yielding two subscales representing high activation
positive affect, and high activation negative affect. These analyses were
undertaken on the data sets separately so that the findings from the first
analysis could be replicated and confirmed with the analysis of the second
data set. As it was previously mentioned, the original ‘Tiredness’ subscale
of the 4-DMS contained only items pertaining to the physiological state of
tiredness, and the variables within the 60-item pool did not allow for a
broader measurement of this quadrant. Therefore, additional affect items
were introduced into an item pool contained in the questionnaire for Study
3, and factor analyses were performed in order to identify an alternative
subscale for this quadrant. Therefore, the following analyses pertain to a
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The Circumplex Model of Affect
two-factor solution based on Study 1 data, and a three-factor solution based
on Study 3 data.
Principle components analysis with varimax rotation was performed
through SPSS FACTOR on 60 items from the Trait Mood Scale (Huelsman,
Nemanick & Munz, 1998) for the sample of 236 (Set A) and 246 (Set B)
participants.
The item correlation matrices were observed for singularity,
multicolinearity and factorability of the items. Some singularity may exist,
as some variables correlated above .8 (such as Exhausted-Fatigued, JitteryNervous, Relaxed/Restful, Tense-uptight). All other items correlated above
.3 with at least one other item (Coakes & Steed, 2001). The Kaiser-MeyerOlkin Measure of Sampling Adequacy (KMO) was well above the required
level of .6 for both sets, and the Bartlett’s Test of Sphericity value was
significant, therefore factor analysis is appropriate. The communalities
were all adequate, ranging between .59 and .85.
11.5.1 High Negative and High Positive Affect Subscales
As both the high negative and high positive affective factors in the
initial rotation contained up to 15 individual items (some of which crossloaded on other components), the number of items in each component was
reduced to five. Items for both the high activation factors were chosen with
a combination of statistical strength and ability to adequately describe the
broad nature of the theoretical factors. In component 1 (High Activation
Negative Affect), the variables chosen included Agitated, Hostile,
Distressed, Anxious and Nervous. For component 2 (Activated Positive
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The Circumplex Model of Affect
Affect), the items Eager, Enthusiastic, Excited, Inspired and Determined
were selected. In the initial trial, the item ‘Lively’ was chosen instead of
Excited, but this item crossloaded on two other factors when tested on Data
Set A. The item ‘Energetic’ was then tested, however this also crossloaded
on one other factor. Finally, ‘Excited’ was tested, and this item was free of
crossloadings in both data sets.
Absolute valued below .4 were suppressed in the Varimax rotation.
Each of the ten items fell into one of the two factors with no crossloadings
(See Table 8).
Table 8 - Two-Factor Affect Solutions for Data Sets A and B
Factor
Agitated
Hostile
Scared
Distressed
Anxious
Nervous
Eager
Enthusiastic
Excited
Inspired
Determined
1
.819
.803
.796
.792
.734
.691
Factor
2
.873
.839
.795
.794
.776
Data Set A
Anxious
Agitated
Nervous
Scared
Hostile
Distressed
Enthusiastic
Eager
Determined
Inspired
Excited
1
.784
.769
.740
.738
.716
.702
2
.813
.804
.782
.723
.694
Data Set B
Factor 1 and 2 accounted for approximately 20% and 18% of the
total variance in affect scores respectively. The reliability analysis showed
item-total correlations in both factors for all items were good (above .6).
Item-total correlations overall were very high, ranging from .63 to .80. The
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The Circumplex Model of Affect
internal consistency (Cronbach’s Alpha) for both factors was high (α = .90
for Factor 1, α = .88 for Factor 2). Furthermore, the chosen items had a
minimal correlation of .3 with at least one other, but the correlations were
not so high as to suggest singularity (in factor 1 R ranged between .42 and
.74, in Factor 2 R ranged from .48 to .79).
11.5.2 Low Negative Affect Subscale
As discussed in the previous section 11.4, the original items used by
Huelsman and colleagues (1998) to represent the low activation negative
affect subscale were deemed an inappropriate representation of the
circumplex model of affect. The five items used in the 4-Dimensional
Mood Scale for low negative affect all related to tiredness, and do not
encapsulate the breadth of low activated negative mood. Therefore, nine
items were selected from the Affective Lexicon (Clore, Ortony & Foss,
1987) and added to the item pool within Study 3, along with the two original
subscale items with the highest factor loadings (tired and fatigued).
In order to conduct a factor analysis and replace the original items
with alternative affect items that more broadly represent the circumplex
model of affect, the item pool used for the factor analysis of the low
activation negative affect subscale included tired, fatigued, bored, sad,
sleepy, depressed, gloomy, upset, exhausted, flat, and discontented.
A principle components analysis with Varimax rotation was
performed through SPSS FACTOR on the 28 affect items (11 items for the
low negative affect scale, five original 4-Dimensional Mood Scale items for
the low activation positive affect subscale, and five and six items for the
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The Circumplex Model of Affect
high activation negative and positive affect subscales respectively) for the
sample of 473 participants within the Study 3 data set.
The item correlation matrix was observed for singularity,
multicolinearity and factorability of the items. Singularity may exist
between ‘upset’ and ‘gloomy’, as these variables correlated to .81. All other
items correlated above .3 and less than .8 with at least one other item
(Coakes & Steed, 2001). The Kaiser-Meyer-Olkin Measure of Sampling
Adequacy (KMO) was well above the required level of .6, and the Bartlett’s
Test of Sphericity value was significant, therefore factor analysis is
appropriate. The communalities were all adequate, ranging between .46 and
.81.
The extraction yielded 4 factors with eigenvalues >1. Furthermore,
inspection of the scree plot clearly suggests that a four-factor solution
accounts for the majority of variance (68.54%). In order to achieve a simple
structure, the solution was rotated using SPSS VARIMAX, and loadings <.4
were omitted from the interpretation of the factors. This procedure led to a
more simple structure in the factor matrix that greatly improved
interpretability and understanding of the factors.
The rotated factor solution revealed that the fourth factor (low
negative affect) solely consisted of items closely related to tiredness (tired,
fatigued, exhausted, sleepy and flat), and therefore was similar with the
original low negative affect subscale devised by Huelsman and colleagues
(1998). Furthermore, the item ‘flat’ crossloaded heavily on the high
negative affect subscale (.57 on low negative affect subscale and .51 on the
high negative affect subscale), and the only item that did not crossload on
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The Circumplex Model of Affect
the high NA subscale was ‘tired’. As the aim of the factor analysis was to
broaden the low negative affect subscale by utilising items depicting varied
affect, a subsequent factor analysis was performed with only one measure of
physical fatigue, ‘tired’. In removing the multiple items with a similar
meaning to ‘tired’, the items would be unable to clump together forming a
subscale representing tiredness.
When the factor analysis was repeated after the subtraction of the
items ‘fatigued’, ‘exhausted’, and ‘sleepy’, a four factor solution was
evident according to the eigenvalues >1 and scree plot. After SPSS
VARIMAX rotation, ‘tired’ was still the only item that loaded solely on the
fourth factor. The only other fourth factor item with a crossloading below .4
on the high negative affect subscale was ‘bored’. The majority of the items
that were chosen according to the theory of circumplex affect to represent
low negative affect fell into the high activation negative affect factor, with
significant crossloadings. Furthermore, the item ‘nervous’ was originally
chosen to represent high activation NA, but fell into the low activation NA
factor. Therefore, only two items could clearly represent low activation
NA, and the addition of other items intended to represent low activation NA
contaminated the original, strong subscale for high activation NA, see Table
9.
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The Circumplex Model of Affect
Table 9 - Four-Factor Affect Solution
Factor
1
2
3
4
Eigenv.=5.96 Eigenv.=3.81 Eigenv.=3.80 Eigenv.=2.97
.829
Distressed
.795
Hostile
.768
Upset
.754
Agitated
.745
.406
Gloomy
.711
Discontented
.659
Scared
.636
.484
Depressed
.627
Anxious
.860
Eager
.802
Enthusiastic
.789
Inspired
.745
Determined
.665
.421
Excited
.840
Calm
.765
Serene
.764
Peaceful
.726
Relaxed
.707
Tranquil
.712
Tired
.606
Bored
.492
.592
Sad
.409
.580
Nervous
.482
.538
Flat
This factor solution is very messy due to significant crossloadings,
and there is no clear fourth factor. It was decided that a fourth factor
consisting of broad items representing low activation NA could not be
statistically extracted. Therefore, the theoretical circumplex model of affect
may best be measured using the four subscales of high and low activation
PA and high and low activation NA, while acknowledging the conceptual
limitations of the low activation negative affect subscale. In addition, a
brief depression inventory such as the Depression Anxiety Stress Scale or
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The Circumplex Model of Affect
Beck Depression Inventory should be used in conjunction with the affect
scale to cover the low activity negative mood states.
A VARIMAX rotated factor analysis with a three factor solution
(covering high activation PA and NA and activation low PA) was
performed, and factor loadings >4 were suppressed for ease of
interpretation, see Table 10. The only item with a significant crossloading
was ‘excited’, which falls within the high PA subscale with a loading of .66,
but crossloads to .43 on the low PA subscale. All other variables fall into
the appropriate subscales without any crossloadings >.4, thus all subscales
were well represented by the chosen items.
Table 10 – Factor Analysis of the Three-Factor Affect Scale
Distressed
Anxious
Agitated
Scared
Hostile
Nervous
Calm
Serene
Peaceful
Tranquil
Relaxed
Eager
Enthusiastic
Inspired
Determined
Excited
1
Eigenv. =3.81
.845
.777
.776
.770
.706
.659
Factor
2
Eigenv. =3.66
3
Eigenv. =3.51
.833
.795
.757
.753
.742
.429
.864
.814
.804
.761
.661
Factor 1, 2 and 3 accounted for approximately 24%, 23% and 22%
of the total variance in affect scores respectively, with the three-factor
solution accounting for 69% of variance in total. The reliability analysis
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The Circumplex Model of Affect
showed item-total correlations in all factors for all items were good (above
.6). Item-total correlations overall were very high, ranging from .60 to .82.
The internal consistency (Cronbach’s Alpha) for all factors was high; alpha
= .88 for Factor 1 (high NA), alpha = .91 for Factor 2 (low PA), and alpha =
.89 for Factor 3 (high PA). Furthermore, the chosen items showed
favourable correlations with each other within the correlation matrix,
wherein the items had at least a .3 correlation with at least one other, but the
correlations were not so high as to suggest singularity (in factor 1 R ranged
between .42 and .74, in Factor 2 R ranged from .48 to .79).
With regard to the item ‘excited’ that crossloaded on the low
activation PA subscale, this item had a high item-total correlation score
(.67) indicating that the construct measured by the item is congruent with
that of the scale as a whole. Furthermore, the internal consistency statistic
(Cronbach’s Alpha) would be reduced from .89 to .87 if the item were
deleted.
The subscale correlations were again calculated, and are reported in
Table 11. The highest correlation occurred between the High Activation
and Low Activation Positive Affect subscales. Correlations between the
High Activation Negative and Low Activation Positive subscales were also
high. The High Activation Negative and Positive Affect subscales were the
least correlated.
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The Circumplex Model of Affect
Table 11 Subscale Correlations of the Proposed Scale
1
1 High Activation NA
2 High Activation PA
2
3
-.35**
-.51**
.64**
3 Low Activation PA
**
Correlation is significant at the .01 level
In conclusion, the results of these statistical analyses indicate that the
factors of high and low activation negative affect cannot be reliably
separated through factor analysis when low activation negative affect is
broadly defined. A number of different items and item combinations were
tested in the factor analysis of the low activation NA subscale, and the
analyses were conducted on a respectable sample size. However, the factors
of high activation negative affect and low activation negative affect can be
separated via factor analysis when the latter is restricted to tiredness.
Therefore, it can be concluded that the most appropriate method of
measuring circumplex affect is with the four-factor scale covering high and
low activation PA and high and low activation NA, taking into account the
conceptual limitations of the narrowly-defined low activation negative
affect subscale. It is recommended that the affect scale is used concurrently
with a brief inventory (such as the Depression Anxiety Stress Scale or BDI)
used to cover low activation negative affect.
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The Circumplex Model of Affect
11.6
Summary
To briefly summarize this chapter:

The factor analysis of the 4-Dimensional Mood Scale was
successfully replicated within an Australian community sample. All
items within the 4-DMS loaded on the appropriate factors without
crossloadings, with the exception of the item ‘calm’, which loaded
positively on the Relaxation factor (.68), and negatively on the
Negative Arousal factor (-.44), and ‘upset’, which loaded positively
on the Negative Arousal factor (.71) and positively on the Tiredness
factor (.42).

The four subscales of the replicated 4-DMS were highly reliable,
with internal consistency levels of .88 to .93.

While the Negative Arousal and Positive Energy subscales of the 4DMS were statistically strong and reliable, the items within these
subscales are too narrow to represent the quadrants of the
circumplex model of affect. Therefore, the two high activation
factors, for positive and negative affect, were reconstructed with
alternative affect items from the original 60-item pool. The high
activation negative affect factor comprised hostile, agitated, scared,
distressed, nervous, and anxious. The high activation positive affect
factor comprised eager, enthusiastic, inspired, determined and
excited.

The Relaxation factor of the 4-DMS was deemed an appropriate
measure of low activation positive affect, and so was retained. The
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The Circumplex Model of Affect
items within this factor comprise calm, serene, peaceful, relaxed and
tranquil.

The Tiredness subscale was similarly found to be reliable, however
the items within this factor do not broadly measure the circumplex
quadrant of low activation negative affect. As all of the items from
the original 60-item pool loading on this factor were associated with
the physiological experience of tiredness, alternative items were
selected from the Affective Lexicon in order to enable a broader
measurement of this quadrant. The additional items comprise tired,
fatigued, bored, sad, sleepy, depressed, gloomy, upset, exhausted,
flat, and discontented.

A final factor analysis was conducted specifying a four factor
solution using the above 28 items. The results indicated that there
was no clear factor of low activation negative affect, as items within
the high and low activation negative affect factors significantly
crossloaded.

