M E T H O D O L O G I C A L I S S UE S I N N U R S I N G R E S E A R C H
Psychometric evaluation of the Cognitive Appraisal of Health Scale
with patients with prostate cancer
Muayyad M. Ahmad
BSc MSc PhD
Assistant Professor, Faculty of Nursing, University of Jordan, Amman, Jordan
Submitted for publication 24 July 2003
Accepted for publication 7 May 2004
Correspondence:
Muayyad Ahmad,
Faculty of Nursing,
University of Jordan,
Amman,
Jordan.
E-mail: mma4@ju.edu.jo
Journal of Advanced Nursing 49(1), 78–86
Psychometric evaluation of the Cognitive Appraisal of Health Scale with patients
with prostate cancer
Aim. This study was designed to investigate the psychometric properties of the
Cognitive Appraisal of Health Scale, specifically with prostate cancer patients.
Background. Measurement of appraisal is considered a relatively new area in the
health studies literature. Cognitive appraisal of potentially stressful events becomes
important when individuals face a crisis such as a change in their health status. So
far, confirmatory factor analysis (CFA) has not been used to study the factor
structure of the Cognitive Appraisal of Health Scale.
Methods. The structure of the questionnaire was analysed by exploratory factor
analysis, CFA using structural equation modelling and an Analysis of Moment
Structure procedure with a sample of 133 patients with prostate cancer.
Results. The results support the three-factor model for the Cognitive Appraisal of
Health Scale because it has robust structure and excellent goodness-of-fit indices.
Several of the 23 items were grouped into different factors from those in Kessler’s
scale.
Conclusion. The use of the reduced version of Cognitive Appraisal of Health Scale
with men under health stress is recommended.
AHMAD M.M. (2005)
Keywords: Cognitive Appraisal of Health Scale, appraisal, prostate cancer, nursing,
confirmatory factor analysis
Introduction
Cognitive appraisal
Cognitive appraisal is the process by which an individual
evaluates or judges a potentially stressful event for meaning
and significance to their own well-being (Lazarus & Folkman
1984). Cognitive appraisal of potentially stressful events
becomes important when people face crises such as a change
in their health status; however, cognitive appraisal has not
been well studied (Kessler 1998). In cognitive appraisal, a
person evaluates the effect of an encounter with the environment on their well-being. Such an appraisal has three forms:
(1) irrelevant, when the encounter with the environment has
78
no effect on well-being, (2) benign-positive, when the
encounter with the environment is perceived as positive and
(3) stressful. Additionally, there are three types of stress
appraisal: (a) harm or loss, referring to damage already
experienced, whether from an illness or damage to the self or
social esteem; (b) threat, referring to harm or loss that has not
yet occurred but is anticipated and (c) challenge, when the
appraisal provides an opportunity for growth, mastery, or
gain (Coyne et al. 1981, Lazarus & Folkman 1984, Lazarus
1998). Lazarus and Folkman (1984) described the meaning of
stress in terms of appraisal; in their study, they asked whether
the stress-inducing event was perceived as a harm/loss, threat,
or challenge, and whether or not it was perceived as
controllable.
2005 Blackwell Publishing Ltd
Methodological issues in nursing research
Psychometric evaluation of the CAHS
When people face a potentially stressful situation, such as a
cancer diagnosis, cognitive appraisal is carried out by the
mental operations of thinking and reasoning (Lazarus &
Folkman 1984). Several studies report that a cancer diagnosis is
usually appraised as a stressful event that poses a threat greater
than that of other serious diseases (Krause 1991, Lev 1992).
In a study examining how cancer patients coped with their
disease, Krause (1991) reported that 82 such patients (68%)
described their feelings in response to the diagnosis as horror,
shock, terror, surprise, anger, a sense of time standing still,
emptiness, a sense of unreality, punishment and fate. In a
study of patient strategies for adapting to a cancer diagnosis,
Lev (1992) reported that this can be associated with pain,
disfigurement, and death. Thus, patient reactions to a cancer
diagnosis tend to be very intense, with profound psychological reactions such as shock, disbelief, denial, anger, sadness,
depression and grief (Frank-Stromborg 1989, Fawzy 1995).