The circumplex model of affect can therefore be measured using the
four subscales of high and low activation positive affect, and high
and low activation negative affect. The high activation subscales
were re-defined using alternative items, whereas the original low
activation subscales from the 4-DMS were used. This measurement
should be supplemented with a measure of depression to provide
broader measurement of the low activation negative affect quadrant
of the circumplex.
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The Circumplex Model of Affect
Chapter 12
Discussion
A good deal of research has been conducted on the construct of
affect, and in particular, the relationship between positive and negative
affect. The currently accepted conceptualization of affect is the circumplex
model. This model depicts affective states as the combination of the bipolar
dimensions of valence and activation, which lie at 90 degrees of one
another. Huelsman, Nemanic and Munz (1998) operationalised the
theoretical circumplex model by assigning affect variables via factor
analysis to the four quadrants of the circumplex.
12.1
Replication of the 4-Dimensional Mood Scale
The factor analysis of the 4-Dimensional Mood Scale was
successfully replicated within an Australian community sample. Two items
significantly crossloaded on two factors. These included the item ‘calm’,
which loaded positively on the Relaxation factor (.68), and negatively on the
Negative Arousal factor (-.44), and ‘upset’, which loaded positively on the
Negative Arousal factor (.71) and positively on the Tiredness factor (.42).
These were not particularly concerning, as the primary loadings on the
intended factors are significantly higher than the other factors. The four
subscales of the replicated 4-DMS were highly reliable, with internal
consistency levels of .88 to .92. These were comparable to the internal
consistency levels reported by Huelsman, Nemanick and Munz (1998),
which ranged from .87 to .93, see Table 12.
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The Circumplex Model of Affect
The correlations between the subscales of the replicated 4-DMS
were calculated, and reported in Table 7. These subscale correlations, and
the subscale reliability indices (Cronbach’s alpha) can be compared with
those reported by Huelsman, Nemanick and Munz (1998), See Table 12.
Table 12 Comparison of Subscale Correlations Between the 4-DMS
and the Proposed Scale
1
2
3
4
1 Neg Arousal -Replication
=.88
.55**
-.58**
-.24**
Huelsman et al
=.91
-.57**
.61**
-.31**
– Replication
-
=.92
-.39**
.39**
Huelsman et al
-
=.93
-.46**
-.43**
3 Relaxation – Replication
-
-
=.92
-.39**
Huelsman et al
-
-
=.88
-.40**
4 Pos Energy – Replication
-
-
-
=.90
Huelsman et al
-
-
-
=.87
2 Tiredness
**
Correlation is significant at the .01 level
The subscale correlations of the original and the revised 4-DMS are
remarkably close. Moreover, the order of correlation strength between
subscales is consistent in original and replicated scales. In both analyses,
the highest correlated subscales are Negative Arousal and Relaxation, and
the least correlated are Negative Arousal and Positive Energy.
The Tiredness subscale had the highest reliability (Cronbach’s
alpha) coefficient within both the original and replicated 4-DMS.
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The Circumplex Model of Affect
Cronbach’s alpha was found to be lower for the replicated Negative Arousal
subscale, and higher for the replicated Positive Energy and Relaxation
subscales, than those reported by Huelsman, Nemanick and Munz (1998).
12.2
Three Proposed Affect Subscales
This study expands on the work of Huelsman et al (1998) by reevaluating and refining the affect variables that were used in the 4Dimensional Mood Scale. This re-evaluation was undertaken due to a
concern that the items initially used in the scale developed by Huelsman et
al (1998) did not adequately encapsulate the breadth of each quadrant of the
circumplex model.
For this investigation, the context of the two high activation
subscales (high activation positive and high activation negative affect) was
changed using different affect items from Huelsman et al’s (1998) original
60-item pool using a combination of theoretical and statistical methods of
selection. Following factor analysis, the items chosen for these two
subscales included anxious, agitated, scared, hostile and nervous for the
high activation NA subscale, and eager, enthusiastic, inspired, determined
and excited for the high activation PA subscale. The original items within
the low activation positive affect subscale used by Huelsman et al (1998)
were retained as they appear to adequately represent this quadrant of the
circumplex model. The low activation negative subscale was also retained
with no item changes, as this subscale was not able to be redefined using
alternative items that encapsulate this quadrant more broadly.
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The Circumplex Model of Affect
The low activation negative affect subscale was re-developed by
selecting a variety of affect items from the affective lexicon (Clore, Ortony
& Foss, 1987), as the original items only accounted for the subjective
physiological experience of tiredness and there were no appropriate
alternative variables within Huelsman et al’s (1998) original 60-item pool.
A factor analysis was performed in order to allocate the new items to the
low activation NA subscale, however this factor could not be clearly
defined. The only variable that loaded solely on this factor was ‘tired’,
while the introduction of the additional variables intended for the low NA
subscale resulted in the corruption of the high activation NA subscale.
Thus, some of the variables intended for the low NA subscale were placed
in the high NA subscale, and vice versa.
The highest subscale correlation of the replicated 4-DMS was found
between Negative Arousal and Relaxation could not be compared with the
proposed scales, as the low activation negative affect factor was not
included in the revised affect scale. The high activation positive and low
activation positive affect subscales had the highest correlation within the
new scale, correlating to .64. In both the replicated 4-DMS and the
proposed scales, the high activation negative affect (Negative Arousal
factor) and low activation positive affect (Tiredness factor) had the second
highest correlation statistic (-.55 in the 4-DMS, -.51 in the new scale).
Finally, the high activation negative affect and high activation positive
affect subscales of both the original and the proposed scales were the least
correlated, with statistics of -.24 and -.35 respectively. The reliability
coefficients of the two revised subscales were high, with Cronbach’s alpha
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The Circumplex Model of Affect
statistics of .88 and .89 for the high activation negative and positive
subscales respectively. The scale reliability of the revised high activation
NA subscale was slightly lower than that in the original 4-DMS (differing
by .03), and the reliability of the revised high activation PA subscale was
higher than the original (differing by .02).
These findings suggest that although the items in two of the
subscales of the new affect scale have been changed, the subscales remain
as reliable as the original, and the relationship between the subscales,
according to correlation indices, remains consistent.
As the high activation NA subscale contains items that broadly
encapsulate this quadrant of the circumplex model, and incorporates the
major affective categories of anxiety, fear, anger and distress, this factor
will be retained. The high activation PA subscale also represents a broad
measurement of this cirumplex quadrant, so this factor will also be retained.
The original low activation positive affect subscale items from the 4-DMS
adequately define this affective quadrant. However, the affective categories
that are theoretically consistent with low activation NA, depression,
sadness, boredom etc, could not be encapsulated in a fourth factor.
Therefore, the items within the four subscales of the current affect scale
measure the conceptual space of the circumplex model, as defined by the
valence and activation axes. Due to the conceptual limitations of the low
activation negative affect subscale, this scale is to be used in conjunction
with a depression inventory, in order to provide broad measurement of all
affective quadrants.
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Section Three – Integration of SWB and Stress Theory
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Integration of SWB and Stress Theory
Section Three Overview
In the final section of this thesis, the Conservation of Resources
Theory (Hobfoll, 1988) is reviewed. This is an integrative theory of stress
that encapsulates the relationship between stress and resources, and
incorporates the environmental aspects of stress by acknowledging
interrelationships between the individual, immediate social group, and the
community as a whole. The four resource categories identified in this
theory are reviewed, including objects, personal characteristics, conditions
and energies.
The conceptual similarities between the Conservation of Resources
Theory and SWB Homeostasis theory are then identified, and rationale for
the integration of these theories is presented. The Conservation of
Resources Theory assumes that stress results when environmental demands
placed on an individual exceed their external and internal resources. The
SWB homeostasis theory assumes that homeostasis failure results when
environmental stressors cannot be effectively managed by the individual’s
cognitive buffering mechanisms. An hypothesised integrated model of
homeostasis and stress is then presented, identifying the relationship
between stable individual characteristics, internal and external resources,
and the outcome states of stress and SWB, and the effect of environmental
stressors on each of these components.
The data analysis of Study 3 had a threefold aim. First, regression
analyses were undertaken in order to identify the strongest predictor of
SWB. The three affect subscales devised in Study 2 were found to be
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Integration of SWB and Stress Theory
stronger predictors than depression, neuroticism and extraversion, which
were identified as SWB predictors in Study 1. Second, the hypothesis that a
curvilinear relationship exists between SWB and depression was tested, and
these results were compared to those of Study 1. There was a slightly nonlinear relationship between the variables, and a plateau occurring at the
70%SM threshold point, supporting the findings of Study 1. Furthermore,
there was evidence that a curvilinear model better fits the data when SWB
scores occurring within the normative range (>70%SM) are separated from
those below, indicating that the relationship between depression and SWB
in conditions of homeostasis regulation differs from that in conditions of
homeostasis failure.
Finally, some components of the hypothesised integrated model of
SWB homeostasis and Conservation of Resources Theory were tested
through Structural Equation Modelling. Model 1 (based on variables from
Questionnaire 1) comprised affect, personality, control, optimism, and
SWB. Model 2 (based on variables from Questionnaire 2) comprised affect,
self-esteem, SWB, stress, anxiety and depression. The two models tested
had adequate goodness of fit indices after minor modification, and explained
43% and 88% of variance in outcome variables respectively.
These findings indicate that there is support for an integrated SWB
and stress theory. This integrated theory promotes a deeper understanding
of the process of SWB regulation and homeostasis failure.
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Integration of SWB and Stress Theory
Chapter 13
Conservation Of Resources Theory (Hobfoll, 1988)
Both the relationship of psychological traits to health, and the link
between stress and health, represent fundamental building blocks of health
psychology (Hobfoll, Banerjee & Britton, 1994). The link between stress
and illness is much stronger for some individuals than others, causing
controversy regarding the nature of the stress-health relationship. However,
the difference in individual susceptibility to the effects of stress can be
explained by differences in individual levels of psychosocial risk factors and
differences in levels of psychosocial resources (Hobfoll, Banerjee & Britton,
1994). The SWB Homeostasis theory explains some of the psychosocial
risk factors that may underpin the variance in susceptibility to stress
observed in individuals; namely differences in personality, affect and
positive cognitive biases. Although these factors allow a comprehensive
understanding of the maintenance of SWB, they do not fully account for
observed individual differences in stress vulnerability and resilience.
Conservation of Resources (COR) theory (Hobfoll, 1988; 1989) is
an integrative theory of stress that places emphasis on both environmental
and internal processes with relatively equal measure. In contrast to the
exclusively cognitive nature of modern stress theories (Lazarus, 1966;
Lazarus & Folkman, 1984, 1987), COR theory incorporates the
environmental aspect of stress by acknowledging interrelationships between
the individual, immediate social group and the community as a whole
(“individual-nested in family-nested in tribe”, Hobfoll, 2001, p338).
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Integration of SWB and Stress Theory
COR theory suggests that the promotion of wellbeing and prevention
of stress depend on the availability and successful management of resources
(Hobfoll, 2001). A number of principles and corollaries of COR theory
illustrate the relationship between stress and resources follow.
Firstly, resource loss is disproportionately more salient than
resource gain. This means that given equal amounts of loss and gain, loss
will have significantly greater impact. Resource gains are seen as acquiring
their saliency in light of loss. When resources are lacking, lost or invested
without consequent gain, people become vulnerable to psychological and
physical disorders and debilitated functioning (Hobfoll, 1988; Hobfoll &
Jackson, 1991), and therefore, resource gain becomes important in the
context of loss (Hobfoll, 2001).
Second, resources must be invested to protect against resource loss,
recover from losses, and gain resources. The process of resource
investment may sound coldly economic, however it can be applied even to
abstract resources, such as the investment of time, energy and trust for the
acquisition and maintenance of love. For instance, an individual may invest
their self-esteem by approaching an attractive person of the opposite sex. If
the meeting is successful, the individual may gain love. While the
individual risks resource loss, as rejection may result in some self-esteem
loss, an individual who lacks self-esteem may not take the risk at all and
avoid potential failure.
Third, COR theory assumes that those individuals with greater
resources are less vulnerable to resource loss and more capable of resource
gain, and in contrast, those who lack resources are more vulnerable to
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Integration of SWB and Stress Theory
resources loss and less capable of resource gain. Individuals with greater
resources are less likely to encounter resource loss in challenging
circumstances because they can successfully mobilize their resources in
response to the circumstances to prevent loss, and deploy resources in order
to produce resource gain (Hobfoll, Banerjee & Britton, 1994).
Within this chapter, stress and stress buffers will be briefly
discussed, and each of the four categories of resources within Hobfoll’s
(1988) COR theory will be explored.
13.1
Stress
The traditional and arguably the most robust conceptualization of
stress was posited by Lazarus (1966) and advanced more specifically by
Lazarus and Folkman (1984). They depicted psychological stress as the
outcome of a particular kind of relationship between person and
environment, wherein the stress relationship is one in which demands, tax,
or exceed the person’s resources. Each situation or event that an individual
encounters is appraised as involving harm (negative), the threat of harm
(potentially negative), or challenge (positive) (Lazarus, 1990). The
appraisal of the situation or event is known as the primary appraisal. The
individual then appraises whether they have the resources needed to meet
the demands of the situation, which is known as secondary appraisal. Once
a person has appraised a transaction as stressful, coping processes are
brought into play to manage the troubled person-environment relationship.
These processes influence the person’s subsequent appraisal and hence the
kind and intensity of the stress reaction (Lazarus, 1990).
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Integration of SWB and Stress Theory
Within the COR model, stress is defined as “a reaction to the
environment in which there is a perceived threat of a net loss of resources, a
perceived net loss of resources, or a perception that an investment of
resources is not producing a net gain (Hobfoll & Stephens, 1990, p457).
This is a resource-based theory of stress, as opposed to an appraisal-based
theory of stress such as that previously discussed. The basic tenet of this
model is that people strive to retain, protect and build resources, and that
what is threatening to them is the potential or actual loss of these valued
resources. In other words, individuals strive to construct a world in which
they are protected, comfortable, loved, and esteemed (Hobfoll, 1988), and
the key function of the stress response is defense against resource loss
(Quick & Gavin, 2001).
According to COR theory, stress will occur when any of three
conditions occur; 1) environmental circumstances threaten resource loss, 2)
environmental circumstances result in actual resource loss, or 3) individuals
invest resources without receiving adequate return on their investment
(Hobfoll, Banerjee & Britton, 1994). COR theory takes an approach to
stress wherein the focus is on external demands rather than internal
demands. Intense or prolonged exposure to stressors, such as traffic
congestion, marital disagreements and financial concerns, can contribute to
distress, physiological immune response and deteriorating health (Kohn,
1996). Therefore it is important to understand the process of stress and the
resource mechanisms that enable coping in the face of difficulty.
The two types of stress theory; resource-based and appraisal-based,
have some aspects in common, as well as some fundamental differences.
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Integration of SWB and Stress Theory
Both Lazarus’ conceptualization of stress and the COR theory emphasize
the importance of resources in enabling coping. In appraisal-based theories,
the outcome of a situation as stressful or not stressful is dependent on and
individual’s appraisal of whether or not they have the resources to meet the
demands of the situation. COR theory is more concrete, suggesting that the
fit of personal, social, economic and environmental resources with external
demands determines the direction of stress responding and resultant
outcomes (Hobfoll, 2001). That is, the fit between the situational demand
and the resources determines the level of stress encountered, regardless of
appraisal.
While the cognitive, appraisal-based theory of stress proposed by
Lazarus and folkman (1984) is arguably still the most popular
conceptualization, the COR model will be used for the purposes of this
research for several reasons. Firstly, the COR theory is relatively simplistic.
Stress, or lack thereof, is a product of the relationship or ‘fit’ between a
situation (or demand) and the resources. This model can thus be applied to
groups as well as individuals. The element of appraisal in other theories
adds complexity and abstraction because the process of appraisal is
influenced and confounded by subjectivity and individual differences such
as personality, affect, optimism, perceived control etc. These factors are
specific and measurable resources within the COR theory. Finally, the
principles of COR theory fit well with the SWB homeostasis theory. They
both depict a resource-based model wherein the outcome (be it level of
stress or SWB) is dependent on the ability of resources to manage, buffer
and meet demands.
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Integration of SWB and Stress Theory
13.2
Resources
According to COR Theory (Hobfoll, 1988), resources are objects,
personal characteristics, conditions or energies that are valued by the
individual or group, or a means of attaining these objects, personal
characteristics, conditions or energies. Object resources are items such as
houses and cars, which are valued because of some aspect of their physical
nature, or because they acquire secondary status value based of their rarity
or expense. Conditions are resources to the extent that they are valued and
sought after, such as marriage or seniority. Personal characteristics are
resources to the extent that they can generally aid stress resistance, such as
self-efficacy, self-esteem and optimism. Finally, energies such as time,
money and knowledge, are resources that aid in the acquisition of other
kinds of resources. According to the COR model, resource gain reduces the
vulnerability to immediate loss following a stressful event, and to ensuing
loss that may occur in the wake of initial loss (Ford & Gordon, 1999).
COR theory begins with the basic tenet that people strive to obtain,
retain, protect, and foster those things they most value (Hobfoll, 1988,
1989). Those things that people value are termed resources, and these are
either directly valued (such as home, family and health) or instrumental in
the acquisition of basic valued resources (such as insurance, money or
knowledge) (Hobfoll, Ennis & Kay, 2000).
A fundamental assumption of COR theory is that individuals are
cognitively biased to overestimate the weight of resource loss compared to
gain (Hobfoll, Banerjee & Britton, 1994). Furthermore, COR theory
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Integration of SWB and Stress Theory
predicts that resource loss is the principal ingredient in the stress process.
When chronic stressors (such as poverty) result in the depletion of
resources, people are increasingly vulnerable to negative stress sequealae,
that if ongoing, result in rapid and impactful loss spirals (Hobfoll, 2001).
This occurs because resource loss is stressful in itself, and because people
must invest resources to offset further resource loss, therefore once an initial
loss occurs, people become increasingly vulnerable to ongoing loss. For
instance, poverty, or the inability to meet financial demands, is a significant
stressor that leads to further resource loss, such as decreased self-esteem and
optimism, decreased health through inability to afford adequate health care,
loss of material possessions such as home and vehicles, etc.
Under normal circumstances, when individuals have sufficient
resources to successfully cope with intrinsic and extrinsic stressors, resource
gain is not inversely related to psychological distress, whereas resource loss
is strongly and directly related to distress (Hobfoll, Lilly & Jackson, 1992).
Resource gain does, however, play an important role when loss cycles have
been initiated and the system is attempting to counteract the loss, or halt loss
cycles by making resource gains (Hobfoll, Banerjee & Britton, 1994). Loss
is a consistent negative theme within life event surveys. The major life
events that have been found to be critical to psychological stress are all
profound loss events, including loss of a loved one, loss of health, severe
loss of income, loss of employment, loss of love and loss of freedom
(Hobfoll, Banerjee & Britton, 1994). Therefore, when loss occurs, the
extent to which an individual successfully copes with loss largely depends
on the adequacy of the resources at their disposal.
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Integration of SWB and Stress Theory
13.2.1 Object Resources
Object resources include physical objects such as home, car, jewelry
and clothing. They provide a ‘base of operations’ for coping, and can be
used in a problem-solving manner (Hobfoll, Freedy, Green and Solomon,
1996). Object resources within this theory serve two main functions.
Firstly, they aid in the acquisition of other resources. For example, the
possession of material assets (a big house with good furniture, new car etc)
may enhance confidence, self-esteem and social status. Transportation can
be a key to evacuation or obtaining medical treatment (Hobfoll et al., 1996).
Secondly, object resources can be transformed into other resources.
Material assets can be ‘liquidated’ and turned into energy resources either
through selling or borrowing against the value of the assets.
13.2.2 Personal Characteristic Resources
Personal resources are characteristics or skills that individuals
possess. Key personal resources include job skills, social prowess, selfesteem and sense of personal efficacy (or mastery/control) (Hobfoll, et al.,
1996). Control, self-esteem and optimism are both resources and cognitive
buffers within the SWB Homeostasis theory. As these factors have
previously been discussed in Chapter 3, they will now be briefly explored in
the context of resources.
Control
One of the most widely researched personal resources is that of
control. Control can be broadly defined as the sense that one can affect
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Integration of SWB and Stress Theory
one’s environment with reasonable success (Hobfoll, Banergee & Britton,
1994). Regardless of the conceptualization of control (ie, as mastery, selfefficacy, locus of control, learned helplessness, primary and secondary
control or hardiness), it is an important element within the COR theory, and
in fact “may be a critical construct because it is a managerial resource.”
(Hobfoll, Banergee & Britton, 1994, p44). Individuals high in control are
more likely to use effective behaviours in managing demands (whether
health and lifestyle behaviours, work habits, etc) because they believe that
their behaviour can influence the outcome of their circumstance. People
with a high sense of control are likely to take steps to positively manipulate
the resources within their environment and engage in problem solving
behaviours.
Furthermore, control also has an important stress-buffering role
within the psychological concept of hardiness (Kobassa, Maddi &
Courington, 1981). Individuals with high control cognitively perceive
positive events as controllable, whereas they perceive negative events as
being changeable and unstable. If individuals believe that they can affect
the environment and view negative events as challenges rather than
overwhelming setbacks, they are more likely to rally resources to change
events in their favour.
Self-Esteem
Self-esteem is a key resource within the COR theory, as is viewed
both as an important individual resource, and as a basic building block from
which other resources are established (Hobfoll, Banerjee & Britton, 1994).
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Integration of SWB and Stress Theory
As a resource, high self-esteem enables individuals to cope with stress in a
more productive and problem-solving manner. As a building block to other
resources, individuals with high self-esteem, for instance, may perceive they
have a more supportive social network to go to when in need of assistance.
Low self-esteem is not only implicated in illness, but is also a pivotal
instigator of problems such as eating disorders and self-distructive
behaviours (Hobfoll, Banerjee & Britton, 1994).
Cast and Burke (2002) acknowledge that self-esteem enables people
to view their life positively, and also recognize self-esteem as a personal
resource that can support individuals and ensure that negative emotions do
not become too overwhelming. Self-esteem operates as a type of personal
resource that protects individuals from negative experiences, however, as it
is a resource, self-esteem can be used up in the process, so there are limits to
its ability to buffer stress (Cast & Bourke, 2002).
13.2.3 Condition Resources
Condition resources are the conditions that are intrinsically valued
by people or that facilitate acquisition or protection of valued resources.
These include seniority at work, stable employment, social systems, a good
marriage, and being a member of a family (Hobfoll, et al., 1996). Perceived
social support is one of the most commonly researched resources within this
category, and will be explored in more detail.
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Integration of SWB and Stress Theory
Perceived Social Support
Social support, as it has been typically studied in the health
literature, reflects “those social interactions or relationships that provide
individuals with actual assistance or with a feeling of attachment to a person
or group that is perceived as caring or loving” (Hobfoll & Stokes, 1988,
p499).
Social support is a pivotal resource within the COR theory as it is the
principal vehicle for obtaining resources that are not possessed by the self,
and is basic to the development of self-esteem and a sense of identity
(Hobfoll, Banerjee & Britton, 1994). Perceived social support can act as a
specific resource to cover resources that may be lost or lacking, or it can
help recruit latent resources, for instance, by bolstering self-esteem (Ford &
Gordon, 1999). In this regard, social support facilitates the preservation of
basic resources.
Perceived social support has been linked with both direct and
indirect effects on health. Directly, social support is related to better health
irrespective of stress levels (Cohen & Wills, 1985) by modifying health
behaviour (Cassel, 1976; Thoits, 1983) and enhancing self-esteem and
positive affect (Cohen & Syme, 1985). Indirectly, social support also has a
stress buffering effect, by moderating emotional distress (anxiety,
depression and anger) in response to stress, and thereby affecting
immunological functioning (Cohen & Syme, 1985).
Consistent with the stress-buffering hypothesis, perceived social
support has been shown to have an impact on positive wellbeing for people
under stress. People with greater perceived social support have been found
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Integration of SWB and Stress Theory
to experience less psychological distress during stressful circumstances
(Bansal, Monnier, Hofoll& Stone, 2000) and higher SWB (Abbey, Abramis
& Caplan, 1985; Holahan et al., 1996; Revenson, Schiaffino & Gibofsky,
1991; Schaefer, Coyne & Lazarus, 1981). This resource plays an important
role in coping, in psychological adjustment for individuals with arthritis
(Germano, 1996) and coronary heart disease (Holahan et al., 1996), and it
also enhances recovery and adherence to treatment recommendations
(Revenson et al., 1991).
13.2.4 Energy Resources
Energy resources include money, credit, owed favors, and
knowledge (Hobfoll, et al., 1996). Energy resources facilitate the
attainment of other resources; they are only valued to the extent they allow
access to other resources.
There seems no doubt that wealth is significantly and directly
associated with living a healthier and longer life, as socioeconomic status
consistently predicts morbidity and premature mortality (Quick & Gavin,
2001). It can be argued that the higher rates of morbidity and mortality may
be due to the observed findings that poorer individuals have less access to
quality health care and that there is a link between wealth and poor health
behaviours such as smoking, poor diet and lack of exercise (Quick & Gavin,
2001). However, it is also true that the lower the financial status of the
individual, the more susceptible they are to the stressors of life, both
through vulnerability to resource loss and the lack of resource reserves to
survive such a loss (Quick & Gavin, 2001).
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Integration of SWB and Stress Theory
Although energy resources such as financial security are important
within COR theory, once an individual has enough money to afford a good
standard of living, further personal gain becomes self-serving, and does not
advance one’s ability to cope or increase stress resilience (Quick & Gavin,
2001).
13.3
Summary
In conclusion, the Conservation of Resources Theory (Hobfoll,
1988) is an integrative stress theory that emphasizes the need to accrue,
maintain and mobilize resources in order to cope with the stressors
encountered in daily life. The four categories; object, condition, personal
characteristic and energy resources, allow us to cope with stress either by
using investments to counteract stress, or to gain other resources. However,
prolonged stress will inevitably reduce the resources that can be drawn
upon, leaving the person vulnerable to a breakdown of their coping system.
In a simple analogy, the COR system can be likened to a car: while rough
terrain, multiple passengers (adding extra weight) and time (affecting the
engine parts) act as stressors on the car’s system, breakdown will only occur
when the resources of engine maintenance, petrol, water in the radiator, etc,
have been depleted.
A potential criticism of the COR Theory is that resources are
limitless and as such the theory has circumscribed utility because it is too
general. Hobfoll (1988) created an instrument that lists 74 resources that
community samples identified as important, and while this is extensive, it
would be potentially impossible to devise an exhaustive list of all resources.
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Integration of SWB and Stress Theory
Moreover, the importance of resources (whether small specific resources or
key resources such as self-esteem) may be weighted differently by each
individual, indicating that the impact of resource loss may not be predictable
within a group. Finally, Hobfoll (1988, 2001) has identified several key
resources such as social support, self-esteem, control and money, however
there is no evidence to suggest that these are the only resources that make a
significant contribution to coping and stress. Therefore, the relationship
between resource loss and stress could potentially be underestimated.
While Hobfoll’s (1988) theory is based within the stress literature
and the Subjective Wellbeing Homeostasis Theory (Cummins et al., 2002)
is based within the quality of life literature, there are many parallel ideas
and much shared vision between them. Therefore, the following chapter
explores the common themes between the theories, and argues the utility of
an integrated and symbiotic model that encompasses both frameworks.
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Integration of SWB and Stress Theory
Chapter 14
Integration of Homeostasis and COR Theory
While the Conservation of Resources (Hobfoll, 1988) and SWB
Homeostasis theories have developed from different areas of literature
(stress and quality of life respectively), it is apparent that the underlying
framework and values of the models have much in common.
As a very brief and generalised statement, the COR theory (Hobfoll,
1988) can be said to be the process by which individuals maintain the ability
to cope with stressors by utilising internal and external resources. The
depletion of these resources leads to the breakdown of the coping
mechanism, and hence the experience of stress. In a similar fashion, the
SWB Homeostasis theory (Cummins, Gullone & Lau, 2002) represents the
way in which subjective wellbeing is regulated and maintained through
psychological determinants that buffer wellbeing from external stressors.
Experience of chronic or severe external stressors will eventually overcome
the SWB regulatory system and lead to the breakdown of homeostasis.
A consistent theme in these theories is that individuals are armed
with ‘tools’ that allow for the defence against environmental and lifestyle
stressors, and that when these tools are used up or overcome, the systems
fail. While the specific wording of system failure differs between the
theories, with COR theory referring to the experience of stress as the
outcome, and SWB Homeostasis theory referring to homeostatic failure and
dissatisfaction as the outcome, the consisted theme underlying both is that
individuals cannot sustain fully functional, productive or fulfilling lifestyles
when in conditions of system failure.
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Integration of SWB and Stress Theory
The similarities between the two theories are more profound than the
differences. The differences stem from being borne from different circles of
literature. The COR theory assumes that stress results when environmental
demands placed on an individual exceed their external and internal
resources. This theory is operationalised in an economic manner, whereby
resources are depicted as ‘units’ of resistance that either meet or fall short of
‘units’ of demand, and where resources can be ‘invested’ to offset stressors
or used to acquire other resources.
In contrast, the SWB Homeostasis theory is written from a
psychologically-oriented literature base, and thus focuses on internal
psychological variables and subjective experience. This theory predicts that
the set-point range of SWB experience differs between individuals, and that
this influences how resilient or vulnerable individuals are to external
stressors. The psychological factors that buffer wellbeing from stressors or
provide resilience from negative circumstances are not depicted as ‘units’
that can be used up, but as a regulatory system that can be overcome.
While these differences are minor, the underlying meaning is
consistent. Both theories hold a conceptualisation that individuals rely on
certain internal and external factors that enable them to cope with life and
maintain positive wellbeing. Without these factors, the individual is
vulnerable to the effect of environmental stressors. The theories can be
likened to an analogy of the human immune system: People come in contact
with pathogens routinely in every day life, but we are protected from their
harmful effects by our immune system. When our immune system is
depleted, we are then vulnerable to the viruses and bacteria in our
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Integration of SWB and Stress Theory
environment and may become sick. Similarly, the internal and external
factors or resources that buffer stress and maintain wellbeing can be viewed
as a ‘psychological immune system’.
There is some commonality between the factors that are seen as
pivotal within the COR and SWB Homeostasis theories. The first resource
category within the COR theory is objects, and refers to material
possessions that enhance the quality of life. The influence of these factors is
acknowledged within SWB literature, however they are not incorporated
into the SWB homeostasis theory, as this is a conceptualisation of a system
of psychological mechanisms.
Energy resources include money, credit, owed favours and
knowledge. Few specific factors have been identified within this category,
however household income is a common measure. Income and wealth are
not incorporated within the model of SWB Homeostasis, as these are again
not psychological variables.
Condition resources within the COR theory relate to conditions that
are valued by individuals, and involve status and relationships, such as
being married, having a family, and work seniority. Perceived social
support is the most common measure of condition resources within COR
literature. While perceived social support is known to exert a strong and
predictable influence on SWB, with a consistently demonstrated positive
association between social support and SWB (Holahan et al., 1996;
Revenson et al., 1991; Schaefer et al., 1981), this factor is not
conceptualised within the SWB Homeostasis theory. As the quality of
human relationships is such an important area, it is likely that the SWB
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Integration of SWB and Stress Theory
Homeostasis theory would benefit from the inclusion of perceived social
support measurement.
The resource category of personal characteristics is the most
complex and widely researched of the resource categories, as this involves
the internal, psychologically based factors. Self-esteem and control are two
commonly measured factors within COR literature. These factors, with the
addition of optimism, comprise the cognitive buffers that represent the
second-order determinants of SWB homeostasis. Therefore, there is a
significant overlap of measurement between the theories within this domain.
The incorporation of optimism as a resource into the COR
framework would be a beneficial addition. Optimism correlates highly with
SWB and satisfaction (Scheier & Carver, 1992), and can be expected to
influence the experience of and resilience to stressors. Optimism reflects
the predisposition to perceive events or circumstances as positive or
negative, and is closely linked with perceived control (Scheier & Carver,
1992). Therefore, optimism should not be overlooked with regard to its
influence on stress and coping.
Furthermore, the COR model does not incorporate the stable
individual dispositions of personality or affect. The COR theory is simple
due to its basic conceptualisation of the relationship between resources and
stress (wherein resources – demand = stress level), however the influence of
these individual difference factors on stress cannot be overlooked.
Personality and affect play a significant role in determining other less stable
factors such as optimism, control and self-esteem (Cummins et al., 2002).
Moreover, these factors may also influence the perception of stressors, the
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Integration of SWB and Stress Theory
coping techniques used to deal with stressors (Scheier, et al., 1986), and the
likelihood of experiencing stressors or negative life circumstances (Lu,
1999). More research is needed in this area.
An integrated model of the Conservation of Resources and SWB
Homeostasis theories is proposed in Figure 12. This model comprises 1st
order, 2nd order and 3rd order determinants, which are influenced by
environmental stressors and give rise to the experience of stress and SWB.
Environmental Stressors
1st order
determinants
External Resources
Personality &
Affectivity
Internal Resources
2nd order
determinants
Cognitive Buffers
Personal
Characteristic
Resources
SWB
Condition
Resources
Energy and
Object Resources
3rd order determinants
Environmental Stressors
Figure 12 - Integrated Model of Homeostasis and COR Theory:
Homeostasis Regulation
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Integration of SWB and Stress Theory
Within this model, 1st order determinants are depicted as those stable
and predetermined individual difference characteristics. Second order
determinants are internal psychological variables, and 3rd order determinants
are external objective factors. This integrated model retains the 1st order
determinants of personality and affectivity, which were not represented
within the COR model. The 2nd order determinants comprise the cognitive
buffers of the SWB Homeostasis model and the personal characteristic and
condition resources of the COR model. Finally, the 3rd order determinants
represent objective resources, and comprise energy and object resources.
The 1st order determinants are hypothesised to influence the less
stable cognitive resources of optimism, control, self-esteem and perceived
social support. An hypothesised relationship exists between the 2nd order
internal resources and 3rd order external resources, in that the possession or
lack of 3rd order external resources hypothetically impacts on self-esteem
etc, and being optimistic or high in control hypothetically influences the
wealth and material possessions that are obtainable. Environmental
stressors or demands represent strain placed on the 2nd and 3rd order
systems. This strain can be absorbed or buffered by internal and external
resources. Finally, the ability of the 2nd and 3rd order determinants to
process environmental stressors gives rise to, and is reflected in, the
experienced level of subjective wellbeing.
The system depicted in Figure 12 describes homeostasis regulation,
where the environmental stressors impinged on the system are effectively
managed, and subjective wellbeing is maintained at the set-point level. In
Figure 13, homeostasis failure is described, wherein extreme or chronic
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Integration of SWB and Stress Theory
environmental stressors are net effectively managed, the homeostatic
mechanisms fail, and stress is experienced, resulting in SWB loss and the
potential psychological consequence of depression.
Figure 13 Integrated Model of Homeostasis and COR Theory:
Homeostasis Failure
Two examples will be given to illustrate the hypothesised
mechanisms of the integrated model. Firstly, a woman who is reasonably
high in extraversion and positive affect (1st order determinants) has a car
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Integration of SWB and Stress Theory
accident and damages her vehicle (environmental stressor). She has
sufficient money (energy resource) to cover the expense and is
comprehensively insured (condition resource). As no-one was hurt, she
remains fairly optimistic, however her sense of control is decreased
(cognitive buffers). The environmental stressor is processed by the system,
she suffers minimal stress, and retains a high subjective wellbeing.
Alternatively, a man who is relatively high in neuroticism and
negative affect (1st order determinants) loses his job (environmental
stressor). The man becomes financially unstable (energy resources) and he
becomes pessimistic and argumentative with family, and he progressively
believes he is a worthless person. The system cannot absorb the impact of
the environmental strain, and he experiences significant stress. The man’s
subjective wellbeing is lowered due to homeostatic failure, and after a
period of time, he becomes clinically depressed.
The integrated model draws from both theories, and can be expected
to explain more thoroughly the process of SWB. This model incorporates
additional resource factors that are not represented within the SWB
homeostasis theory. Moreover, the model expands on SWB homeostasis by
exploring the process of homeostatic failure, through weighting internal and
external resources with environmental stressors. A model in which factors
associated with SWB homeostasis and COR are integrated is expected to
better explain and predict the experience of stress and SWB. Furthermore,
it can be argued that stress and SWB are fundamentally linked concepts that
should be measured concurrently. After all, it is the demands and stress of
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Integration of SWB and Stress Theory
life, and the way in which we cope with these, that determines our
satisfaction and happiness with life.
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Integration of SWB and Stress Theory
Chapter 15
Study 3 Methodology
15.1
Aim
The first study covered in Section One aimed to test the relationship
between subjective wellbeing and the psychological variables of depression,
anxiety and personality in a current Australian sample. This first study also
aimed to test for evidence that subjective wellbeing levels are maintained by
a homeostatic mechanism.
In this third study, the theories of subjective quality of life and stress
(COR theory) have been integrated with the aim of discovering how the
regulation of subjective wellbeing works. The study aims to identify the
relationship between subjective wellbeing, psychological variables such as
personality, affect, optimism, control and self-esteem, and psychological
variables of ill-health, depression, anxiety and stress.
Previous research, along with the results of study 1, has found a
strong relationship between SWB and neuroticism (Costa & McCrae, 1980;
Lu & Shih, 1997), and between SWB and depression (Barge-Schaapvels et
al., 1999; Hansson, 2002). Therefore, it is expected that these factors will
play an important role in the prediction of SWB levels. Prior research also
suggests that SWB has a positive relationship with extraversion (Costa &
McCrae, 1980; Herringer, 1998), positive affect (Diener, 1984), optimism
(Aspinwall & Taylor, 1992), control (Taylor & Brown, 1988) and selfesteem (Lucas et al., 1996), and a negative relationship with negative affect
(Diener, 1984), anxiety (Headey & Wearing, 1989) and stress. While these
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Integration of SWB and Stress Theory
relationships are both well-researched and intuitive, it is not known which
are the stronger predictors when analysed together.
This study also aims to test the relationship between the subscales of
the new affect scale, devised in Section Two of this thesis, and SWB, and
the ability of this scale to predict Personal Wellbeing Index scores.
In this study a model is presented that conceptualises and maps the
process of subjective wellbeing and the role of each of the aforementioned
components. It is hoped that, when analysed together, the process of SWB
homeostasis can be better understood through acknowledging the
contribution of the internal and external resources and psychological factors
analysed in this study.
The scales used include the Personal Wellbeing Index (Cummins et
al., 1993), the neuroticism and extraversion subscales of the NEO Five
Factor Inventory (Costa & McCrae, 1992), the Life Orientation Test
(Scheier & Carver, 1985), Rosenberg’s Self-esteem Scale (Rosenberg,
1965), Perceived Control of Internal States Scale (Pallant, 2000), the
Depression, Anxiety and Stress Scale (Lovibond & Lovibond, 1995) and
affect subscales (See section two).
15.2
Research Hypotheses
The hypotheses for study 1 were:
1. That correlation and regression analyses would yield similar
results as those found in study 1, with neuroticism and
depression being the strongest predictors of PWI scores
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Integration of SWB and Stress Theory
2. That there is a significant difference in personality, affect,
optimism, control, self-esteem, depression, anxiety and stress
scores between individuals with normal and sub-average
levels of subjective wellbeing
3. That there is evidence of a curvilinear relationship between
PWI scores and depression scores
4. That the psychological components of personality, affect,
optimism, control, self-esteem and stress can be modelled
and tested as a conceptualisation of an integrated subjective
wellbeing and stress theory using structural equation
modelling.
15.3
Method
15.3.1 Materials
Two questionnaires were compiled for Study 3. As the inventories
used in the study were so extensive, it was necessary to divide the
inventories into two questionnaires, so as to reduce participant fatigue and
increase response rate. Each questionnaire contained only 4-5 inventories.
Both questionnaires contained the Personal Wellbeing Index and the affect
items, and the other inventories were split between the two.
Questionnaire 1 comprised the:

Personal Wellbeing Index

Affect Measure

NEO-FFI,

Life Orientation Test
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Integration of SWB and Stress Theory

Perceived Control of Internal States Scale,
Questionnaire 2 comprised the:

Personal Wellbeing Index

Affect Measure

Depression Anxiety Stress Scale and

Rosenberg’s Self Esteem Scale.
Questionnaires 1 and 2 are included in Appendix C and D
respectively. The questionnaires were mailed to the residential addresses of
each participant. Consent was assumed on return of the questionnaire.
Detailed descriptions of the instruments, reliability statistics and rationale
for the use of each instrument are reported below.
15.3.2 Demographic Data
The demographic data for gender, age and income were collected by
matching the identification code with the information on the Australian
Unity database that had been collected from the original telephone
interview. Specific details relating to the demographic data are reported in
the following data analysis chapter.
15.3.3 Australian Unity Wellbeing Index
The Australian Unity Wellbeing Index (Cummins et al., 2003),
including the National Wellbeing Index and the Personal Wellbeing Index
(PWI), were again used in this study. Refer to Chapter 6.3.3 for details of
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Integration of SWB and Stress Theory
these measures. As in Study 1, only the PWI results were analysed for the
purposes of this research.
15.3.4 NEO-Five Factor Inventory (NEO-FFI)
The Neuroticism and Extraversion subscales of the NEO-FFI (Costa
& McCrae, 1992) were used in this study. See Chapter 6.3.5 for details on
this inventory.
15.3.5 Affect Scale
A three-factor affect scale was constructed, based on the 4Dimensional Mood Scale (Huelsman, Nemanick & Munz, 1998), in Section
Two of this thesis. The scale comprises the three subscales; high activation
negative affect, high activation positive affect, and low activation positive
affect. The details of the scale and its construction are outlined in Chapter
11.4. The subscale reliability was high, ranging from alpha levels of .88 to
.89, and the scale explained 69% of variance in affect.
15.3.6 Life Orientation Test-Revised (LOT-R)
The Life Orientation Test-Revised (Scheier & Carver, 1985) was
designed to measure the extent to which individuals possess favourable
expectation concerning life outcomes (e.g. “In uncertain times I usually
expect the best”). In this study, responses for the 6 items are given using a
5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree).
Scores can range from 6 to 30, with higher scores indicating higher levels of
optimism. The reliability of the LOT-R is satisfactory (Cronbach’s alpha =
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Integration of SWB and Stress Theory
.78), and test-retest correlations range from .56 to .79 for periods from 4
months to two years (Scheier, Carver & Bridges, 1994). Scores on the
LOT-R have been shown to correlate positively with internal control beliefs
and self-esteem, and negatively with depression, hopelessness, and
perceived stress (Scheier & Carver, 1985, 1987).
15.3.7 Self-Esteem Scale (SES)
In the Self-Esteem Scale (Rosenberg, 1965), which is designed to
measure global self-esteem, individuals are required to respond to a series of
10 statements (e.g., “On the whole I am satisfied with myself”) using a 4point response format ranging from 1 (strongly disagree) to 4 (strongly
agree). Scores range from 10 to 40, with higher scores representing higher
self-esteem. This scale has been used extensively and has been shown to
have adequate internal consistency (Cronbach’s alphas =.77 and .88) and
test-retest reliability (r=.82).
15.3.8 Perceived Control of Internal States Scale (PCOISS:12)
The Perceived Control of Internal States Scale (Pallant, 2000) is a
12-item inventory designed to provide a measure of participants’
perceptions of their ability to influence their internal states and moderate the
impact of aversive events on their emotions, thoughts, and physical
wellbeing. The scale uses a 5-point Likert scale ranging from 1 (strongly
disagree) to 5 (strongly agree). Internal consistency for this scale is good
(Cronbach’s alpha = .92), and construct validity is high (r=.60).
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Integration of SWB and Stress Theory
15.3.9 Depression Anxiety Stress Scale (DASS-21)
The DASS-21 (Lovibond & Lovibond, 1995) comprises 21 items
measuring the three separate components of depression, anxiety and stress.
Cronbach’s alphas for the DASS Depression, Anxiety and Stress subscales
were .94, .87 and .91 respectively. Concurrent validity of the DASS-21
with other depression and anxiety inventories were high, ranging from r=.79
to .85 (Antony et al., 1998).
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Integration of SWB and Stress Theory
Chapter 16
Study 3 Data Analysis
The data for Study 3 originated from one mail-out, which took place
in February of 2003. The questionnaires were sent to 1569 participants on
the Australian Unity database. Questionnaire 1 was completed by 214
participants, and questionnaire 2 was completed by 259 participants.
Together, the PWI and affect inventories were completed by 473 people.
The data were screened using the FREQUENCIES option in SPSS
before commencing analysis. Eight cases were deleted due to the
dichotomous nature of the data. These cases had scores of perfect 10 for all
of the satisfaction domains, and had the highest or lowest possible scores for
most of the remaining items of the questionnaire.
The seven personal domain scores of the Personal Wellbeing Index
(PWI) and composite PWI scores were converted to percent scale maximum
scores (%SM) by creating syntax to compute the score of each variable
multiplied by 10. For example, the syntax read: COMPUTE sma1eco =
a1eco * 10. This procedure re-coded the data from 0 to 100.
Several NEO Five-Factor Inventory items were recoded due to
reverse scoring. Details of this procedure are presented in Chapter 8. Using
the RECODE command of SPSS, items 3, 5, 7, 9 and 10 of the Self-Esteem
Scale, 2, 4, and 6 of the Life Orientation Test, and 1, 5, 7 and 10 of the
Perceived Control Scale were reverse scored. Total scores were then
computed. Items 1, 6, 8, 11, 12, 14, and 18 of the DASS were added and
multiplied by 2 to produce the DASS Stress score; Items 2, 4, 7, 9, 15, 19
and 20 of the DASS were added and multiplied by 2 to produce the DASS
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Integration of SWB and Stress Theory
Anxiety total score, and; Items 3, 5, 10, 13, 16, 17, and 21 of the DASS
were added and multiplied by 2 to produce the DASS Depression score,
giving each subscale a range of 0-42. Total Self-Esteem scores were
computed by adding all items, giving a possible range of 10-40. Total Life
Orientation Test scores were computed by adding all items, giving a range
of 6-30. Finally, total Perceived Control Scale scores were computed by
adding all items and giving a possible range of 12-120.
16.1
Assumptions of Normality Testing
16.1.1 Demographic Data
The annual household income of the 277 participants from the Data
Set who answered this item was categorised into the five income groups
used in Study 1 as previously described. The distribution of income was
normal on inspection of the histogram and both normal and detrended plots,
with the average income being between $31,000 and $60,000. The
significance of skew and kurtosis statistics were within acceptable ranges,
suggesting the income variable meets the assumption of normality.
The proportion of males to females (N=469) was approximately
even, with 203 males (43.3%) and 266 females (56.7%). Age ranged from
18 to 88 years (M=52, SD=15). The skewness statistics for the gender and
age variables were within the acceptable range of –4 to +4, and the
histograms show a roughly normal distribution pattern, indicating that these
variables meet the assumption of normality.
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Integration of SWB and Stress Theory
16.1.2 Personal Wellbeing Index
All seven of the PWI domains displayed significant positive
skewness. The ‘Satisfaction with Personal Relationships’ variable had the
most non-normal curve, with negative skew and positive kurtosis (S=-10.62,
K=7.47, M = 76.9%SM). The other six PWI variables had no significant
kurtosis, and ranged in degree of skew from –5.76 (future security) to –9.10
(safety). The Personal Wellbeing Index (%SM) score was also negatively
skewed and slightly leptokurtic (S=-8.53, K=5.52). The overall %SM score
for the variable ‘How Satisfied are you with your Life as a Whole’ was also
negatively skewed and leptokurtic (S=-9.36, K=7.19).
On inspection of the boxplots, the variables Satisfaction with Health,
Achievements in Life, Personal Relationships, Community Connectedness,
Satisfaction with Life as a Whole and %SM Personal Wellbeing Index score
all contained at least one extreme outlier. On inspection of the z-scores, the
following variables contained standardised values in excess of 3.29:
Material Standard of Living (three z values=-3.66), Health (z=-3.43),
Productivity (two z values=-3.31, two z values =3.86), Personal
Relationships (z=-3.79), Safety (z=-3.73), Community Connectedness (four
z values=-3.34), %SM PWI scores (z=-3.73, z=-3.32), and Life as a Whole
(z=-3.56, two z values = 4.14). While these extreme outliers influence the
normality of the data, these cases are retained without transformation, as
cases of sub-average satisfaction are a central focus in this research
investigation.
Table 13 contains the mean and standard deviation statistics of the
Personal Wellbeing Index variables.
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Integration of SWB and Stress Theory
Table 13 - Mean and Standard Deviation of PWI variables
N=473
M
SD
Standard of Living
72.41
20.13
Health
69.77
20.69
Achievements in Life
71.11
18.56
Personal Relationships
76.91
20.58
Safety
74.19
20.20
Community Connectedness
70.25
21.34
Future Security
66.65
22.87
Personal Wellbeing Index
71.67
14.80
Life as a Whole
72.41
17.94
All statistics calculated from the Scale Maximum PWI scores
16.1.3 NEO Five-Factor Inventory
The NEO-FFI variables were expected to produce similar results in
normality testing as were found from Study 1, with the majority of the 24
NEO variables producing normal distributions.
The 12 Neuroticism items and 12 Extraversion items were once
again added together to form ‘Total Neuroticism’ and ‘Total Extraversion’
scores respectively. The significance of skew and kurtosis statistics were
acceptable for both sets, the z-scores were acceptable, the boxplots revealed
no extreme outliers, and the normal and detrended pots were normal. In
addition, the Kolmogorov-Smirnov statistics were non-significant for these
variables. Therefore, the total neuroticism and total extraversion variables
meet the assumptions of normality.
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Integration of SWB and Stress Theory
16.1.4 Depression Anxiety Stress Scale (DASS)
The Depression, Stress and Anxiety subscales of the DASS each
comprise seven items, which are added and multiplied by two, giving a
possible score range of 0-42. As the inventory measures clinical symptoms
of negative psychological states, the normality testing was expected to
produce non-normal results as the majority of the population do not
experience these psychological states to a clinical degree.
The mean Total Depression subscale score was 5.60 (N=258,
SD=6.94), with the scores ranging from 0 to 38. The mean Total
Depression score was significantly positively skewed and leptokurtic
(S=15.03, K=20.73), indicating that the majority of the population have very
low levels of depression with a slight minority of the population having high
levels of depression symptomatology.
The mean Total Anxiety subscale score was 3.36 (N=258, SD=4.58),
with the scores ranging from 0 to 34. The mean Total Anxiety score was
also significantly positively skewed and leptokurtic (S=19.06, K=39.45),
indicating that the majority of the population have very low levels of
anxiety.
The mean Total Stress subscale score was 9.22 (N=257, SD=7.26),
with the scores ranging from 0 to 38. The mean Total Stress score was only
slightly positively skewed and leptokurtic (S=6.82, K=5.36), indicating that
the majority of the population have low levels of stress, however total stress
scores fall within a more varied and bell-shaped pattern than depression or
anxiety.
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Integration of SWB and Stress Theory
While all of these variables were negatively skewed and leptokurtic,
none were transformed as this would alter the interpretability and meaning
of subsequent results.
16.1.5 Self-Esteem Scale
The Self-Esteem Scale comprises ten items which are added to give
a possible score range of 10-40. The mean total Self-Esteem score was
32.01 (N=259, SD=4.94). The scores ranged from 17-40, and the histogram
revealed a roughly bell-shaped distribution of individual scores. There was
no significant skew or kurtosis, and no extreme outliers or z scores in excess
of 3.29. Therefore, the total Self-Esteem score meets the criteria of
normality.
16.1.6 Life Orientation Test (LOT)
The Life Orientation Test is a measure of optimism, and is
comprised of six items which are added to give a possible score range of 630. The mean total LOT score was 16.51 (N=214, SD=3.63). The scores
ranged from 6-22, and the histogram revealed a roughly bell-shaped
distribution of individual scores. The results were slightly negatively
skewed (S=-6.28), with no significant kurtosis. The normal and detrended
plots showed slight abnormality of scores, and several outlying scores were
present on the box plot. While these results displayed some abnormality of
LOT score distribution, there were no extreme outliers according to z scores
in excess of 3.29.
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Integration of SWB and Stress Theory
16.1.7 Perceived Control of Internal States Scale (PCOISS)
The Perceived Control of Internal States Scale comprises twelve
items which are added to give a possible score range of 12-120. The mean
total PCOISS score was 77.33 (N=214, SD=16.45). The scores ranged from
26-116, and the histogram revealed a bell-shaped distribution of individual
scores. There was no significant skew or kurtosis, acceptable normal and
detrended plots, and no extreme outliers or z scores in excess of 3.29.
Therefore, the total PCOISS score meets the criteria of normality.
16.2
Exploring Relationships
16.2.1 Pearson Product-Moment Correlation
One of the aims of the study was to investigate the relationship
between subjective wellbeing and the psychological variables of personality,
depression and anxiety, and to discover whether the results found are similar
to that of Study 1. These relationships were investigated using Pearson
product-moment correlation coefficient. Preliminary analyses of scatter
plots were performed to ensure no violation of the assumptions of
normality, linearity and homoscedasticity.
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Integration of SWB and Stress Theory
Table 14 - Pearson Product-Moment Correlations Between Measures of
PWI and Neuroticism, Extraversion, Anxiety and Depression
PWI Neuroticism Extraversion Anxiety Depression
1
-.42**
.37**
-.45**
-.59**
PWI
(N)
(458)
(212)
(212)
(246)
(246)
-.465**
a.
a.
Neuroticism
N
211
a.
a.
Extraversion
N
.65**
Anxiety
N
248
71.67
42.16
69.36
3.34
5.60
Mean
14.80
21.53
16.95
4.54
6.94
Standard
Deviation
**
Correlation is significant at the .01 level
a. Could not calculate correlations as these inventories were placed in different
questionnaires
In study 1 the highest correlation occurred between neuroticism and
anxiety. Unfortunately, these variables could not be correlated in this study
as these inventories were placed in different questionnaires, so they did not
have a common data set. The highest correlation that could be observed in
this study occurred between anxiety and depression (r=.65, p=.01), followed
by that between PWI and depression, r=-.59, p=.01.
As study 3 contained several different variables that were not tested
in study 1, further correlations were produced. Two correlation matrices are
presented below (Tables 15 and 16) to correspond with the inventories
encapsulated in questionnaires 1 and 2.
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Integration of SWB and Stress Theory
Table 15 - Pearson Product-Moment Correlations Between Measures of
PWI and Affect, and Neuroticism, Extraversion, Optimism and Control
1
2
3
4
5
6
7
8
1
-.53**
.57**
.60**
-.42**
.37**
-.21**
.30**
-.35**
-.52**
.71**
-.32**
.19**
-.42
.65**
-.49**
.55**
-.24**
.42**
-.51**
.32**
-.10
.46**
5 Neu
-.47**
.24**
-.54**
6 Ext
1
-.21**
.35**
1 PWI
2 High NA
3 High PA
4 Low PA
-.05
7 Optimism
8 Control
Mean
71.67
13.99
36.93
31.55
42.16
69.36 16.49
77.49
St. Dev.
14.80
10.96
11.20
9.58
21.53
16.95 3.64
16.58
**
Correlation is significant at the .01 level
In the first correlation matrix (Table 15) that was generated from the
inventories in questionnaire 1, the highest correlation between all eight
variables occurred between neuroticism and high activation negative affect,
r= .71, p=<.01, followed by that between high activation positive affect and
low activation positive affect, r= .65, p=<.01. Of the variables included in
this matrix, the variable that had the strongest correlation with PWI was low
activation positive affect.
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Integration of SWB and Stress Theory
Table 16 - Pearson Product-Moment Correlations Between Measures of
PWI and Affect, and Depression, Anxiety, Stress, and Self-Esteem
1
1 PWI
2
3
4
5
6
7
8
-.53**
.57**
.60**
-.59**
-.45**
-.47**
.58**
-.35**
-.52**
.53**
.41**
.52**
-.56**
.65**
-.51**
-.29**
-.29**
.56**
-.58**
-.40**
-.50**
-.10
.65**
.64**
-.68**
.63**
.50**
2 High NA
3 High PA
4 Low PA
5 Dep
6 Anxiety
-.46**
7 Stress
8 S-E
Mean
71.67
13.99
36.93
31.55
5.60
3.34
9.05
32.10
St. Dev.
14.80
10.96
11.20
9.58
6.94
4.54
7.28
4.94
** Correlation is significant at the .01 level
In the second correlation matrix (Table 16) that was generated from
the inventories in questionnaire 2, the highest correlation between the eight
variables occurred between depression and self-esteem, r=-.68, p=<.01,
followed by that between anxiety and depression, and high activation and
low activation positive affect, r=.65, p=<.01. Of the variables included in
this matrix, the highest correlate of PWI was again low activation positive
affect (r=.60), closely followed by the correlation with depression (r=.59)
and self-esteem (r=.58).
A comparison of the same correlation coefficients by gender, age
and income were performed in order to investigate the possibility of
differences across demographic variables. This was done using partial
correlations in SPSS. None of the correlation coefficients were found to be
207
Integration of SWB and Stress Theory
statistically different, indicating there were no gender, age or income
differences.
16.2.2 Standard Multiple Regression
An aim of this study was to test whether data analysis would result
in similar outcomes as were found in Study 1. That is, depression,
extraversion and neuroticism are strong predictors of PWI scores. This
finding will be re-tested with the addition of the other variables in this
current study. As there were a total of seven inventories (PWI, affect
inventory, NEO-FFI, LOT, PCOISS, DASS, and Self-Esteem) that were
divided over two questionnaires, two separate regression analyses were
conducted in order to evaluate the predictive ability of all variables.
The first standard multiple regression was performed between
Personal Wellbeing Index scores as the dependent variable, and
Neuroticism, Extraversion, Control, Optimism, High activation Negative
Affect (H-NA), High activation Positive Affect (H-PA) and Low Activation
Positive Affect (L-PA) as independent variables. Analysis was performed
using SPSS REGRESSION and SPSS FREQUENCIES for evaluation of
assumptions.
With at least 213 respondents in the data set and seven IVs, the
number of cases is well above the minimum requirement of 111 (104 + 7,
Tabachnick & Fidell, 2001, p117) for testing individual predictors in
standard multiple regressions. The standardised residual values scatterplots
were examined, and revealed one outlier. This outlying case was
investigated and deemed valid, so was not deleted from the data set. The
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Integration of SWB and Stress Theory
normal regression plots showed no deviations from linearity, and the
residual values on the scatterplot were distributed in a central and roughly
rectangular fashion, indicating that the assumptions of normality,
homoscedasticity and independence of residuals were met. Multivariate
outliers were sought using the IVs as a part of an SPSS REGRESSION run
in which the Malhalanobis distance of each case to the centroid of all cases
is computed. Malhalanobis distance is distributed as a chi-square (χ2)
variable, with degrees of freedom equal to the number of IVs. To determine
which cases were multivariate outliers, the critical χ2 was calculated at the
most stringent alpha level for seven degrees of freedom (χ2 at α=.001 for 7df
= 24.32). No cases were identified as multivariate outliers.
The correlations between the variables in the regression model all
show a substantial relationship (at least .3) with the dependent variable,
Personal Wellbeing Index scores (See Table 14). The collinearity tolerance
diagnostics are quite respectable (ranging from .37 to .88), indicating that
the assumption of multicolinearity has been met.
Table 17 displays the unstandardized regression coefficients (B) and
intercept, the standardized regression coefficients (β) and R2. The R2 for
regression was significantly different from zero, F(7, 188) = 26.74, p<.001.
For the regression coefficients that differed significantly from zero, the 95%
confidence limits were calculated.
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Integration of SWB and Stress Theory
Table 17 - Standard Multiple Regression of Extraversion, Neuroticism,
Control, Optimism and Affect on PWI Scores
Variable
PWI
1
2
3
4
5
6
B
β
.06
.07
.10
.14
-.05
-.06
-.25
-.06
-.29
-.37***
.22
.29***
.21
.29***
(DV)
1 Ext
.37
2 Neu
-.42
-.47
3 Control
.29
.35
-.54
4 Opt
-.21
-.21
.24
-.05
5 H-NA
-.53
-.32
.71
-.42
.19
6 H-PA
.57
.55
-.49
.42
-.24
-.35
7 L-PA
.59
.32
-.51
.46
-.10
-.52
***
.65
R2
.50
Adjusted R2
.48
Correlation is significant at the .001 level
Three of the IVs made significant unique contributions to the
prediction of PWI levels, however these did not include Extraversion or
Neuroticism, as was found in Study 1. Therefore, the hypothesis that the
regression analyses would yield similar results as those found in Study 1
was not supported. In this study, high activation negative affectivity is the
strongest predictor of PWI scores, followed by low activation positive affect
and high activation positive affect. While all three of these affect variables
were significant at the p<.001 level, none of the other variables were close
to providing a significant unique contribution to PWI scores. While
Neuroticism did not prove to provide a significant contribution to PWI
scores, this was the fourth strongest predictor of PWI scores after the three
affect variables, β=.14, p=.089.
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Integration of SWB and Stress Theory
A second multiple regression analysis was conducted with the
variables within the other questionnaire, between Personal Wellbeing Index
scores as the dependent variable, and High activation Negative Affect (HNA), High activation Positive Affect (H-PA), Low Activation Positive
Affect (L-PA), Depression, Anxiety, Stress and Self-Esteem as independent
variables. Analysis was performed using SPSS REGRESSION and SPSS
FREQUENCIES for evaluation of assumptions.
The assumptions of normality, homoscedasticity and
multicolinearity were again tested in the same manner as the previous
analysis. As the standardised residual values scatterplots revealed two
outliers and several multivariate outliers, seven cases were deleted from the
data set. After these deletions, all assumptions of normality were met.
Table 18 displays the unstandardized regression coefficients (B) and
intercept, the standardized regression coefficients (β) and R2. The R2 for
regression was significantly different from zero, F(7, 219) = 34.26, p<.001.
For the regression coefficients that differed significantly from zero, the 95%
confidence limits were calculated.
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Integration of SWB and Stress Theory
Table 18 - Standard Multiple Regression of Depression, Anxiety, Stress,
Self-Esteem and Affect on PWI Scores
Variables
PWI
Dep
Anx
Stress S-E
(DV)
H-
H-PA
B
β
-.55
-.20*
-.32
-.08
.17
.08
.27
.09
-.28
-.20***
.25
.20**
.39
.25***
NA
Depression
-.59
Anxiety
-.40
.59
Stress
-.38
.60
.57
Self-Esteem
.55
.63
-.46
-.38
H-NA
-.53
.55
.41
.51
-.52
H-PA
.55
-.46
-.23
-.23
.53
-.30
L-PA
.61
-.51
-.31
-.43
.47
-.49
.61
R2
.56
Adjusted R2
.52
*
Correlation is significant at the .05 level
Correlation is significant at the .01 level
***
Correlation is significant at the .001 level
**
In this analysis, four of the IVs made significant unique
contributions to the prediction of PWI levels. These results are consistent
with that of the previous multiple regression, in that the three affect
variables were found to be the strongest predictors of PWI scores.
Specifically, the low activation positive affect variable was the strongest
predictor of affect, followed by high activation negative affect, high
activation positive affect, and depression. The depression variable was
hypothesised to be the strongest predictor and to thus replicate analysis
results from Study 1, however this hypothesis was again not supported.
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Integration of SWB and Stress Theory
16.3
Comparison of Groups
The second hypothesis of this study states that there is a significant
difference in personality, affect, optimism, control, self-esteem, depression,
anxiety and stress scores between individuals with normal and sub-average
PWI scores. This hypothesis was tested with a one-way between-groups
multivariate analysis of variance.
The independent variable, PWI scores, was divided into two groups
of normal subjective wellbeing (70-100%SM) and sub-average subjective
wellbeing (<69%SM). Preliminary assumption testing was conducted to
check for normality, linearity, univariate and multivariate outliers,
homogeneity of variance-covariance matrices, and multicollinearity, with no
serious violations noted.
As two questionnaires were used in this study, separate Multivariate
Analysis of Variance procedures, relating to the variables in questionnaires
1 and 2 respectively, were conducted.
The results of the MANOVA are displayed in Table 19. A
significant multivariate effect was found. Univariate analyses revealed that
there was a statistically significant difference between individuals with
normal and sub-average subjective wellbeing levels on all variables.
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Integration of SWB and Stress Theory
Table 19
Summary of MANOVA for differences between Two
Groups of PWI Scores and Affect, Optimism, Control, Neuroticism and
Extraversion
Multivariate
Approx. F
Hyp. DF
Error DF
Test (Wilks’ Lambda)
12.83***
7
179
F-test with
Univariate:
DF (1,185)
Normal PWI
Sub-average
(n=129)
(n=58)
Variable
F
2
Power
M
SD
M
SD
Low PA
46.05***
.20
1.0
68.60
17.15
50.21
17.14
High PA
52.62***
.22
1.0
68.06
15.43
50.60
14.75
High NA
60.74***
.25
1.0
18.54
14.72
38.33
18.76
Opt
38.38***
.17
1.0
23.04
3.75
19.40
3.71
Control
15.05***
.08
0.9
80.46
15.18
70.40
18.88
Neu
37.34***
.17
1.0
36.70
18.08
55.90
23.41
Ext
15.20***
.08
0.9
72.70
17.19
62.48
15.09
***
Correlation is significant at the .001 level
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Integration of SWB and Stress Theory
Table 20
Summary of MANOVA for differences between Two
Groups of PWI Scores and Affect, Stress, Anxiety, Depression and SelfEsteem
Multivariate
Approx. F
Test (Wilks’ Lambda)
19.37
F-test with
Univariate:
Hyp. DF
Error DF
7
204
***
DF (1,210)
Normal PWI
Sub-average
(n=129)
(n=58)
Variable
F
2
Power
M
SD
M
SD
Low PA
74.99***
.26
1.0
70.81
15.51
50.81
17.79
High PA
58.02***
.22
1.0
67.70
16.32
49.48
18.04
High NA
57.86***
.22
1.0
15.20
14.17
32.24
18.36
Stress
38.08***
.15
1.0
6.94
5.35
12.71
5.37
Anxiety
52.48***
.20
1.0
1.69
2.19
5.95
6.09
Dep
49.34***
.19
1.0
3.11
4.15
9.31
8.58
S-E
56.68***
.21
1.0
33.85
3.94
29.14
5.14
***
Correlation is significant at the .001 level
An inspection of the mean scores indicated that people with subaverage levels of subjective wellbeing reported lower high and low
activation positive affect, control, extraversion, optimism and self-esteem,
and higher levels of high activation negative affect, neuroticism, stress,
anxiety and depression.
16.4
Testing the Homeostatic Regulation of SWB – The
Relationship Between SWB and Depression
The third hypothesis stated that there is evidence of a curvilinear
relationship between PWI and depression scores. If this hypothesis were
supported, SWB levels should remain stable with increasing depression
215
Integration of SWB and Stress Theory
scores, until a certain level of depression is reached (signifying homeostatic
breakdown), whereupon SWB levels would sharply drop.
This hypothesis was tested using the same procedure that was used
in Study 1. This hypothesis was tested by splitting the participant’s
depression scores into groups, then calculating the mean PWI scores for
each depression group, and graphing these PWI scores. The depression
scores as measured by the DASS ranged from 0-26. Depression groups
were formed on the basis of incrementing scores of 2 (see Table 21). The
four highest depression groups (20, 22, 24 and 26) were combined to
increase reliability due to the low numbers in these groups. The PWI means
for each depression group were then calculated using SPSS COMPARE
MEANS command, and these values were plotted graphically, as seen in
Figure 14.
Table 21
Means and Standard Deviations of PWI Scores of Ten
Groups of Increasing Depression Scores
Depression
Group
0
2
4
6
8
10
12
14
16
20-26
Mean
N
80.65 55
74.47 55
70.14 40
71.77 25
66.86 15
61.55 12
54.03 11
53.02
7
46.67
3
45.24
6
Total N
Std.
Deviation
9.22
9.44
11.53
12.69
14.26
19.11
10.44
10.01
20.52
17.67
229
216
Integration of SWB and Stress Theory
Test of Curvilinear Relationship Between
PWI and Depression Scores
Normal
85
80
75
70
65
60
55
50
45
40
Mild
Moderate
Severe
SWB
Mean PWI Scores
r2 = 0.973
0
2
4
6
Poly. (Mean PWI
Scores)
8 10 12 14 16 18 20 22 24
Depression
Figure 14 Mean PWI Scores for Depression in Incrementing Groups of
Ten
Figure 14 displays evidence of a curvilinear relationship between
PWI and depression scores. A 3rd order polynomial trend line was
computed. This trend line suggests the curvilinear model explains a large
proportion of the mean PWI score variance, r2 = .97, and provided a better
fit to the data points than a linear model, r2 = .93 (linear trend line not
pictured in Figure 14). As the curvilinear model fits the data only slightly
better than the linear model, the relationship between SWB and depression
illustrated in Figure 14 should be considered essentially linear.
A plateau occurs at the 70%SM level, whereby PWI scores remain
stable with increasing depression scores. Further increases in depression
precipitate a steeper drop in PWI scores.
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Integration of SWB and Stress Theory
SWB
Test of Curvilinear Relationship Between
Normal and Low PWI Groups and Depression
Normal
85
80
75
70
65
60
55
50
45
40
Mild
Moderate
Severe
R2 = 1
Normal PWI
Low PWI
Poly. (Low PWI)
R2 = 0.979
0
2
4
6
Poly. (Normal PWI)
8 10 12 14 16 18 20 22 24
Depression
Figure 15 Mean PWI Scores for Depression Groups: Comparison of
Normal and Sub-Average PWI Groups
In Figure 15, the data points have been split at the hypothesised
lower threshold point of 70%SM, thereby giving two groups of normal PWI
scores and sub-average PWI scores. Third order polynomial trend lines
were then added to each series of data points. This graph illustrates that a
3rd order curvilinear model perfectly fits the data within the normal PWI
score group, r2 = 1, and provides a very good fit to the sub-average PWI
score group, r2 = .098. Moreover, the curvilinear model is better able to fit
the observed mean PWI scores when the normal and sub-average groups are
separated than when applied to the PWI scores as a whole.
The categories of depression severity according to the DASS manual
(Lovibond & Lovibond, 1995), including normal, mild, moderate, and
severe are indicated in Figure 15. The graphs (Figures 14 and 15) indicate
that SWB is stable until depression scores approach the mild category,
218
Integration of SWB and Stress Theory
where increasing depression scores are contingent upon rapid SWB decline.
The graphs also provide evidence for the hypothesised lower SWB
threshold of 70%SM, whereby no depression symptomatology is evident
within the normal range of SWB, but is evident within the sub-average
range of SWB.
16.5
Structural Equation Modelling
Structural Equation Modelling (SEM) using AMOS 4 (Arbuckle &
Wothke, 1999) was used to test the hypothesised relationship among
psychological predictor variables and endogenous variables within the
Integrated Model of SWB Homeostasis and COR Theory (Figure 12).
As the Study 3 results are based on two separate questionnaires (due
to the number of inventories used within the study), two models were
generated in order to test the hypothesis. The first model tested the
hypothesised relationship between the predictor variables (neuroticism,
extraversion, high activation negative affect, high activation positive affect,
low activation positive affect, optimism and control), and the two
endogenous variables of PWI scores and PWI in two groups of normal and
sub-average scores, as shown in Figure 16.
219
Integration of SWB and Stress Theory
AMOS Hypothesised Model 1
Optimism
Ext
High NA
Psych
Wellbeing
2nd Order
1st Order
Neu
Control
High PA
Low PA
PWB
PWI group
Figure 16 Integrated Model of SWB Homeostasis and COR Theory:
Hypothesised Model 1
Maximum Likelihood (ML) estimation method was used to analyse
the covariance matrix of the model, as this is the most frequently used
estimation method in SEM (Ullman, 2000). A range of the typically
reported fit indices, including two of the most frequently reported fit
indices, the Comparative Fit Index (CFI) and the Root Mean Square
Residual (RMSEA), were examined to determine the adequacy of the
model. The other examined indices were the maximum likelihood chisquare test, the Goodness-Of-Fit Index (GFI), the Adjusted Goodness-Of-Fit
Index (AGFI), the Normed Fit Index (NFI), the Incremental Fit Index (IFI),
and the Expected Cross Validation Index (ECVI).
The hypothesised structural model was found not to provide a good
fit to the data. In order to improve the fit of the model, SEM was used as a
220
Integration of SWB and Stress Theory
more exploratory and model-building technique (Ullman, 2000). An
inspection of the t-tests for regression weights indicated that all variables in
the model make a significant unique contribution to the model. An
examination of the modification indices indicated that the model could be
improved by the addition of further statistically significant pathways.
Examinations of the MI for model 1 indicated that it would be
improved by the addition of five pathways. The added pathways included a
direct effect from high activation positive affect (High PA) to low activation
positive affect (Low PA); high activation negative affect to extraversion,
high activation negative affect (High NA) to neuroticism, and from control
to the 1st order determinants construct. The added covariance pathways
between the personality and affect variables make theoretical sense, as these
constructs are highly correlated, and measure similar psychological traits. It
can be hypothesised that a covariance pathway from control to the 1st order
determinant construct improves the model because this variable identifies as
both a cognitive process (as a positive cognitive buffer), and a stable
individual characteristic, thus linked to the 1st order determinants construct.
This revised model, displayed in Figure 17, was shown to provide a better
fit to the data than the hypothesised model, and accounted for 43% for the
variance in PWI scores.
221
Integration of SWB and Stress Theory
AMOS Revised Model 1
AMOS Revised Model 1
Optimism
Control
Control
2nd Order
1st Order
Neu
1
Neu
1Ext
Ext
1NA
High
High NA
1 PA
High
PA
High
1 NA
Low
Low
PA
Psych
Wellbeing
PWI group
PWB
Figure 17 Revised Model 1
The second model tested the hypothesised relationship between the
predictor variables (high activation negative affect, high and low activation
positive affect, and self-esteem), and the endogenous variables (PWI,
depression, anxiety, and stress), as shown in Figure 18.
AMOS Hypothesised Model 2
SES
S-E
High NA
High PA
Psych
Wellbeing
2nd Order
1st Order
Low PA
anxiety
stress
dep
PWB
Figure 18 Hypothesised Model 2
222
Integration of SWB and Stress Theory
Again, the goodness of fit indices were not adequate for the
hypothesised model, and examination of the ML estimates indicated that the
model could be improved with the addition of pathways. Examinations of
the MI for model 2 indicated that it would be improved with the addition of
three pathways. These included a direct effect from high activation negative
affect to high activation positive affect, from PWI to the 2nd order
determinants construct (cognitive buffers), and from stress to anxiety. The
revised model accounted for 88% of the variance in PWI, depression, stress
and anxiety scores, see Figure 19. Again, the covariance pathways between
the affect variables make theoretical sense due to their high correlation.
Stress and anxiety were highly correlated (.63), thus this covariance
pathway makes sense. Finally, the pathway from the 2nd order determinant
construct to PWI indicates that there is a direct effect between the positive
cognitive buffers and SWB.
223
Integration of SWB and Stress Theory
AMOS Revised Model 2
AMOS Revised Model 2
SES
S-E
2nd
1NA
High
NA
High
err03
1
High
PA
High
PA
err04
Psych
Wellbeing
2nd
Order
1
Order
res1
1st Order
Low PA
1Anx
anxiety
err13
1
stress
err12
stress
dep
PWB
err10
PWI
1
Figure 19 Revised Model 2
After modification, the goodness-of-fit indices for both models were
within the acceptable ranges, see Table 22. While further modification of
the models could have been undertaken through the addition of further
pathways and covariancies, these were tested and did not produce a
significantly better fit to the data. Moreover, care was taken not to ‘overfit’
the data (Hoyle, 1995). The strength of fit between the models and the data,
and the finding that Models 1 and 2 explain 43% and 88% of variance in
PWI, depression, stress and anxiety scores respectively, indicates that the
revised models provide a strong framework for the integrated SWB
Homeostasis and Conservation of Resources Theory.
224
Integration of SWB and Stress Theory
Table 22
Goodness of Fit Indices for Structural Models
Model
Hypothesised
Revised
Hypothesised
Revised
Model 1
Model 1
Model 2
Model 2
2
178.04
76.71
143.22
59.61
df
26
21
18
15
p
.000
.000
.000
.000
2/df
6.85
3.65
7.96
3.97
GFI
.85
.92
.85
.94
AGFI
.74
.83
.70
.86
NFI
.83
.93
.97
.93
CFI
.85
.94
.97
.95
RMSEA
.17
.11
.18
.11
IFI
.85
.95
.97
.95
ECVI
1.06
.61
.87
.45
Note. GFI: Goodness-Of-Fit Index, AGFI: Adjusted Goodness-OfFit Index, NFI: Normed Fit Index, CFI: Comparative Fit Index, RMSEA:
Root Mean Square Residual, IFI: Incremental Fit Index, ECVI: Expected
Cross Validation Index.
16.6
Summary
To briefly summarize this chapter:

The results of Study 3 were based on two questionnaires.
Questionnaire 1 contained measures of SWB, affect, personality,
optimism and control. Questionnaire 2 contained measures of SWB,
affect, depression, anxiety, stress, and self-esteem.

The highest correlation between the variable in Questionnaire 1
occurred between neuroticism and high activation negative affect,
225
Integration of SWB and Stress Theory
and high activation positive affect and low activation positive affect
respectively.

In Questionnaire 2, depression and self-esteem had the highest
correlation, followed by anxiety and depression, and high activation
and low activation positive affect.

Low activation positive affect had the highest correlation with PWI
scores in both questionnaires.

The hypothesis that depression and neuroticism would be the
strongest predictors of PWI scores was not supported. Using
Questionnaire 1 results, high activation negative affect was the
strongest predictor, followed by low activation positive affect and
high activation positive affect. Using Questionnaire 2 results, low
activation positive affect was the strongest predictor of PWI scores,
followed by high activation negative affect, high activation positive
affect, and depression.

The second hypothesis, that there is a significant difference in
personality, affect, optimism, control, self-esteem, depression,
anxiety and stress scores between individuals with normal and subaverage PWI scores was supported. There was a significant
multivariate effect, and univariate analyses revealed that the
difference between mean variable scores across the two groups of
normal and sub-average PWI scores differed significantly at the
p=.001 level.

The third hypothesis stated that a curvilinear relationship exists
between depression and PWI scores. There was some evidence to
226
Integration of SWB and Stress Theory
support this hypothesis, with a third order curvilinear polynomial
model fitting the data well, r2=.97. Furthermore, when PWI scores
are divided into the categories of normal (>70%SM) and subaverage range (<69%SM), the curvilinear polynomial models have a
stronger fit with the data, r2=1 for the normal range PWI group, and
r2=.98 for the sub-average range group.