However, these responses in previous studies need to be
evaluated with appraisal measurements that are specific to
the type of appraisal used in each situation., since Lazarus
and Folkman (1984) reported that each type of appraisal is
expected to stimulate different types of coping strategies. No
reports were found in the literature about how prostate
cancer patients appraise their diagnosis and its treatment
consequences.
addition to this setting limitation, the author did not establish
validity of this scale.
Mishel and Sorenson (1991) used the 15-item appraisal
scale that was a part of the Ways of Coping Checklist
(Folkman 1984). They collapsed the four appraisal subscales
of threat, challenge, harm, and benign/irrelevant into two
categories of danger and opportunity. These two scales limit
the measurement of primary appraisal only to the emotional
responses encountered in stressful events. Another scale, the
Appraisal of Caregiving Scale (ACS) was developed by
Oberst et al. (1989) to measure the extent to which cancer
patient caregivers perceive the intensity of the illness/caregiving situation as a threat, challenge, harm/loss, or benign.
The ACS reflects the construct of primary appraisal; however,
its use is limited to studies of caregiver stress.
The literature is inconsistent when it comes to assessing the
necessity for coping in a situation appraised as irrelevant or
benign positive. According to Lazarus and Folkman (1984),
when an event is appraised as in this way, it does not require
coping. When a diagnosis of cancer is appraised as stressful, it
will evoke a number of responses (Menaghan 1982, Folkman
& Lazarus 1988). On the contrary, Kessler (1998) considered
that, even under non-stressful situations, coping occurs under
benign/irrelevant appraisals. Therefore, the Cognitive
Appraisal of Health Scale (CAHS) as developed by Kessler
includes all types of appraisals and is arguably more
comprehensive.
Measurement of appraisal
The instrument
Measurement of appraisal is considered a relatively new area
of study, as most of appraisal scales were developed only
within the past 15 years. A number of researchers are
responsible for defining the field of study. Gass (1988), in
his study, used one question to assess appraisal of bereavement, and reported that this question was developed based on
Lazarus and Folkman’s theoretical perspective because there
were no other measures of appraisal available. However,
evaluating individuals’ appraisal using a single category or
item is not appropriate because primary appraisal is considered a multidimensional concept (Kessler 1998).
Oberst et al. (1991) developed the Appraisal of Illness Scale
(AIS) to study stress appraisal in cancer patients in relation to
threat, loss, financial strain, and overall stressfulness; however, the validity and reliability of the AIS are not reported. In
order to measure stress appraisal, Kammer (1994) has
reviewed the work of Folkman and Lazarus to establish the
Emotion Appraisal of Nursing Home Placement Tool
(EANH). This was developed to assess how family members
think about the nursing home placement of their elders. In
The need for an instrument to measure appraisals by
individuals with a variety of potentially stressful healthrelated events led to the construction of the CAHS (Kessler
1998). In the early development stages of the primary
appraisal portion of the CAHS, the scale consisted of 27 items
derived from theoretical literature on appraisal and from other
existing instruments on appraisal. The 27-item cognitive
appraisal was administered to a convenience sample of 201
women at 0Æ3–21 years after diagnosis with breast cancer
(Kessler 1998). The ages of the participants ranged from 29 to
82 years. Participants were asked to respond to each item
according to their appraisal of their health status. The 27 items
with four separate scales measure the cognitive appraisal
dimensions of threat, challenge, harm/loss and benign/irrelevant. All items are scored on a 5-point Likert scale from 1
(strongly disagree) to 5 (strongly agree). Higher scores indicate
more agreement with the appraisal item or scale.