Structural equation modelling was used to test the hypothesised
integrated model of SWB Homeostasis and Conservation of
Resources Theory. While the hypothesised AMOS models did not
initially provide a good fit to the data, minor modifications
(consisting of additional covariance pathways) improved the models.
The revised models had adequate goodness-of-fit indices, and
explained 43% and 88% of the data.
227
Integration of SWB and Stress Theory
Chapter 17
Study 3 Discussion
17.1
Relationship Between Variables
One of the purposes of Study 3 was to test whether some of the
findings from Study 1 would be supported. Specifically, it was expected
that the variables neuroticism and anxiety would have the highest
correlations of the variables in the study. As variables used in Study 3 were
divided into two separate questionnaires, neuroticism and anxiety could not
be correlated due to the lack of a common data set. However, the highest
correlation between the variables used in Study 3 occurred between anxiety
and depression (r=-.65), and PWI and depression (r=.59) respectively.
These results were comparable to that of Study 1, although the correlation
coefficients between the variables were slightly higher in the present
analysis.
Pearson Product Moment correlations were also conducted for the
variables within Questionnaires 1 and 2 of Study 3. The highest observed
correlation between variables in Questionnaire 1 occurred between
neuroticism and high activation negative affect. This result concurs with
previous research, which finds that these variables are typically highly
correlated, owing to the similar nature of the personality and trait affect
constructs (Berry & Hansen, 1996; McCrae & Costa, 1991; Watson &
Clark, 1992).
The highest observed correlation in variables of Questionnaire 2
occurred between depression and self-esteem. This finding concurs with
228
Integration of SWB and Stress Theory
previous research, in that self-esteem tends to have a strong positive
correlation with SWB (Boschen, 1996; Hong & Giannakopoulos, 1994), and
may be the strongest predictor of SWB overall (Cummins & Nistico, 2001).
17.2
Characteristic Differences Between Individuals with Normal
and Sub-Average SWB.
The hypothesis that there is a significant difference in the mean
scores of the tested variables between individuals with normal and subaverage range PWI scores was supported using Multivariate Analysis of
Variance. The normal range PWI group (70-100%SM) were significantly
higher in low activation positive affect, high activation positive affect,
control, self-esteem, optimism and extraversion, and lower in high
activation negative affect, neuroticism, stress, anxiety, and depression. This
result is intuitive, as the literature in the initial chapters of the thesis
indicates that individuals high in SWB tend to be higher in measures of
positive psychological factors, and lower in psychological illbeing.
The finding that the mean scores of the variables were significantly
different to the .001 alpha level supports the hypothesised lower threshold
of 70%SM. The SWB homeostasis theory depicts the normative SWB
range as occurring between 70-80%SM, with 70%SM being the
hypothesised lower threshold, beyond which adaptive capacity is exceeded
and defeated (Cummins et al., 2002). It is possible that if the 70%SM point
was not indicative of a lower threshold, beyond which homeostasis fails and
SWB decreases, the reported significant differences may not have been
found.
229
Integration of SWB and Stress Theory
17.3
Prediction of SWB
Multiple regression was performed to test whether data analysis
results would be consistent with those in Study 1, which found that
depression, extraversion and neuroticism are the strongest predictors of
SWB.
In both regression analyses performed of the variables of
Questionnaires 1 and 2, the three affect subscales were found to be stronger
predictors of PWI scores than the expected variables. The affect subscales
correlated more strongly to PWI scores than neuroticism or extraversion, so
it could be argued that the stronger predictive ability of affect was
anticipated. It is interesting to note, however, that high activation negative
affect correlated reasonably highly with PWI scores (r=-.53), and
neuroticism has a significantly lower correlation with PWI scores (r=-.42),
when these variables correlated quite highly together (r=.71). Therefore, it
appears that while negative affect and neuroticism are similar both in
statistical correlation and theoretical conceptualisation, affect measurement
possesses a quality that makes it a stronger predictor of SWB.
In a similar fashion, the second regression analysis revealed an
interesting observation with depression. Depression had the second highest
correlation with PWI scores (r=-.59), however did not have higher
predictive power than any of the three affect subscales. This findings again
suggests that the affect measures possess predictive power that goes beyond
that of depression and neuroticism.
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Integration of SWB and Stress Theory
17.4
The Homeostatic Mechanism
The theory of SWB homeostasis was tested in Study 1 by splitting
depression scores into groups, plotting the mean PWI scores for each group,
and observing whether a curvilinear relationship exists between the two
factors. In Study 1, the results offered little conclusive evidence to support
a curvilinear relationship. The graph, however, did suggest that a threshold
exists at the 70%SM point, whereby SWB loss is resisted, followed by a
more rapid decline in PWI scores.
An explanation for the above findings provided in Chapter 8 stated
that it is possible that the nature of the relationship between depression and
SWB was influenced by the fact that the mean PWI scores of both data sets
used in Study 1 were below the anticipated normative range of 702.5%SM.
Therefore, this test of SWB homeostasis was repeated in the Study 3
analysis.
The overall mean PWI score of 71.7%SM was slightly higher than
those in Study 1, being 71.1 and 69.3%SM in Data Sets A and B
respetively. The mean PWI scores for the ten depression groups were
plotted, and revealed evidence of a curvilinear relationship. A 3rd order
polynomial trend line was computed and fit to the data. This model (with a
regression coefficient of r2=.97) fit the data more closely than a linear
model, r2=.93, thus suggesting that the relationship between depression and
SWB is indeed non-linear.
Furthermore, when the normal range PWI group (>70%SM) was
separated from the sub-average PWI group (<69%SM), the curvilinear
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Integration of SWB and Stress Theory
model provided an even closer fit to the data, indicating that these two
groups behave differently and have independent relationships to depression.
Again there is evidence of a lower SWB threshold occurring at the
70%SM point. PWI scores decline from 80% to 70%SM, then as
depression increases, PWI scores briefly remain stable, and in fact increase
slightly. This suggests that the SWB regulatory system is resisting
homeostatic defeat, consistent with the assumptions of SWB homeostasis
theory (Cummins et al., 2002).
The PWI scores are observed to drop steeply from about 67%SM to
45%SM, and mild depression (measured by scores of 10-13 on the
depression subscale of the DASS) first occurs at 63%SM. Therefore, a
decline in PWI is evident before depression reaches a diagnosable level. It
can be assumed from this finding that SWB measures may prove to be a
more sensitive indication of depression than depression inventories. The
rationale for this assumption is that the loss of SWB indicates the process of
becoming unhappy and dissatisfied that may lead to depression, whereas
depression inventories measure the outcome of depression in individuals
whose SWB homeostatic systems have already failed. If this is true, then
SWB loss pre-empts depression, and thus a measure such as the PWI would
be more sensitive to depression, and could be used in a more preventative
fashion if used longitudinally with individuals who are known to be at risk
of depression.
A further interesting observation was that the two mean PWI scores
that fall below the 50%SM level fall into the moderate and severe
depression categories respectively. Cummins (1998) stated that world
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Integration of SWB and Stress Theory
population SWB scores predictably fall within the range of 60-80%SM,
with an adaptive range of 50-100%SM. Moreover, SWB levels of 50%SM
and under are grossly deviated from the normative range, and could be
hypothesised to coincide with depression as a consequence of SWB
homeostasis failure. There is some support for this hypothesis, as those
individuals under the 50%SM point reached a clinical severity of
depression.
17.5
Testing the Hypothesised Integrated Model
Structural equation modelling was used to test the hypothesised
integrated model of SWB homeostasis and COR theory that was presented
in Figure 12. Two models were constructed and tested within AMOS,
consistent with the variables in Questionnaires 1 and 2. While the initial
models did not have adequate goodness-of-fit statistics, minor modifications
were made in accordance with the Maximum Likelihood estimates, which
resulted in improved model power and adequacy of goodness-of-fit
statistics.
Both of the models tested within AMOS used the SWB Homeostasis
theory (Cummins et al., 2002) as a foundation. This foundation depicts
stable traits as 1st order determinants, which influence the positive cognitive
biases as 2nd order determinants, which together lead to psychological
wellbeing, or SWB.
The first model depicts affect (measured by the three affect
subscales of high activation positive affect, low activation positive affect,
and high activation negative affect) and personality (extroversion and
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Integration of SWB and Stress Theory
neuroticism) as the 1st order determinants. The 2nd order determinants are
control and optimism (measured by the PCOISS:12 and the LOT-R), and
the psychological wellbeing construct is measured by PWI scores and PWI
scores according to group (being >70%SM or <70%SM).
Several covariance pathways were added to the revised model, and
most of these were anticipated due to the high correlations that occur
between these variables. For instance, it was anticipated that the model
would improve with covariancies between neuroticism and high activation
negative affect, extraversion and high activation positive affect, and high
activation positive and low activation negative affect.
There was one covariance that needed to be performed according to
the Maximum Likelihood estimates, however, that was not anticipated. The
estimates indicated that the model would improve with the addition of a
covariance pathway from control to the construct of 1st order determinants.
It could be interpreted, then, that control is linked with stable individual
characteristics, and fits well within this construct. No previous research has
been found that makes comment as to the property of control as a purely
cognitive process versus a stable individual characteristic, however it could
be argued that control is more stable, enduring and trait-like than other
cognitive factors such as optimism and self-esteem. For instance, optimism
and self-esteem may be readily impacted on by environmental
circumstances, however control may relate to more stable beliefs,
attributional styles and behavioural responses that are applied in a similar
fashion to all situations, and are not impacted on by external events. If this
is the case, control may be better placed within the model under the 1st order
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Integration of SWB and Stress Theory
determinants with personality and affect. This will require testing in future
research studies.
The revised second mode tested within AMOS included affect
within the 1st order determinants, self-esteem within the 2nd order
determinants, and PWI, stress, depression and anxiety as measures of
psychological wellbeing. The added covariance pathways were logical,
including that between high activation negative and high activation positive
affect, between stress and anxiety, and between PWI scores and the 2nd
order determinants construct.
While each model does not contain all of the variables within study
3 collectively, they both accounted for a considerable amount of variance in
measures of psychological wellbeing, 43% and 88% respectively. A
perplexing question, however, relates to why the second model was so much
more powerful than the first. An outcome of 43% variance explained by a
model is respectable, but 88% explained variance is very high and
considerably rare (Tabachnick & Fidell, 2001).
The second model depicts affect as leading to self-esteem, which
together lead to levels of SWB, stress, depression and anxiety, with a direct
relationship between SWB and affect and self-esteem. It is not understood
why this model is more powerful than the other when it contains less
variables in the 1st and 2nd order determinant constructs. Affect was,
however, stronger than other variables in the prediction of PWI scores, and
self-esteem is known as possibly the most influential of the cognitive
buffers (Cummins & Nistico, 2001). More research would need to be
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Integration of SWB and Stress Theory
conducted in order to further the understanding about the mechanism of this
model and the importance of the variables.
While some aspects of the models were not fully understood, it is
clear that both successfully model the relationship between individual
difference factors and psychological wellbeing factors. Moreover, it can be
expected that a combined model that incorporates all the variables within
the hypothesised integrated model of SWB homeostasis and COR theory
would be more statistically powerful than these. This may further research
into the integrated process of subjective wellbeing, stress and depression.
17.6
Summary
One major conclusion drawn from the Study 3 data analysis is that
the three affect subscales, derived through factor analysis in Study 2, are
considerably stronger correlates and predictors of SWB than other variables
traditionally considered the most reliable predictors, such as neuroticism.
This finding suggests that further research is required to continue to test the
relationship between affect and SWB, and to compare these affect subscales
with other measures of affect.
A second conclusion drawn from this study is that there is support
for an integrated model of stress, depression and subjective wellbeing. The
two hypothesised models were tested and hound to explain a significant
amount of variance in PWI, depression, stress and anxiety scores. Thus
there is potential to expand on these findings by incorporating all of the
variables into one model, and also adding in other variables that relate to
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Integration of SWB and Stress Theory
subjective wellbeing, COR or stress theory, such as perceived social support
and attributional style.
237
General Discussion
Chapter 18
General Discussion
This thesis has reviewed literature pertaining to subjective
wellbeing, and its determining factors based on the presented model of
SWB homeostasis (Cummins et al., 2002). This model depicts personality
and affect factors as stable individual characteristics and 1st order
determinants of SWB, and optimism, self-esteem and control as the 2nd
order tripartite system of cognitive buffers. Together, the 1st and 2nd order
determinants allow for the regulation of SWB within the 70-80%SM range.
Depression reflects an absence of SWB, and can be hypothesised to
occur when the SWB regulatory system is overcome by extrinsic stressors,
resulting in homeostasis failure.
The aim of this research was to explore the operation of the
homeostatic system, which particular emphasis on homeostasis failure and
the relationship between SWB and depression. In addition, this research
had an exploratory component, in which new affect subscales were
developed from an existing four-factor scale, and an hypothesised model
based on the integration of SWB, depression and stress theory was
presented and tested.
In general, the results concurred with prior research, in that high
SWB was found to have a strong relationship with extraversion, positive
affect, optimism, self-esteem, and control; whereas low SWB was found to
have a strong relationship with neuroticism, negative affect, stress, anxiety
and depression. Furthermore, there was evidence to suggest that depression
238
General Discussion
indeed co-occurs with the failure of SWB homeostasis, and thus there is an
argument for the utility of SWB inventories in depression detection and
treatment evaluation. Finally, an integrated theory of SWB, depression and
stress appears to be justified, based on the successful results of the
exploratory data analysis and intuitive reasoning. The following sections
will discuss in detail the homeostatic mechanism under normal conditions
and in relation to depression.
18.1
The Normative SWB Range
Within the literature, it has been consistently found that indices of
subjective wellbeing for the majority of people are negatively skewed, with
scores lying over the midpoint of the measurement scales. Reviews by
Cummins (1995; 1998; 2000) have revealed the normative range of life
satisfaction in Western countries to be 75.0  2.5 %SM. It was based on
these findings of positive subjective wellbeing that the existence of a
homeostatic mechanism was proposed.
It was expected, then, that the mean Personal Wellbeing Index
scores for the studies within the current research would fall around 75%SM
and within the normative 70-80%SM range. Unexpectedly, the mean SWB
level of participants within both studies was lower than predicted.
Study 1 was based on two participant groups who were posted the
questionnaire at different times, yielding two separate data sets. The
participants in Data Set 1 had a mean PWI score of 71.1%SM (N=236), and
those in Data Set 2 had a mean of 69.3%SM (N=246). The participants in
Study 3 had a mean PWI score of 71.7%SM (N=458).
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General Discussion
It is clear from these mean scores that all three participant groups
involved in this research failed to meet the expected SWB level of 75%SM,
with one group (Data Set B of Study 1) falling outside of the normative
range. As this is the case, it is necessary to discuss the possible reasoning
and implications of this finding.
A possible explanation for the finding of relatively low mean SWB
levels is Study 1 was that the mail-out of questionnaires for this study
occurred in November of 2001. This was only shortly after the tragic events
of September 11. It could be assumed that this phenomenal event may have
influenced the overall SWB of the population, and in particular, the domains
of ‘Safety’, and ‘Future Security’.
The mean PWI score for Study 3 had increased in this data set from
those in Study 1, however it was still at the lower end of the expected
normative range. The second questionnaire mail-out, for Study 3, took
place in February of 2003. This could be described as a time in which there
were fears of ongoing terrorist attack and growing political tension. It is
therefore possible that:
1. Individuals have adapted to the negative events of the social
climate, as evidenced by the increase in PWI mean scores
from Study 1 to Study 3, and that
2. The ongoing social concerns continue to impact upon the
overall SWB levels of the population, as evidenced by the
failure to reach the predicted normative level of 75%SM.
The possibility that the sample used in the studies was not
representative of the general population was considered. There were 940
240
General Discussion
participants all together within the studies, who were involved in a
collaborative study between Australian Unity and Deakin University. These
participants collectively represented the national population on a
geographically proportional basis, and the demographic factors of gender,
age and income showed normal distributions within all studies. Therefore,
it seems unlikely that the sampling method was flawed or that the sample is
not representative of the general population.
Assuming that the 75%SM level can be considered the ‘normal’
SWB level within Western countries, and the occurrence of SWB means
below this is ‘abnormal’, the possible implications of this finding are
relatively unknown. It cannot be predicted what influence, if any, a lower
SWB mean will have on analysis results. However, predictions can be
made about the influence of a SWB mean that falls below the hypothesised
lower threshold point of 70%SM (Cummins et al., 2002). It was found that
the participants within Data Set B were higher in neuroticism, anxiety and
depression, and lower in extraversion than those in Set A, even though their
PWI mean scores were not significantly different. This is important to note,
as it suggests that even slight reductions in SWB may impact significantly
on the psychological characteristics of individuals, and the relationship
between measured variables.
18.2
The Homeostatic Mechanism
The purpose of the homeostatic mechanism is to ensure a positive
sense of wellbeing for the individual. It is proposed that the key
components of the homeostatic mechanism comprise personality, affect, and
241
General Discussion
the positive cognitive buffers, optimism, self-esteem and control. These
components influence the way in which new we integrate and cope with the
extrinsic conditions and stressors in life, and the extent to which these affect
us.
The homeostatic mechanism and predictors of SWB were
investigated in this research for two purposes. Firstly, analyses were
undertaken to explore the relationship between SWB and depression, and
the degree to which depression can be predicted by low SWB scores.
Secondly, evidence for a homeostatic system and the occurrence of
homeostatic defeat was tested in order to discover the potential of SWB
inventories to be used for the purpose of depression detection and treatment
evaluation.
18.2.1 Prediction of SWB
Across studies 1 and 3, the variables found to be the strongest
predictors of subjective wellbeing were neuroticism, extraversion and
depression (in Study 1), and the three subscales of affect, high activation
positive affect, low activation positive affect, and high activation negative
affect (in Study 3). In all regression analyses, and with each data set,
different variables were found to be the strongest predictors, and thus it
seems that the ability of a construct or variable to explain subjective
satisfaction varies under different circumstances. The potential implications
of these issues will be discussed.
Personality
242
General Discussion
Previous research indicates that personality traits are strong
predictors of SWB, particularly neuroticism (Costa & McCrae, 1989;
Headey & Wearing, 1989) and extroversion (Costa & McCrae, 1980; Diener
et al., 1992). It is generally expected that neuroticism will be negatively
associate with SWB, and extraversion positively associated with SWB.
Moreover, it is generally found that neuroticism has a stronger correlation
with SWB than does extraversion (Costa & McCrae, 1980; DeNeve &
Cooper, 1995; Headey & Wearing, 1989; Heaven, 1989; Schmutte & Ryff,
1997).
As Study 1 was based on two separate data sets using the same
questionnaire, it was possible to test the predictive strength of the variables
in Set A, and re-test these in Set B, therefore potentially reinforcing the
conclusions drawn. Unexpectedly, however, the data sets were found to
have different characteristics, with differences in mean scores of Personal
Wellbeing Index, neuroticism, anxiety and depression. These differences
appeared to impact upon the strength of the variable relationships to SWB.
Neuroticism was the strongest predictor of SWB in Data Set A,
followed by depression. Extraversion was not found to be a statistically
significant predictor. The participants in this data set were characterised as
lower in neuroticism, depression and anxiety, and higher in extraversion and
satisfaction than the other set. The mean SWB score of this participant
group also fell within the normative 70-80%SM range. In contrast,
extraversion was the strongest predictor of SWB in Data Set B, which was
described as a participant group that were less satisfied, and whose mean
SWB score fell bellow the normative range.
243
General Discussion
An important point worthy of reiteration is that the difference in
predictive strength of extraversion was not related to an overall difference in
extraversion scores per se. Data Sets A and B had near identical
extraversion scores, thus the finding that extraversion was predictive in Set
B and not Set A could indicate that the general characteristics of the group
define the predictiveness of particular variables, rather than the variable
properties themselves.
Depression
Previous research indicates that depression is inversely related to
SWB (Barge-Schaapveld et al., 1999; de Leval, 1999; Hansson, 2002;
Holloway & Carson, 1999; Kammann & Flett, 1983; Koivumaa-Honkanen
et al., 2001; Pyne et al., 1997). Furthermore, it seems intuitive that
depression represents a lack of satisfaction with life. Therefore, depression
was expected to be a strong predictor of SWB level.
Depression was found to be a significant predictor over Study 1
(both in Set A and Set B) and Study 2. However, in all analyses, depression
was never the strongest predictor of SWB. This indicates that while
depression is consistently related to SWB (unlike extraversion), depression
does not define SWB, thus depression is not the strongest predictor.
In Study 1, the predictive strength of depression was surpassed by
the personality variables, neuroticism and extraversion. In Study 3,
however, the predictive strength of depression was surpassed by the three
affect variables mentioned previously. On reflection, it is apparent that
personality and affect are both hypothesised to be the 1st order determinants
244
General Discussion
of the SWB homeostasis regulatory system. The 1st order determinants are
those that are stable individual characteristics that are not readily influenced
by environmental conditions.
With this in mind, it is possible to hypothesise that an individual’s
set-point SWB level is strongly determined by the stable constructs of
personality and affect, thus depression, reflecting a reaction to unstable
environmental factors, is a less significant predictor. It is possible that
depression is a consistent but weaker predictor of SWB because high
depression scores highly predict low SWB, but that a lack of depression is
non-predictive of SWB. For instance, it is expected that people suffering
from depression would report a low satisfaction with life, both in general
and in specific domains. This low evaluation of life and lack of enjoyment
in areas of life is an integral part of depression diagnosis. However, the lack
of depression would not be expected to correlate with SWB for two reasons.
First, a lack of depression merely indicates that in individual is not
depressed; it does not indicate that they are happy, or to what extent.
Secondly, it may be possible for individuals to experience depression
symptoms (such as changes in sleeping and eating patterns, restlessness) and
yet be coping successfully with life, therefore reporting elevated depression
and SWB scores. Further research into the effect of depression on SWB is
needed.
Affect
Affect is known to influence SWB levels (Diener, 1984), and is
closely related to personality, both statistically and conceptually (McCrae &
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General Discussion
Costa, 1991; Nemanick & Munz, 1997). As such, it was expected that
affect would be a strong predictor of SWB.
Affect was a measured variable in Study 3, and was found to be a
strong predictor of SWB. In fact, all three affect subscales were the
strongest predictors of SWB, and these were significantly stronger than any
other variable. While it was predicted that affect would predict SWB, it was
unexpected that affect would be so much stronger as a predictor than the
other variables, particularly personality.
With the strong theoretical and statistical ties between affect and
personality, it was expected that these variables would be roughly equal in
predictive power. The contrasting finding that affect is a significantly
stronger predictor indicates two possibilities. First, affect could simply be
both conceptually distinct from personality, and a superior predictor of
SWB. Second, the measurement of affect was based on three subscales that
were devised in Section 2 of this thesis. The creation of these subscales was
based on the modification of the 4-Dimensional Mood Scale (Huelsman,
Nemanick & Munz, 1998), which was an extension of the original Positive
and Negative Affect Schedule (Watson & Tellegen, 1985). As the 4-DMS
is a relatively new scale and the three subscales proposed in the thesis have
derived from this, it was not possible to predict the relationship between the
subscales, personality and SWB. It is apparent, however, that these affect
subscales are highly powerful, in that they are significantly more predictive
of SWB than other variables that are traditionally known to be highly
associated with SWB. Research should be conducted to discover if these
findings are replicable.
246
General Discussion
Summary
The contrasting findings discussed above suggest that there is not a
particular variable that is reliably predictive of SWB, but that relationships
between SWB and other constructs are unstable and dependent on the
overall participant characteristics. Two implications can be drawn from
these findings. First, it may be relatively futile conducting regression
analyses and claiming that certain variables highly predict SWB, as unless
the general characteristics of the group are known, the findings may not be
replicable or applicable to the general population. Second, it may be that
different constructs or variables are reliably predictive of different levels of
SWB. For instance, negative psychological measures such as neuroticism
and depression may reliably predict SWB within the normative range,
whereas positive psychological measures, such as extraversion and positive
affect, may predict sub-average SWB. These possibilities need further
research investigation.
18.2.2 Homeostatic Failure
Under normal circumstances, SWB is maintained within a set-point
range within the moderate to high positive range, around 752.5%SM. The
SWB equilibrium is maintained by regulating mechanisms involving stable
individual characteristics and cognitive factors. These mechanisms
effectively absorb the impact of environmental stressors, thereby allowing
the stable maintenance of SWB in unstable conditions.
247
General Discussion
Every homeostatic system, however, has its limitations, and there are
going to be circumstances in which the environmental stressors apply more
pressure on the system than can be effectively managed, resulting in its
failure to maintain SWB at moderately high levels. In these situations,
homeostatic breakdown occurs.
Depression is conceptually linked to homeostasis breakdown, and
can be perceived as the consequence of homeostasis failure and indicative of
abnormally low SWB levels. Therefore, one of the aims of the thesis was to
identify the relationship between depression and SWB at various levels of
depression. This was done by dividing depression scores into groups of
increasing symptom severity, calculating the mean PWI scores for these
groups, and graphing these data points. SWB homeostasis theory would
predict that the relationship between SWB and depression is curvilinear,
wherein SWB levels remain stable with increasing depression
symptomatology, until the 70%SM lower threshold point is reached, at
which point SWB levels drop sharply.
The test of SWB homeostasis in Study 1 provided weak evidence of
a curvilinear relationship between SWB and depression. There was a fairly
linear relationship between the two factors, however a third order
polynomial curvilinear trendline fit the data points more closely than a
linear model (r=.98 and r=.95 respectively). While this relationship was
fairly linear in nature, a plateau occurred at the 70%SM level. This plateau
appears to indicate resistance to homeostasis failure occurring at the lower
threshold point, consistent with the predictions of Cummins, Gallone and
Lau (2002).
248
General Discussion
This test of homeostasis was repeated in Study 3 with similar
findings. There was again weak evidence of a curvilinear relationship
between SWB and depression scores, however the curvilinear model
trendline fit the data more closely than the linear model (r=.97 and r=.93
respectively). Furthermore, there was again a plateau occurring at the
70%SM point. The consistency of the findings indicate that a), the
relationship between depression and SWB tends to be slightly more
curvilinear than linear, and that b), the homeostatic system tends to resist
failure at the 70%SM point.
In Study 3, a second graph was created, wherein the data points for
individuals within the normative SWB range and those within the subaverage range were separated. Curvilinear trendlines were then applied to
the two groups of data points separately. In this analysis, the trendlines
provided a closer fit to the data than they had previously, when applied to
the ungrouped data points all together. This finding indicates that the two
groups of SWB scores (normal and sub-average range SWB) are better
defined separately, and have different properties in relation to depression.
For instance, the relationship between SWB and depression in individuals
with normal SWB levels seems to differ from that in individuals with subaverage depression. This argument may relate to the suggestion above in
18.2.1 that the ability to predict SWB from depression scores may be
dependent on the SWB level and other psychological characteristics of the
individual.
While the relationship between depression and SWB was not found
to be as curvilinear as expected, it is important to note that different
249
General Discussion
depression scales were used in Study 1 and Study 3 (Hospital Anxity
Depression Scale, and Depression Anxiety Stress Scale, respectively) and
still the same trend between SWB and depression were found. This
indicates stability in the nature of relationship between the variables.
Finally, possibly the most clinically interesting finding to come out
of the homeostasis testing was that depression appears to be preceded by a
drop in SWB scores below the lower threshold of 70%SM. In Study 3,
depression is not indicated at the 67%SM level, but mild depression is
indicated at the 63%SM level. Therefore, if 70%SM is viewed as the lower
SWB threshold and scores falling below this indicate homeostatic failure,
then homeostasis failure occurs before the onset of depression
symptomatology. Moreover, depression scores increased with the decrease
in SWB, such that SWB scores below 50%SM were indicative of moderate
to severe depression. It may be possible, then, to map out clinical
depression severity according to SWB level. For instance, the results of
Study 3 indicate that no depression in evident at 70-100%SM, mild
depression occurs between 54-64%SM, and moderate to severe depression
occurs below 50%SM.
These arguments make theoretical sense, as homeostasis failure is
hypothesised to result from the inability of the cognitive mechanisms to
successfully manage environmental stressors (Cummins et al., 2002),
whereas depression occurs when cognitive vulnerability interacts with stress
(Simons et al., 1993), causing a negative cognitive bias (Beck et al., 1979).
In essence, unmanageable stressors cause homeostasis to fail and SWB to
250
General Discussion
drop, which is then followed by the presentation of depressive
symptomatology.
As this research was exploratory in nature and the relationship
between depression and SWB have not been studied in this way before, it
essential that further research is conducted to discover more about their
relationship, and the potential for SWB inventories to be used in
conjunction with or instead of depression inventories.
18.2.3 Rationale for the Use of SWB Inventories for Depression
If the arguments that were explored above are justified, it is
foreseeable that SWB inventories could effectively be used alongside
traditional diagnostic procedures for depression detection and treatment
evaluation. The evidence to suggest that this could be done was explored in
the previous section, 18.2.2, however, the reason that this should be done is
now discussed.
In clinical psychology and psychiatry, depression is diagnosed
according to criteria within diagnostic manuals such as the DSM-IV (APA,
1994). Psychiatrists generally follow a medical model, mainly attributing
depression to biological causes, and typically treating depression with
pharmacological medication, thereby treating the biological cause of
depression, as well as using other more cognitive or psychological therapies.
While depression aetiology may in some cases be attributable to
biology, and depression does indeed respond to pharmacological treatment,
the founders of cognitive perspectives of depression hold that depression is
the consequence of negative thinking, rather than the cause (Coyne &
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General Discussion
Gotlib, 1983). Both the Learned Hopelessness theory (Alloy & Abramson,
1999) and Beck’s Theory (Beck et al., 1979) state that negative cognitions
play a causal role in depression, which means that negative thoughts
precede depression symptomatology. This perspective fits well with the
theory of SWB homeostasis, which assumes that when environmental
stressors overwhelm the homeostatic system, the cognitive buffers (of
optimism, self-esteem and control) fail, and SWB drops, potentially leading
to depression.
The Conservation of Resources Theory (Hobfoll, 1988) follows a
similar principle, despite having a slightly different focus. The COR Theory
assumes that stress is caused when the system of resources (i.e., self-esteem,
optimism, social support, money) is used up and depleted by environmental
stressors, thereby allowing an individual to experience negative stress and
potentially suffer depression.
If depression is indeed a consequence rather than a cause of negative
thinking, then there is justification on several levels for the use of SWB
inventories in depression detection and treatment evaluation. These will be
explored in point form.
1. Viewing depression as a consequence implies that depression
follows the breakdown of the system that regulates SWB.
Therefore, it is intuitive that SWB measures could be used to predict
depression before its onset or at a low level of severity. Thus, SWB
measures could be used in a preventative fashion, to enable the
treatment of clients before their symptoms are severe, or before they
loose the motivation or will to fight the condition. The data analysis
252
General Discussion
conducted in Study 3 testing the homeostatic mechanism and
relationship of SWB to depression provided evidence to suggest that
it would indeed be possible to use SWB measures in this way. A
drop in SWB below the lower 70%SM threshold occurred before
depression symptomatology was severe enough to diagnose,
therefore the SWB measure provided a more sensitive measurement
of depression onset than did the depression inventory.
2. SWB is measured according to specific domains of life. Therefore,
SWB can be examined both as a total, general feeling of satisfaction
with life, or as an indication of satisfaction with particular
components of life. One of the criticisms of depression made in
Chapter 5.2 was that depression inventories only provide an overall
score indicating an arbitrary level of depression, which is not related
to any particular areas of life. Therefore, these inventories allow the
diagnosis of depression, but do not provide information about why a
person may be depressed, or in what area of life. SWB inventories
could be used in conjunction with depression inventories to provide
additional information about specific domains that may be
attributing to or most severely affected by the depression, thus
guiding treatment.
3. Finally, SWB may be effectively used to evaluate the success of
depression treatment. The justification for this is twofold. First,
depression inventories can only state that an individual is either
depressed, or not depressed. Therefore, if we hold the view that
health is indeed more than the absence of illness, these inventories
253
General Discussion
simply cannot provide an adequate evaluation of treatment success.
SWB measures provide the measurement of subjective wellbeing on
a continuum from dissatisfaction to satisfaction, therefore providing
a more sensitive evaluation of treatment success. Second, if
depression is the consequence of negative thinking, then increases in
SWB should precede decreases in depression symptomatology, just
as it was found that SWB decreases preceded depression onset.
18.3
The Conceptualisation and Measurement of Affect
It should be noted that thorough testing is required before general
assumptions can be made about affect or its relationship to other variables
as based on the three proposed subscales. It is recommended that the
subscales be tested in comparison with the traditional PANAS (Watson &
Tellegen, 1985), in order to establish its relationship with other variables, in
particular, personality and SWB.
18.3.1 Proposed Affect Subscales
In Section 2 of the thesis, the cirumplex model of affect was
reviewed, and the 4-Dimensional Mood Scale (Huelsman, Nemanick &
Munz, 1998) was replicated through factor analysis and then revised by
creating two new subscales that provide a better fit to the theoretical
conceptualisation of affect. The replication of the 4-DMS was successful,
finding that all items within the scale loaded on the appropriate factor
254
General Discussion
without significant crossloadings, and the reliability of each subscale was
high. A second factor analysis was then undertaken in order to choose items
for the revised subscales according to statistical strength and conceptual
relevance.
Several new items were used in the high activation positive affect
and high activation negative affect subscales to provide a broader
measurement of these quadrants of the affective circumplex. These items
included eager, enthusiastic, inspired, determined and excited in the high
activation positive affect subscale, and distressed, anxious, agitated, scared,
hostile, and nervous in the high activation negative affect subscale. These
items also loaded on their appropriate factor with no significant
crossloadings, and the subscales were high in reliability.
The original low activation positive affect subscale used by
Huelsman and colleagues (1998) was retained as this was deemed to provide
a broad measurement of this affective quadrant. The original low activation
negative affect subscale contained only items relating to the physical state of
tiredness, however, despite an attempt to broaden this measure by including
affective states of depression, sadness, boredom, etc into the item pool, a
clear low activation negative affect factor could not be defined through
factor analysis. Therefore, it was recommended that the original subscale
should be used in conjunction with a brief depression inventory to
compensate for the affective breadth that is lacking in this quadrant of the
circumplex.
The new subscales were devised with the aim to create a scale that
represents the underlying theory of affect, and to test whether such as scale
255
General Discussion
would be useful in psychological research studies. As was reviewed before,
the new subscales were found to be the strongest predictors of SWB, and
appear to tap into a construct that is conceptually dissimilar to personality.
This was evidenced by correlations between the affect subscales and
personality subscales being less than .8 (which would indicate singularity,
or measurement of the same construct), and the fact that affect predicted
SWB but personality did not.
The findings lead to the conclusion that the new affect scales were
successful in broadening the measurement of affect, and that they are useful
in psychological research studies. As the scale is largely experimental,
further studies are needed to confirm the appropriateness of each item
within the subscales, and to investigate the relationship of affect (as
measured by the new subscales) to other psychological variables.
18.3.2 Recommendations for Scale Improvement
The findings of the analyses relating to the new affect subscales in
this thesis have been positive, however there are several ways in which the
affect scale could be improved.
First, further research is needed to discover whether the low
activation negative affect quadrant of the circumplex can be defined as a
reliable and independent factor. When additional items were introduced
into the item pool to represent this quadrant and multiple factor analyses
were performed, the factor could either only be represented by items
relating to tiredness, or if these items were removed from the pool, the items
within the high activation and low activation negative affect factors could
256
General Discussion
not be separately defined due to high crossloadings. Therefore, it is
important that research is conducted to discover if the low negative affect
quadrant can indeed be measured separately from high negative affect,
whether this is not possible, or whether this quadrant is essentially a subfactor or component of high activation negative affect.
Second, a multidimensional scaling analysis should be conducted on
affect items within a large item pool. This analysis would test the
assumption that the items within the new subscales provide a broader
measurement of affect according to the theoretical circumplex, and allow
this hypothesis to be supported or refuted. It would also be possible to see if
any other items would provide a broader measurement of affect in each
quadrant, or if additional items should be incorporated into the scale.
Moreover, multidimensional scaling would allow the anslysis of the items
that fall within the low activation negative affect quadrant. Hypotheses
could then be generated about why measurement of this construct has been
difficult. It is possible that the items that fall within this quadrant are not
intuitive or unexpected. Alternatively, it is possible that the affective
strength of the high activation dimensions pulls the items out of this
quadrant, or that the items form a tight cluster and so broad measurement is
not possible.
18.4
The Integration of Homeostasis, Stress and Depression
Theory
The third section of this thesis investigated the potential for the
integration of SWB homeostasis and Conservation of Resources theory. A
257
General Discussion
model of this integrated theory was presented in Figure 12. This model
builds on the SWB homeostasis theory by viewing cognitive factors both as
positive buffers and resources, and by adding to the model energy and objet
resources as third order determinants of SWB. According to this model,
when the homeostatic mechanisms are operating successfully,
environmental stressors are absorbed by the internal and external buffers
and resources, no negative stress is experienced, and SWB remains
regulated to a moderately high level. When the stressors are numerous,
severe or chronic, however, the resources are depleted, the cognitive
regulating mechanisms fail, negative stress is experienced, and SWB drops
as a consequence of homeostasis failure.
18.4.1 Evaluation of the Hypothesised Models
The hypothesised integrated theory was tested in Study 3 using
structural equation modelling using the following variables in two models.
Personality and affect were measures of 1st order determinants of SWB,
self-esteem, optimism and control were measures of 2nd order determinants
of SWB, and the psychological wellbeing was measured with the PWI,
depression, anxiety and stress subscales. These variables were divided and
tested in two models according to the questionnaire they were placed in.
With minor modifications, consisting of added covariance pathways
between some of the variables, these models provided a good fit to the data,
and successfully accounted for variance in psychological wellbeing. The
models in fact accounted for 43% and 88% of variance in SWB and
psychological wellbeing respectively. This finding suggests that a) the
258
General Discussion
mechanisms responsible for regulating SWB can indeed be described in a
model involving the hypothesised variables, and b), that there is reason to
believe that a single model incorporating all of the variables tested over the
two AMOS models would provide a stronger fit to the data and explain
more of the variance in psychological wellbeing measures.
18.4.2 Justification for an Integrated Model
As we have seen that the homeostasis and COR theories can be
integrated, it is now important to discuss the rationale for this integration. It
is thus argued that there is conceptual strength in a theory integrating the
components of SWB, stress and depression for the following reasons:
1. Depression can be perceived as the absence of SWB. Under
conditions of homeostatic defeat, the symptoms of depression are
coincident with low values within the cognitive buffering system
and decrease of SWB level (Cummins et al., 2002). Thus, as the
buffering systems fail to absorb the impact of negative events
and environmental stressors, their own valued decrease and the
consequential fall in SWB is mirrored by a rise in the indices of
depression (Cummins et al., 2002)
2. Cognitive theories of depression view stress and negative events
as activating negative cognitions, which lead to the loss of SWB
and the onset of depression (Alloy & Abramson, 1999; Beck et
al., 1979)
259
General Discussion
3. The diathesis-stress model of depression predicts that depression
is the outcome of stress and cognitive vulnerability to depression
(Simons et al., 1993)
4. Cognitive vulnerability to depression relates to the individual
set-point range, which predicts the extent to which the
homeostatic system is robust to negative life events. Thus, a
relatively high setting should indicate a high level of resilience,
while a low setting predicts a fragile system, or vulnerability,
that is prone to failure, and therefore, to depression (Cummins et
al., 2002)
5. The cognitive buffers that regulate SWB influence the pattern of
thinking and the processing of negative experiences (Cummins,
2000), and relate to how stressful situations are appraised
(Lazarus, 1990), and
6. The resource variables that were from COR theory that were
added to the SWB theory will potentially provide a more
thorough explanation of SWB homeostasis
It is further argued that an integrated theory of SWB, stress and
depression would have the following implications:
1. An integrated theory would provide a better understanding of
SWB maintenance, the effect of stress on the psychological
system, and the occurrence of depression
2. Academically, the integrated theory furthers theoretical research
in the area of SWB, and
260
General Discussion
3. Clinically, the integrated theory encourages mental health
professionals to take a more ideographic and psychosocial view
of depression, and to be aware of the influence of stress and
SWB loss on the mental health of individuals.
18.5
Limitations
The above discussions have reviewed the findings of the data
analysis within this thesis, and provided a rationale for the use of SWB
measurement in the area or depression and the expansion of SWB theory
with the integration of stress and depression theory. In light of the research
successes, it is now important to discuss the limitations of the three studies.
18.5.1 Study 1
One major limitation of Study 1 was imposed by the alteration of the
response format for the Hospital Anxiety and Depression Scale. This scale
was measured on an 11-point Likert scale as opposed to the original 4-point
Likert scale. Additionally, the response format was altered from a
frequency rating (‘not at all’ to ‘most of the time’) to the more standard
‘Strongly Agree’ to ‘Strongly Disagree’) format. Therefore, the data
collected reports only the degree to which a participant agrees that
depression and anxiety symptomatology applies to them, and could not be
used to detect clinical anxiety or depression levels. This limited the
research, as it was not possible to identify participants experiencing mild,
moderate or severe levels of depression and anxiety, and it was not possible
to identify at what level of SWB the onset of depression occurs.
261
General Discussion
The participant characteristics in Data Sets A and B were found to
be significantly different, in that Set B was generally less satisfied and
higher in negative psychological measures such as neuroticism and
depression. It is not known why these differences exist, and these
differences may mean that the conclusions of the study can not be
generalised to the population.
The relationship between depression and SWB was analysed by
plotting SWB means according to depression groupings. While the
curvilinear nature of the relationship was tested by applying curvilinear and
linear trend lines to the graph and assessing which better fit the data
(according to r2 values), the property of the observed relationship as linear
or curvilinear was not statistically tested. Furthermore, the theory of
homeostasis predicts that the slope of the relationship between depression
and SWB within the range of 70-100%SM is more shallow than the steeper
decline of SWB below 70%SM. This prediction was not statistically tested.
18.5.2 Study 2
Many of the affect items were found to have significant skew and
kurtosis. These variables were not altered. If the research was investigating
the relationships between affect and other variables within a normal
population group, it would have been advisable to remove or transform nonnormal cases so that the findings relate to a normal population. However, as
this research was investigating the states of depression and anxiety, which is
expected to coincide with abnormally high scores on negative affect items
and low scores on positive items, the non-normal cases needed to be
262
General Discussion
retained in order to explore the relationship between affect and depression.
Thus, while the decision to retain the non-normal cases can be justified, it is
important to note that outlying cases tend to shift the mean of the measured
variable, and therefore it is possible that conclusions drawn involving the
non-normal affect variables cannot be generalised to a normal population.
A second limitation was that multidimensional scaling analyses were
not conducted when affect items to represent the affect subscales were
chosen. Multidimensional scaling would have provided justification for the
selection of particular affect items for the new subscales. This analysis was
deemed beyond the scope of the present research.