Four out of five items in the CAHS were developed by
Folkman et al. (1986); three of these four items represent the
coping options associated with threat, challenge and harm/loss.
Appraisals and cancer diagnosis
2005 Blackwell Publishing Ltd, Journal of Advanced Nursing, 49(1), 78–86
79
M.M. Ahmad
The fourth item was added by Kessler (1998) to the instrument
to represent the coping options associated with a benign/
irrelevant appraisal. However, the Benign/irrelevant appraisal
showed no effect on the outcome variables (coping and health
status) in a prostate cancer study (Bjorck et al. 1999). In
addition, the CAHS as developed by Kessler (1998) is still
considered a new instrument to measure cognitive appraisal;
therefore, further examinations of the instrument were needed.
The CAHS as developed by Kessler (1998) depended solely
on exploratory factor analysis (EFA). However, EFA is
primarily useful at the early stages of analysis (Tabachnick &
Fidell 2001). Scales are developed from the items loading
high together on the same factor while loading low on all
other factors (Hair et al. 1998). In addition, exploratory
factor models do not provide any explicit test statistics for
assessing convergent and discriminant validity (O’LearyKelly & Vokurka 1998). Nothing was found in the literature
about how prostate cancer patients appraise their diagnosis
and its treatment consequences. The stability of a newly
developed instrument, such as the CAHS, with different
gender and different diagnosis merits further examination.
Participants
The convenience sample for the study was composed of
133 patients, recruited from two university-affiliated hospitals, where they were diagnosed with and treated for prostate
cancer. The eligibility criteria were men of any age: (a) who
had been diagnosed with prostate cancer and (b) with any
stage of prostate cancer. Any patient with a cancer diagnosis
other than prostate cancer, except where it was secondary to
prostate cancer, was excluded.
After obtaining informed consent, data were collected via
mailed questionnaires. A total of 146 men completed and
returned their packets, but 13 were excluded because they
had other types of cancer. The demographic characteristics of
the participants are shown in Table 1.
Instrument
The CAHS used in this study has 27 items, and responses are
made using a 5-point Likert scale from 1 (strongly disagree)
to 5 (strongly agree). Higher scores indicate more agreement
with the appraisal item or scale.
Confirmatory factor analysis
Confirmatory factor analysis (CFA) is a relatively recent
technique that is rapidly replacing the more traditional
technique of EFA because it allows more precise testing of an
instrument’s factor structure. CFA provides a theory-driven
method for addressing construct validity by assigning the items
in an instrument to their respective factors according to
theoretical expectations (Aluja et al. 2003). Moreover, CFA
enables the researcher to evaluate the reliability of the
instrument in an approach that differs from traditional
assessment of internal consistency (e.g. Cronbach’s alpha)
through partialling out the measurement error (Munro 2001).
CFA also enables the researcher to investigate the factor
structures of the instrument across groups. For example, to
examine gender differences between this study and the original
study by Kessler (1998) in the factor structure of CAHS, CFA
can be used to determine whether the structure changes or is
invariant across groups. So far, CFA has not been used to study
the factor structure of the CAHS as obtained by EFA.
The study
Purpose
The purpose of this study was to use exploratory and CFA
to evaluate the appropriateness of the CAHS in examining
how prostate cancer patients appraise their diagnosis.
80
Ethical considerations
Ethical approval was granted by the Research Ethics
Committees of the study hospitals. The purpose, risks and
benefits were explained to recruits before they decided to
Table 1 Demographic Characteristics’ of the Participants (n ¼ 133)
Characteristic
Age
Range
Mean (SD )
Race
White
Non-white
Marital status
Married
Single, divorced, widower, separated
Education
High school or less
College graduate
Graduate school
Employment status
Full-time
Part-time
Retired
Income
Inadequate
Adequate
Very adequate
n (%)
47–91
66Æ3 (9Æ1)
115 (86Æ0)
18 (14Æ0)
107 (80Æ5)
26 (19Æ5)
46 (34Æ6)
61 (45Æ9)
26 (19Æ5)
49 (36Æ8)
8 (6Æ0)
76 (57Æ2)
13 (9Æ8)
85 (63Æ9)
35 (26Æ3)
2005 Blackwell Publishing Ltd, Journal of Advanced Nursing, 49(1), 78–86
Methodological issues in nursing research
participate. They were assured that their participation was
completely voluntary and that they could discontinue it at
any time without affecting their current or future relationship
with the treating hospital and/or physician.
Results
A principal components analysis with Varimax rotation for
the prostate cancer patients’ cognitive appraisal was
performed with the original 23 items of the CAHS as
presented by Kessler (1998). Forcing a four-factors solution,
53Æ84% of the total variance was explained (Table 2). The
bolded items under Factor 1, in comparison with Kessler’s
(1998) model, include variables from benign/irrelevant
appraisal (a15, a28, a23, a5), items from threat appraisal
(a17, a18), and an item from harm/loss appraisal (a25). The
bolded items under Factor 2 are close to the items loaded in
Kessler’s scale under harm/loss appraisal beside the item (a25).
The bolded items under Factor 3, in comparison with Kessler’s
model, refer to threat appraisal (a6, a7) and challenge
Psychometric evaluation of the CAHS
appraisal (a2). The bolded items under Factor 4 refer to
challenge appraisal in Kessler’s model in addition to item (a2).
Determining the number of factors to be extracted depends
on how strongly and cleanly the variables load on the factors,
so the analyst may decrease or increase the number of factors
(Youngblut 1993). The variable will load strongly in a
particular factor if loading ‡0Æ40, and is considered clean if
the absolute difference between the loading is more than 0Æ20
(Nunnally & Bernstein 1994). It is obvious that many items
had non-clean loading (a25, a5, a9, a10, a14).
Consistent with the Lazarus and Folkman (1984) theoretical model from which the CAHS was drawn, the benign/
irrelevant factor was deleted and further exploratory factor
analyses were conducted on a three-factor model. Accordingly, non-clean loading items were deleted from the model
(a7, a6, a2, a28, a25, a14), and other items were re-allocated
to other factors to fit the theoretical meaning (a5, a23), as
scores were reversed. In addition, one item (a15) was deleted
because of its redundancy with another item (a17), leaving
the model with 16 items.
Table 2 Exploratory factor analysis of the CAHS four-factor model, 23 items with patients with prostate cancer
Principal components with Varimax rotation
Items
Factor 1
Factor 2
Factor 3
Factor 4
a17. I have a lot to lose because of this health problem
a15. I have nothing to lose because of this health problem
a18. I worry about what will happen to me
a28. This health problem doesn’t affect my life
a23. I don’t think much about this health problem
a4. The health problem is frightening to me
a25. I have been hurt by this health problem
a5. This health problem isn’t stressful to me
a13. I have a sense of loss over the things I can no longer do
a9. I have lost interest in the things around me
a8. This health problem has damaged my life
a3. I have not been able to do what I want to do because of this health problem
a19. Relationships with my family and friends have suffered
a21. I have been harmed in some way by this health problem
a10. I have had to give up a great deal because of this health problem
a6. Things will only get worse because of this health problem
a7. This health problem will not go well
a2. This health problem won’t get me down
a26. There is a lot I can do to overcome this health problem
a11. I can beat this health problem despite the difficulties
a24. This health problem has caused me to learn more about myself
a1. I can control what will happen to me
a14. I feel I can handle this health problem
% Variance*
Cumulative variance
0Æ70
0Æ68
0Æ67
0Æ66
0Æ66
0Æ57
0Æ55
0Æ45
0Æ27
0Æ16
0Æ26
0Æ16
0Æ12
0Æ32
0Æ14
0Æ16
0Æ19
0Æ31
0Æ20
0Æ15
0Æ19
0Æ10
0Æ16
0Æ17
0Æ13
0Æ26
0Æ31
0Æ17
0Æ29
0Æ18
0Æ12
0Æ10
0Æ54
0Æ15
0Æ74
0Æ65
0Æ69
0Æ68
0Æ62
0Æ60
0Æ51
0Æ21
0Æ10
0Æ26
0Æ13
0Æ23
0Æ21
15Æ94
15Æ94
0Æ11
15Æ76
31Æ70
0Æ30
0Æ17
0Æ46
0Æ18
0Æ28
0Æ12
0Æ51
0Æ70
0Æ67
0Æ67
0Æ17
0Æ25
0Æ36
0Æ21
0Æ35
0Æ41
11Æ70
43Æ40
0Æ13
0Æ18
0Æ77
0Æ71
0Æ61
0Æ52
0Æ51
10Æ45
53Æ85
*Percentage of the variance accounted for each factor. Rotation Sums of Squared Loadings.