It was decided that the original low activation negative affect
subscale within the 4-DMS did not provide a broad measurement of this
quadrant of the circumplex because it only comprised items related to
tiredness. A factor using alternative items could not be defined. It was
therefore recommended that the original subscale should be retained until
further research can improve on this subscale. However, this subscale,
representing tiredness, was not used in the subsequent analyses in Study 3.
Inclusion of this variable may have improved the models analysed in Study
3, or increased the percentage of explained variance in SWB.
18.5.3 Study 3
One of the main limitations of Study 3 was imposed by the necessity
to divide the inventories used into two questionnaires. It would have been
ideal to conduct the Study 3 analyses using a single questionnaire with all
inventories together. It was necessary to divide the inventories between two
263
General Discussion
questionnaires, however, as a single questionnaire containing all inventories
would have been extensive, and arduous for participants to complete. The
inventories were divided to reduce participant fatigue and increase response
rate. There are several negative implications of conducting this research
with two separate data sets. First, some of the analyses from Study 1
(particularly the regression analyses) could not be replicated for
comparative analyses. Second, the hypothesised integrated homeostasis and
COR model had to be tested in two parts, using two separate models in
structural equation modelling. Therefore, the entire theoretical model could
not be tested.
There were also limitations to the structural equation modelling that
was performed to test the theoretical integrated model. Firstly, the variable
of perceived social support was not tested. Perceived social support is an
important resource within the COR theory, and may have contributed to the
explanation of SWB homeostasis. At the time of planning the inventories
for Study 3, it was decided that the inclusion of this inventory would make
the questionnaires too lengthy.
18.6
Conclusions
18.6.1 Brief Overview of Findings
In general, there is both conceptual and statistical support for the
homeostatic regulation of SWB. A strong association between SWB and
elements of the homeostatic system exists, including the stable individual
characteristics of personality and affect, and the positive cognitive biases.
264
General Discussion
In particular, personality and affect appear to be the most significant
determinants of SWB. Furthermore, people with normal SWB levels,
within the 70-80%SM range, were fundamentally different to those with
sub-average SWB levels, in that they were found to be more satisfied, and
had a more positive perception of themselves, their environment, and their
life.
Affect was found to be the most significant predictor of SWB, and
was an important component in the hypothesised model of SWB
maintenance, which accounted for up to 88% of variance in SWB. The
affect measure used was designed via factor analysis and tested in follow-up
analyses within this research, and was found to have high subscale
reliability.
The hypothesised lower threshold of SWB at 70%SM was supported
within this research. The decline of SWB below the 70%SM threshold
point was found to precede the onset of clinical depression. This was also
evidence that SWB inventories can provided a more sensitive measure of
depression than depression inventories, which measure the symptomatic
consequence of the depression state. Thus, there was rationale for the use of
SWB inventories for depression detection and treatment evaluation.
Theories on stress, depression and SWB share some fundamental
conceptual assumptions. These theories assume that there is a causative
relationship between environmental stressors, cognitive factors, and
psychological wellbeing, whether measured by the experience of stress,
depression or satisfaction. Moreover, they assume that when a strong
negative load is placed on a system, the system attempts to manage this
265
General Discussion
load, and if unsuccessful, the system fails and is vulnerable to the cognitive
consequences of the stressors. The theories of SWB homeostasis and stress
were integrated in an hypothesised model and tested, with the finding that a
large proportion of variance in psychological wellbeing, including SWB,
depression and stress, can indeed be explained through such an integrated
theory.
Therefore, it can be concluded that the variables of personality,
affect, optimism, self-esteem, and control all play an integral role within the
homeostasis and maintenance of SWB, and that SWB in turn plays an
integral role in the onset and severity of depression. Moreover, it can be
concluded that an integrated model of SWB, stress and depression theory
would significantly contribute to psychological literature.
18.6.2 Future Research and Practical Applications
Much of the analyses within this thesis were experimental, and as
such, further research is needed to replicate and expand on the findings. For
instance, more research is required in the area of affect, and on the
confirmation of items that best represent affective experience according to
both theoretical conceptualisation and statistical strength. The ideal affect
measure would be one that is both based on a strong conceptual framework
such as the circumplex model, and accounts for a large amount of the
variance in affective experience. While the current research was an attempt
to achieve this aim, much evolution of affect theory and measurement is
required.
266
General Discussion
It was proposed that SWB measures could effectively be used in
depression detection and treatment evaluation. This hypothesis needs to be
experimentally tested, not only to test the efficacy of SWB for this purpose,
but also to compare the utility of SWB in comparison to traditional
measures of depression detection and treatment evaluation.
Similarly, it is recommended that longitudinal research is conducted
in order to investigate the relationship between depression and SWB over
time. The investigation of SWB levels over time in individuals who have
episodic depression would allow conclusions to be made about the ability of
depression onset to be predicted from SWB measurement. This research
would also be able to identify whether depression onset co-occurs with
SWB loss evenly over all domains, or initially in particular domains, thus
allowing conlusions to be made about the ability of SWB measures to be
used to guide psychological treatment for depression.
Finally, it is recommended that further research is conducted on the
integrated model of homeostasis and stress theory. Such an integrated
model could potentially provide a deeper understanding of the mechanisms
responsible for the maintenance of SWB, and the relationship between the
different measures of psychological wellbeing.
Overall, this thesis represents a new direction for subjective
wellbeing. A high sense of satisfaction and wellbeing is a state that all
people value to and wish to maintain, and for most of us, this is occurs
without conscious effort. However, our increasingly stressful, demanding
and fast-paced environment threatens the maintenance of our SWB, and has
267
General Discussion
contributed to the labelling of depression as the major disorder of the 21st
century. The integration of SWB, stress and depression theory thus
represents a paradigm shift from the view of depression as a biological
disease to the view of depression as a bio-psycho-social consequence of
homeostasis failure. While there is still much theoretical evolution to take
place, it is believed that the marriage of SWB, stress and depression theory
will lead to the earlier detection and more specific treatment of depression,
and a deeper understanding of the subjective wellbeing homeostasis process.
268
A New Direction for Subjective Wellbeing
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291
General Discussion
Appendices
292
General Discussion
Appendix A
Deakin University Human Research Ethics Committee Approval
293
General Discussion
Appendix B
Study 1 Questionnaire
A Survey of Subjective Wellbeing
Survey compiled by Vanessa Cook
Supervised by Professor Robert Cummins
Deakin University, Burwood
294
General Discussion
Dear
Thankyou for taking part in the survey run by the Deakin University Centre on Quality
of Life in April and May of 2001.
I would firstly like to direct you to an executive summary of the results of that study. If
you would like to read the executive summary and more detailed information concerning this study,
you can access the full report through the Australian Centre on Quality of Life website,
www.acqol.deakin.edu.au, by clicking on the ‘Australian Unity Index of Wellbeing’ heading.
After completing the last survey, you stated that you may be willing to be involved in another
related study. Therefore, this package includes a Plain Language statement and Questionnaire for a
study run by Vanessa Cook and Professor Robert A. Cummins through Deakin University. I would
very much appreciate it if you could spare the time to complete the questionnaire and return it to me in
the reply-paid envelope.
Thank-you very much, and we hope your family have a safe and happy Christmas!
Sincerely,
Vanessa Cook.
2
General Discussion
DEAKIN UNIVERSITY ETHICS COMMITTEE
PLAIN LANGUAGE STATEMENT
My name is Vanessa Cook, and I am currently studying the Doctorate of Health Psychology at
Deakin University. My supervisor is Professor Robert Cummins, School of Psychology, at Deakin
University, Burwood.
Research Intention:
I am inviting you to participate in a study that aims to test whether a Quality of Life inventory
which measures levels of satisfaction with life, can also be used to detect how unhappy or depressed
people feel with their life.
If you agree to take part in this study, you will complete a questionnaire that will take no
longer than 20 minutes of your time. The questionnaire includes the Comprehensive Quality of Life
Scale, the Depression Anxiety and Stress Scale, the NEO Personality Inventory, a Mood measure, and
self-esteem, mastery, control and optimism scales. You are asked to complete the questionnaire in
your own time and place it in the reply-paid envelope to send it back to me.
I don’t expect that this questionnaire will make you feel bad. However, if during or after
filling it in you are unhappy or depressed and need to talk to someone, please feel free to cease your
participation and contact one of the services listed at the end of this Plain Language Statement.
Your participation is completely anonymous and voluntary, and you may withdraw from the
study at any time up to the return of the questionnaire. All data collected will be kept strictly
confidential, and you will not be identified. Participants are anonymous, and no individual data will
be analysed or published. The data will stored in accordance with Deakin University guidelines and
destroyed after six years after completion of the study. The results of the study can be made available
to you at your request.
Your participation would be an immense help to me, and I hope, and enjoyable experience for
you.
If you are interested in participating or have any queries or concerns, please do not hesitate to
contact myself, Vanessa Cook on 0419 894 621, vlcook@hotmail.com, or my supervisor, Professor
Robert Cummins on 9244 6845 at any time.
1.
Counselling for couples:
Lifeworks – relationship counselling and edu service (Melb)
Relationships Australia – individual, family and group counselling (Croydon)
96547360
97259964
2.
3.
Telephone counselling:
Life Line – generalist, crisis, info and referral service (24H) (Melb)
Kids Help Line – counselling for children (24H) (Camberwell)
Care Ring – generalist, info and referral service (24H) (North Melb)
Mental health services:
Child and Adolescent Mental Health Service (Heidelberg)
131114
1800551800
136169
(24H)
4.
94963620
94965000
Depression services:
Mood Disorder Support Group – depression, self help, education and referral service (Richmond)
94270406
Should you have any concerns about the conduct of this research project, please contact the Secretary, Ethics Committee,
Research Services, Deakin University, 221 Burwood Highway, BURWOOD VIC 3125. Tel (03) 9251 7123 (International +61 3 9251
7123).
DEAKIN UNIVERSITY ETHICS COMMITTEE
3
General Discussion
For the first part of the questionnaire, please read the words below, and enter a number
from 0 to10 in each box to reflect how you feel. A score of 0 indicates that you strongly disagree
that the word represents how you feel, and a score of 10 indicates that you strongly agree that
the word represents how you feel.
A
B
ctive
Afr
ored
Al
D
ert
Ar
D
rowsy
At
D
tentive
ease
Ag
gravated
Agi
tated
An
xious
rained
oused
At
aid
ull
Cal
m
Co
ntented
No
nchalant
Please answer the following questions by placing a () in the appropriate box for each
question. Do not spend too much time on any one question. There are no right or wrong answers.
How SATISFIED are you with each of the following life areas?
Please (  ) the box that best describes how satisfied you are with each area if your life.
1.
How Satisfied are you with your STANDARD OF LIVING?
Completely
Mi
Completely
xed
Dissatisfied
0
2.
satisfied
1
2
3
4
5
6
7
8
9
10
How Satisfied are you with your HEALTH?
Completely
Mi
Completely
xed
Dissatisfied
0
3.
satisfied
1
2
3
4
5
6
7
8
9
10
How Satisfied are you with what you ACHIEVE IN LIFE?
Completely
Mi
Completely
xed
Dissatisfied
0
4.
satisfied
1
2
3
4
5
6
7
8
9
10
How Satisfied are you with your PERSONAL RELATIONSHIPS?
Completely
Mi
Completely
xed
Dissatisfied
0
satisfied
1
2
3
4
5
6
7
8
9
10
4
General Discussion
5.
How Satisfied are you with HOW SAFE YOU FEEL?
Completely
Mi
Completely
xed
Dissatisfied
0
6.
satisfied
1
2
3
4
5
6
7
8
9
10
How Satisfied are you with FEELING PART OF YOUR COMMUNITY?
Completely
Mi
Completely
xed
Dissatisfied
0
7.
satisfied
1
2
3
4
5
6
7
8
9
10
How Satisfied are you with YOUR FUTURE SECURITY?
Completely
Mi
Completely
xed
Dissatisfied
0
8.
satisfied
1
2
3
4
5
6
7
8
9
10
Do you feel any happier or sadder than you usually do?
Yes,
happier
Yes,
sadder
No
If yes, how much happier or sadder than usual do you feel?
Very
M
oderately
Much
Little
More
0
9.
normal?
1
2
3
4
5
6
7
8
9
10
Has anything happened to you recently causing you to feel happier or sadder than
Yes,
happier
Yes,
sadder
No
If yes, how strong would you rate this influence?
Very
M
oderate
Very
Weak
Strong
0
1
2
3
4
5
6
7
8
9
10
5
General Discussion
10.
How Satisfied are you with the ECONOMIC SITUATION IN AUSTRALIA?
Completely
Mi
Completely
xed
Dissatisfied
0
satisfied
1
2
3
4
5
6
7
8
9
10
11.
How Satisfied are you with the STATE OF THE AUSTRALIAN
ENVIRONMENT?
Completely
Mi
Completely
xed
Dissatisfied
0
12.
satisfied
1
2
3
4
5
6
7
8
9
10
How Satisfied are you with the SOCIAL CONDITIONS IN AUSTRALIA?
Completely
Mi
Completely
xed
Dissatisfied
0
13.
satisfied
1
2
3
4
5
6
7
8
9
10
How Satisfied are you with HOW AUSTRALIA IS GOVERNED?
Completely
Mi
Completely
xed
Dissatisfied
0
14.
satisfied
1
2
3
4
5
6
7
8
9
10
How Satisfied are you with BUSINESS IN AUSTRALIA?
Completely
Mi
Completely
xed
Dissatisfied
0
15.
satisfied
1
2
3
4
5
6
7
8
9
10
How Satisfied are you with AUSTRALIA’S NATIONAL SECURITY?
Completely
Mi
Completely
xed
Dissatisfied
0
16.
satisfied
1
2
3
4
5
6
7
8
9
10
What was your household income over the past year?
Less than $15,000
6
General Discussion
$15,000 – $30,000
$30,000 – $60,000
$60,000 - $90,000
More than $90,000
Once again, please read the words below and enter a number from 0-10 in the box that
reflects the how you feel.
Dete
Ex
As
P
rmined
hausted
hamed
eaceful
Eag
Fat
Di
P
er
igued
stressed
lacid
Ener
La
Gu
P
getic
zy
ilty
leased
Enth
Let
Ho
Q
usiastic
hargic
stile
uiet
Using the same scale ranging from STRONGLY DISAGREE to STRONGLY AGREE,
please respond to each of the following statement by ticking the appropriate box that best
REPRESENTS YOUR OPINION.
1.
I am not a worrier.
Strongly
M
oderately
A
gree
Disagree
0
2.
1
2
3
4
6
7
8
9
10
M
oderately
A
gree
Disagree
0
1
2
3
4
5
Strongly
Agree
6
7
8
9
10
I often feel inferior to others.
Strongly
M
oderately
A
gree
Disagree
0
4.
Agree
I like to have a lot of people around me.
Strongly
3.
5
Strongly
1
2
3
4
5
Strongly
Agree
6
7
8
9
10
I laugh easily.
Strongly
M
oderately
A
gree
Disagree
0
1
2
3
4
5
Strongly
Agree
6
7
8
9
10
7
General Discussion
5.
When I’m under a great deal of stress, sometimes I feel like I’m going to pieces.
Strongly
M
oderately
A
gree
Disagree
0
6.
1
2
3
4
0
1
2
3
4
0
1
2
3
5
4
9
10
Strongly
Agree
6
7
8
9
10
5
Strongly
Agree
6
7
8
9
10
I really enjoy talking to people.
Strongly
M
oderately
A
gree
Disagree
0
1
2
3
4
5
Strongly
Agree
6
7
8
9
10
I often feel tense and jittery.
Strongly
M
oderately
A
gree
Disagree
0
1
2
3
4
5
Strongly
Agree
6
7
8
9
10
I like to be where the action is.
Strongly
M
oderately
A
gree
Disagree
0
11.
8
M
oderately
A
gree
Disagree
10.
7
I rarely feel lonely or blue.
Strongly
9.
6
M
oderately
A
gree
Disagree
8.
Agree
I don’t consider myself especially ‘light-hearted’.
Strongly
7.
5
Strongly
1
2
3
4
5
Strongly
Agree
6
7
8
9
10
Sometimes I feel completely worthless.
Strongly
M
oderately
A
gree
Disagree
0
1
2
3
4
5
Strongly
Agree
6
7
8
9
10
8
General Discussion
12.
I usually prefer to do things alone.
Strongly
M
oderately
A
gree
Disagree
0
13.
1
2
3
4
0
1
2
3
4
0
1
2
3
4
10
5
Strongly
Agree
6
7
8
9
10
5
Strongly
Agree
6
7
8
9
10
M
oderately
A
gree
Disagree
0
1
2
3
4
5
Strongly
Agree
6
7
8
9
10
I am a cheerful, high-spirited person.
Strongly
M
oderately
A
gree
Disagree
0
1
2
3
4
5
Strongly
Agree
6
7
8
9
10
Too often, when things go wrong, I get discouraged and feel like giving up.
Strongly
M
oderately
A
gree
Disagree
0
Strongly
9
I often get angry at the way people treat me.
Strongly
18.
8
M
oderately
A
gree
Disagree
17.
7
I often feel as if I’m bursting with energy.
Strongly
16.
6
M
oderately
A
gree
Disagree
15.
Agree
I rarely feel fearful or anxious.
Strongly
14.
5
Strongly
1
2
3
4
5
Strongly
Agree
6
7
8
9
10
I am not a cheerful optimist.
M
Strongly
9
General Discussion
oderately
A
gree
Disagree
0
19.
1
2
3
4
0
1
2
3
4
0
1
2
3
4
10
5
Strongly
Agree
6
7
8
9
10
5
Strongly
Agree
6
7
8
9
10
I often feel helpless and want someone else to solve my problems.
Strongly
M
oderately
A
gree
Disagree
0
1
2
3
4
5
Strongly
Agree
6
7
8
9
10
I am a very active person.
Strongly
M
oderately
A
gree
Disagree
0
1
2
3
4
5
Strongly
Agree
6
7
8
9
10
At times I have been so ashamed I just want to hide.
Strongly
M
oderately
A
gree
Disagree
0
24.
9
M
oderately
A
gree
Disagree
23.
8
My life is fast-paced.
Strongly
22.
7
M
oderately
A
gree
Disagree
21.
6
I am seldom sad or depressed.
Strongly
20.
5
Agree
1
2
3
4
5
Strongly
Agree
6
7
8
9
10
I would rather go my own way than be a leader of others.
Strongly
M
oderately
A
gree
Disagree
0
1
2
3
4
5
Strongly
Agree
6
7
8
9
10
10
General Discussion
Once again, please read the words below and enter a number from 0-10 in each box to
reflect how you feel.
Exci
N
I
R
ted
umb
rritable
elaxed
Insp
S
J
R
ired
leepy
ittery
estful
Inter
S
N
S
ested
luggish
ervous
erene
Live
S
S
S
ly
pent
cared
olemn
Using the same scale ranging from STRONGLY DISAGREE to STRONGLY AGREE,
please respond to each of the following statements by ticking the appropriate box that indicates
HOW YOU HAVE BEEN FEELING over the past week.
1.
I feel tense or ‘wound up’.
Strongly
M
oderately
A
gree
Disagree
0
2.
1
2
3
4
0
1
2
3
4
8
9
10
5
Strongly
Agree
6
7
8
9
10
M
oderately
A
gree
Disagree
0
1
2
3
4
5
Strongly
Agree
6
7
8
9
10
I can laugh and see the funny side of things.
Strongly
M
oderately
A
gree
Disagree
0
Strongly
7
I get a sort of frightened feeling as if something awful is about to happen.
Strongly
5.
6
M
oderately
A
gree
Disagree
4.
Agree
I still enjoy the things I used to enjoy.
Strongly
3.
5
Strongly
1
2
3
4
5
Strongly
Agree
6
7
8
9
10
Worrying thoughts go through my mind.
M
oderately
Strongly
11
General Discussion
Disagree
A
Agree
gree
0
6.
1
2
3
4
0
1
2
3
4
0
1
2
3
4
10
5
Strongly
Agree
6
7
8
9
10
5
Strongly
Agree
6
7
8
9
10
I feel as if I am slowed down.
Strongly
M
oderately
A
gree
Disagree
0
1
2
3
4
5
Strongly
Agree
6
7
8
9
10
I get a sort of frightened feeling like ‘butterflies’ in the stomach.
Strongly
M
oderately
A
gree
Disagree
0
1
2
3
4
5
Strongly
Agree
6
7
8
9
10
I have lost interest in my appearance.
Strongly
M
oderately
A
gree
Disagree
0
11.
9
M
oderately
A
gree
Disagree
10.
8
I can sit at ease and feel relaxed.
Strongly
9.
7
M
oderately
A
gree
Disagree
8.
6
I feel cheerful.
Strongly
7.
5
1
2
3
4
5
Strongly
Agree
6
7
8
9
10
I feel restless as if I have to be on the move.
Strongly
M
oderately
A
gree
Disagree
0
1
2
3
4
5
Strongly
Agree
6
7
8
9
10
12
General Discussion
12.
I look forward with enjoyment to things.
Strongly
M
oderately
A
gree
Disagree
0
13.
1
2
3
4
Agree
6
7
8
9
10
I get sudden feelings of panic.
Strongly
M
oderately
A
gree
Disagree
0
14.
5
Strongly
1
2
3
4
5
Strongly
Agree
6
7
8
9
10
I can enjoy a good book or radio or TV programme.
Strongly
M
oderately
A
gree
Disagree
0
1
2
3
4
5
Strongly
Agree
6
7
8
9
10
For the final part of the questionnaire, please read the words below and enter a number
from 0 to10 in each box to reflect how you feel.
Prou
d
Tir
ed
Stro
ng
W
eary
Vig
orous
T
ense
U
pset
W
orn out
Stil
l
U
ptight
Tra
nquil
Unt
roubled
Thankyou very much for your participation!
Kind regards,
13
General Discussion
Vanessa Cook.
14
General Discussion
Appendix C
Study 3, Questionnaire One
15
General Discussion
1
16
General Discussion
2
17
General Discussion
3
18
General Discussion
4
19
General Discussion
Appendix D
Study 3, Questionnaire Two
20
General Discussion
1
21
General Discussion
2
22
General Discussion
3
23
General Discussion
4
24
General Discussion
Appendix E
Means and Standard Deviations of all Study 1 items
N
Mean
Std.
Valid
S
et A
Active
98
90
78
Attentive
90
1
81
Drained
1
84
Drowsy
1
83
Dull
1
79
Afraid
82
Aggravate
d
1
80
Agitated
1
78
Anxious
.4837
478
.6612
526
1
.7821
478
1
.6374
395
2
.0389
596
1
.7978
087
2
2
.69030
2
.88421
2
.52770
2
.36179
2
.41384
2.2
2
.54692
2.1
8717
2.8
.01972
2.1
7044
2.4
2
2.2
8744
2.3
.56942
2.6
3670
2.0
2
2.9
4263
1.9
.71673
2.5
3085
2.5
1
1.8
0528
3.5
2
2
2
948
.14949
2.5
4860
2.5
3
2
30
1
.0829
2
28
948
2
1.5
4996
7.1
2
2
28
066
.2526
1.7
8478
5.5
7
2
30
1
.8483
2
28
498
S
et B
8877
7.8
5
2
30
979
.8895
Set
A
7.2
7
2
32
B
.6263
2
31
Set
7
2
29
1
Bored
2
33
1
S
et A
35
1
Aroused
S
et B
1
Alert
Deviation
2.5
2
.61636
2
25
General Discussion
86
At ease
32
1
95
Calm
88
Contented
94
t
1
74
Determine
d
87
Eager
24
1
84
Energetic
1
83
Enthusiast
ic
1
90
Exhausted
81
Fatigued
1
84
Lazy
79
Lethargic
1
76
Ashamed
76
Distressed
1
81
Guilty
2
244
.8798
315
7
.3579
514
3
.3204
550
3
.4130
475
2
.1955
333
2
.2898
955
.
9489
406
1
.6519
667
.
2
.19345
2
.14844
2
.72371
2
.69237
2
.46877
2
.52023
2
.67010
2.4
4162
1.3
.18031
1.8
9215
2.1
2
2.4
1451
1.6
.04365
2.4
8609
2.5
2
2.8
4827
2.3
.03778
2.9
0728
3.5
3
1.9
1301
3.5
.33573
1.8
8306
6.8
2
1.9
2333
6.5
.44514
1.7
1909
6.6
6
2
16
1
.9837
2
17
036
2
2.9
6753
7.3
6
2
20
1
.7433
2
19
954
.50634
2.3
0969
4.4
7
2
21
1
.5115
2
18
598
2
2.2
4317
7.0
4
2
22
1
.2629
2
22
554
.73930
2.2
9156
6.7
7
2
21
830
.0160
2
7521
6.7
7
2
18
1
.1077
2
34
750
7
2
33
1
Nonchalan
2
35
1
.6398
1.7
2
.83664
2
26
General Discussion
76
Hostile
13
1
75
Peaceful
91
Placid
80
1
82
Quiet
84
Excited
25
1
79
Inspired
1
79
Interested
2
00
Lively
80
Numb
1
75
Sleep
78
Sluggish
1
81
Spent
76
Irritable
1
77
Jittery
2
316
.4134
913
7
.5800
060
6
.5667
358
1
.0857
600
2
.6180
564
2
.5635
159
2
.0000
180
2
.3503
434
1
2
.39699
1
.93946
2
.17165
2
.25935
2
.68493
2
.55682
2
.73690
2.5
7201
1.7
.41056
2.4
2133
2.7
2
2.7
3711
2.5
.47568
2.5
3787
2.4
2
1.9
2042
2.6
.18270
2.1
4059
1.5
2
1.7
5184
6.2
.55275
2.1
8191
7.4
2
2.3
4821
5.9
.41481
2.5
5962
5.6
6
2
26
1
.8603
2
22
778
2
2.1
8953
6.1
5
2
26
1
.9837
2
27
441
.30036
2.5
6686
6.6
5
2
25
1
.8297
2
29
757
2
2.2
3724
6.1
6
2
34
1
.0944
2
29
009
.32669
2.0
8910
6.8
6
2
28
579
.0052
2
4501
1.4
7
2
22
1
.2343
2
22
380
1
2
26
1
Pleased
2
14
1
9205
2.1
2
.52729
2
27
General Discussion
74
Nervous
27
1
79
Scared
75
Relaxed
99
1
80
Serene
78
Solemn
26
1
73
Proud
1
91
Strong
1
89
Vigorous
81
Tired
1
85
Weary
80
Worn Out
1
81
Tense
86
Upset
1
79
Uptight
2
369
.5654
856
7
.4815
854
6
.5414
129
3
.8541
897
3
.3389
792
2
.7569
589
2
.4462
696
1
.9497
843
2
2
.13971
2
.20234
2
.29219
2
.85710
2
.83590
3
.01688
2
.74601
2.3
0113
2.5
.76911
2.5
7006
2.3
2
3.0
0676
2.9
.57885
2.7
8697
3.4
2
2.8
3903
3.7
.46630
2.2
1728
4.1
2
1.8
4965
6.0
.41257
2.0
7102
6.7
2
2.6
9422
7.3
.15089
2.6
7324
4.4
7
2
29
1
.9075
2
30
973
2
2.3
9420
5.5
3
2
31
1
.8146
2
31
879
.65596
2.3
1148
5.8
5
2
32
1
.1278
2
33
853
2
1.9
2682
6.3
6
2
33
1
.7638
2
36
727
.35354
2.1
2393
1.5
6
2
22
043
.2000
2
0426
2.2
1
2
32
1
.8324
2
31
885
1
2
27
1
Restful
2
30
1
.4080
2.4
2
.52721
2
28
General Discussion
80
Still
31
1
84
Tranquil
85
Untrouble
d
90
of living
2
37
Health
37
Achievem
ents
46
2
37
Personal
r’ships
2
37
How safe
you feel
2
37
Communit
y conn.
37
Future
security
2
37
NEO1
35
NEO2
2
33
NEO3
34
NEO4
2
34
NEO5
2
122
.5593
431
7
.5254
837
6
.7712
593
6
.5509
780
4
.1957
868
4
.9099
066
3
.6966
844
7
.1581
222
3
2
.21931
1
.81314
2
.15055
2
.34028
2
.77953
2
.40343
2
.80423
2.2
7050
4.5
.96392
2.8
1889
7.2
1
2.3
2593
3.5
.26808
2.5
0725
4.8
2
2.1
2754
4.3
.00899
1.9
2004
6.3
2
1.7
8855
6.4
.78271
2.1
5904
7.4
2
1.7
5529
7.4
.67993
2.0
4214
7.0
7
2
43
2
.1312
2
43
691
2
1.8
5980
6.5
7
2
43
2
.8601
2
43
707
.80407
2.7
8267
7.1
6
2
46
2
.3771
2
46
574
2
2.7
7434
5.5
7
2
46
2
.0526
2
46
043
.74166
2.9
3000
5.4
6
2
46
922
.7081
2
9923
4.8
5
2
46
2
.8967
2
35
628
4
2
35
1
Standard
2
32
1
.1278
2.5
2
.24713
3
29
General Discussion
34
NEO6
43
2
34
NEO7
34
NEO8
33
2
34
NEO10
34
NEO11
43
2
35
NEO12
2
35
NEO13
2
35
NEO14
35
NEO15
2
35
NEO16
35
NEO17
2
35
NEO18
35
NEO19
2
35
NEO20
2
893
.8128
008
3
.8936
664
5
.1362
508
3
.5957
795
6
.2723
648
3
.0936
992
6
.1362
852
3
.8468
402
5
2
.58291
2
.81653
2
.38256
2
.69499
2
.31699
2
.74109
2
.67421
2.6
5421
4.7
.92429
2.5
9325
4.3
2
2.3
5152
6.3
.48674
2.1
8637
3.3
2
2.5
9733
6.3
.68477
2.3
9804
3.9
2
2.5
5900
4.9
.11913
2.5
2489
4.2
2
2.6
8852
4.7
.85845
2.3
2458
2.8
4
2
44
2
.6511
2
44
453
2
2.4
9619
5.0
2
2
43
2
.1453
2
44
374
.57588
2.2
1071
3.3
5
2
44
2
.8248
2
44
979
2
2.5
2631
7.4
2
2
44
2
.2189
2
44
749
.05756
2.3
5515
3.6
7
2
44
058
.2991
2
7951
5.2
3
2
43
2
.3675
2
43
391
5
2
43
2
NEO9
2
43
2
.8590
2.7
2
.80708
2
30
General Discussion
35
NEO21
43
2
36
NEO22
36
NEO23
35
2
36
HAD Q1
36
HAD Q2
44
2
36
HAD Q3
2
37
HAD Q4
2
37
HAD Q5
37
HAD Q6
2
35
HAD Q7
37
HAD Q8
2
37
HAD Q9
36
HAD Q10
2
36
HAD Q11
2
959
.5359
463
2
.0591
397
4
.5527
661
2
.8979
860
2
.6582
298
4
.6920
463
2
.2839
711
2
.1737
603
3
2
.84036
2
.08542
2
.71842
2
.19073
2
.41521
2
.65460
2
.81578
2.4
5112
3.8
.29958
2.3
8254
2.7
2
2.7
1727
2.9
.85323
2.2
2592
4.9
2
2.0
8735
2.9
.77738
2.5
2998
3.1
2
1.8
0767
5.0
.64260
2.6
6406
2.2
2
2.1
8454
2.9
.60060
2.6
8203
2.9
2
2
42
2
.5805
2
42
459
2
2.7
4775
3.7
2
2
42
2
.1907
2
42
686
.49704
2.1
2431
4.7
3
2
42
2
.9449
2
42
656
2
2.2
6153
2.0
4
2
42
2
.4426
2
42
639
.87459
2.4
2238
6.1
1
2
44
008
.5678
2
0262
2.7
6
2
42
2
.4873
2
44
942
2
2
44
2
NEO24
2
44
2
.1574
2.6
2
.68398
2
31
General Discussion
37
HAD Q12
42
2
35
HAD Q13
35
HAD Q14
34
2
37
Total
Neuroticism
28
Total
Extraversion
2
28
Total
Anxiety
38
2
33
Total
Depression
2
31
.2949
1.1077
38
8.9561
43.
0252
6
8.6667
68.
0711
2
0.4635
24.
1213
1
7.8615
69.
19.
7941
.91270
2
.88083
1.6
8132
3089
3
2
1.2
1
2.1
6913
479
7
2
39
337
.89765
1.8
7053
2.6
1
2
39
239
.8043
2
1857
2.4
1
2
46
2
.2936
2
42
802
2
2
43
2
PWI
2
43
2
.4346
1
.59502
13.
69809
1
4.58104
18.
13089
2
1.31835
15.
62877
1
5.94605
12.
01650
1
4.33593
9.6
3543
9
.66767
32