items load < 0Æ10.
Loading not clean.
2005 Blackwell Publishing Ltd, Journal of Advanced Nursing, 49(1), 78–86
81
M.M. Ahmad
Confirmatory factor analysis is a special application of
structural equation modelling (SEM), making it important
to screen data for multivariate normality when performing
CFA (Hair et al. 1998). The multivariate normality of the
data was examined by conducting normality checks by
using Analysis of Moment Structure (AMOS 5.0) software
(Arbuckle 2003). Normality is usually rejected if the ratio
of kurtosis is higher than ±2 and/or skewness is higher
than ±1, which indicates a distribution that departs
significantly from normality (Nunnally & Bernstein
1994). The skewness for items a9, a10, and a24 were
1Æ68, 3Æ49 and 1Æ02 respectively. Kurtosis for item a9 was
2Æ78 and for item a10 was 20Æ32. Thus, three items
(a9, a10, a24) were dropped from the model. Table 3
shows the most interpretable form of the factor analysis
that was performed with the remaining 13 items and three
factors that have the maximum variance accounted for
(55Æ48%).
Table 3 Exploratory factor analysis of the three-factor model of the
Cognitive Appraisal of Health Scale 13 items
Principal components with Varimax
rotation
Items
a13
a3
a8
a21
a19
a23
a18
a4
a17
a5
a11
a1
a26
% Variance*
Cumulative variance
Harm/loss
Threat
0Æ82
0Æ76
0Æ65
0Æ63
0Æ60
0Æ18
0Æ22
0Æ14
0Æ18
0Æ21
0Æ32
0Æ17
0Æ22
0Æ73
0Æ72
0Æ68
0Æ67
0Æ55
34
20Æ80
20Æ80
Challenge
13
25
23
14
14
0Æ82
0Æ77
0Æ73
15Æ14
55Æ48
-0Æ21
19Æ54
41Æ34
*Percentage of the variance accounted for each factor. Rotation Sums
of Squared Loadings.
Items load < 0Æ10.
The Kaiser–Meyer–Oklin (KMO) is a measure that provides
an approach to comparing the zero-order correlations to the
partial correlations between pairs of variables (Munro 2001).
The KMO in the 13-item three factors model is 0Æ82; Kaiser
(1974) stated that if KMO is >0Æ50 it is acceptable. The closer
the KMO to one, the better the correlations between pairs of
variables that can be explained by the other variables (Norusis
1998). Bartlett’s Test of Sphericity evaluates all factors
together and each factor separately against a hypothesis stating
that there are no factors (Tabachnick & Fidell 2001). The
Bartlett’s Test in this study is significant (P < 0Æ001),
indicating that enough shared variance is present.
Item-total correlation refers to a correlation of an item or
indicator with the composite score (total) of all the items
forming the same construct. Items from a given scale
exhibiting item-total correlations <0Æ50 are usually candidates for deletion (Hair et al. 1998). All items in the three
subscales of CAHS demonstrated item-total correlations
between 0Æ62 and 0Æ82.
Confirmatory factor analysis was carried out over the
variance–covariance matrix for the 23-item, 16-item and 13item three-factor models through the AMOS 5.0 statistical
package (Arbuckle 2003). The estimation method was the
Maximum Likelihood. In order to achieve model identification, regression coefficients of the error terms over the
endogenous variables were fixed to 1. The CFA was
performed in order to determine whether the hypothesized
statistical model fitted the actual data set, and a number of
‘goodness-of-fit’ statistics were used on the three factor
models derived by means of EFA (Table 4).
A non-significant chi-square result is an indication of fit
because the researcher seeks to confirm the null hypothesis
(i.e. the model fits the data well) (Byrne 1994). However, the
chi-square goodness of fit is inadvisable because it is greatly
influenced by sample size and violation of multivariate
normality (Joreskog & Sorbom 1993, Wang et al. 1996).
Therefore, chi-square statistics should not be the only method
used for drawing conclusions about data-model fit (Bollen &
Long 1993).
Other commonly used fit statistics include the goodness
of fit index (GFI), comparative fit index (CFI), and
Models
v2
d.f.
P
v2/d.f.
GFI
AGFI
CFI
IFI
RMSEA
M1
M2
M3
476Æ51
154Æ04
64Æ26
224
101
62
<0Æ01
<0Æ01
0Æ40
2Æ13
1Æ53
1Æ03
0Æ75
0Æ87
0Æ93
0Æ69
0Æ83
0Æ90
0Æ76
0Æ90
0Æ99
0Æ76
0Æ91
0Æ99
0Æ09
0Æ06
0Æ02
Table 4 Goodness-of-Fit Indices for three
models in the prostate cancer patients
sample
v2/d.f., relative chi-square; GFI, goodness of fit index; AGFI, adjusted GFI; CFI, comparative fit
index; IFI, increment fit index; RMSEA, root mean square error of approximation; M1, four
factors (23 items); M2, three factors (16 items); M3, three factors (13 items).
82
2005 Blackwell Publishing Ltd, Journal of Advanced Nursing, 49(1), 78–86
Methodological issues in nursing research
incremental fit index (IFI), all with a range 0–1 and with
values >0Æ90 indicating a good fit. CFI is recommended
over GFI because it is less influenced by sample size (Wang
et al. 1996). The root mean square of approximation
(RMSEA) of 0Æ05 or less indicates a ‘close fit’, while values
of more than 0Æ1 justify rejecting the model (Browne &
Cudeck 1989). Therefore, it is best to consider a variety of
fit indices so that the weaknesses of a particular index are
counteracted by the strength of another (March et al.
1996).
The goodness-of-fit indices of the 23-item model were very
poor in general terms (Table 4). The 16-item model obtained
from the second EFA (includes item a9, a10 and a24) yielded
fit indices slightly better than the previous model. According
to chi square results, both of these models were rejected. The
13-item model yielded highly acceptable indices in all
respects. Therefore, the goodness-of-fit indicators support
the three factors 13-item model to measure the cognitive
appraisal.
Confirmatory factor analysis involves the specification
and estimation of one or more hypothesized models of
factors structure, each of which proposes a set of factors
(latent variables) to account for covariance among a set of
observed variables. SEM can be used to test the fit of a
hypothesized model against the sample data. Using CFA to
evaluate the appropriateness of the CAHS in examining how
patients with prostate cancer appraise their diagnosis
provides construct validity for the instrument by assigning
the items to their respective factors according to theoretical
expectations.
Following the convention of AMOS analysis (Arbuckle
2003), observed indicators (items) are enclosed in rectangles. Latent variables (factors) are enclosed in circles,
whereas measurement errors are enclosed in ellipses.
Referring to Figure 1, the structural model is identified
by three interrelated constructs (Threat, Challenge and
Harm/Loss) connected to each other with double-headed
arrows representing a pattern of intercorrelations. The
single-headed arrows leading from the latent constructs to
the boxes are regression paths representing the link
between the factors and their respective set of observed
variables; these coefficients represent factor loadings.
Finally, the single-headed arrows pointing from ellipses to
rectangles represent measurement error associated with
observed variables.
The Harm/Loss factor (five items) describes the damage
that has already occurred as perceived by patients with
prostate cancer, such as ‘This health problem has damaged
my life’ and ‘I have a sense of loss over the things I can no
longer do’. The Threat factor (five items) describes the harm
Psychometric evaluation of the CAHS
0·62
0·72
Threat
0·60
a17
a18
a4
0·59
0·46
a23
a5
0·38
e17
0·52
e18
0·36
e4
0·35
e23
0·22
e5
–0·25
0·77
0·68
0·62
Challenge
a26
e11
0·33
e1
0·39
e21
a3
0·37
e3
a13
0·56
a8
0·46
a19
0·38
a1
–0·48
0·61
Harmless
a21
0·75
0·68
0·62
e26
0·46
a11
0·57
0·63
0·59
e13
e8
e19
Figure 1 Standardized estimates for the 13-item three-factor
structure.
or loss anticipated by the patients that has not yet occurred,
such as ‘I worry about what will happen to me’ and ‘The
health problem is frightening to me’. The Challenge factor
(three items) involves a judgment made by the patients that
demands associated with a stressful encounter can be met
and overcome, such as ‘I can control what will happen to
me’ and ‘There is a lot that I can do to overcome this health
problem’.
The Cronbach’s alphas for the 13-item three-factors
model of CAHS in the present study were 0Æ79 for harm/
loss appraisal, 0Æ74 for threat appraisal, 0Æ70 for challenge,
and 0Æ70 for the total scale.
Internal validity of the CAHS three-factor model was
established by principal component factor analysis. Construct validity is confirmed through SEM by examining the
standardized regression coefficients in the regression of
observed variables on latent variables. The evidence that
the measured variables or factors represent the underlying
constructs becomes stronger when the factor loadings or
coefficients, as compared with their corresponding t-values,
become larger (Bollen 1989). As shown in Table 5, all the
items had t-values above 4Æ23 (P < 0Æ01) which means that
the three-factor model has strong constructs.
2005 Blackwell Publishing Ltd, Journal of Advanced Nursing, 49(1), 78–86
83
M.M. Ahmad
Table 5 Parameter estimates, error terms, and t-values for the threefactor model
Latent
variable
Unstandardized Standardized Error
Item factor loading factor loading term t-values*
Harm/loss a13
a19
a8
a21
a3
1Æ29
0Æ72
1Æ13
1
1Æ12
0Æ75
0Æ62
0Æ68
0Æ63
0Æ61
0Æ20
0Æ13
0Æ19
0Æ20
6Æ42
5Æ65
6Æ09
a
5Æ53
Threat
a18
a17
a4
a23
a5
1Æ13
1
0Æ99
1Æ03
0Æ73
0Æ72
0Æ62
0Æ60
0Æ59
0Æ46
0Æ20
5Æ65
0Æ19
0Æ20
0Æ17
5Æ24
5Æ19
4Æ23
Challenge a26
a11
a1
1
0Æ73
0Æ90
0Æ77
0Æ68
0Æ57
0Æ14
0Æ20
5Æ08
4Æ53
*t-values >2Æ58 considered significant at the 0Æ01 level.
Indicates a parameter fixed at 1Æ0 in the original solution.
Discussion
The measurement of unobserved (latent) variables is considered a recent phenomenon in nursing research. Most
available empirical research in nursing has been exploratory
in nature and has borrowed its methods extensively from
other fields, such as psychology and sociology. Traditional
exploratory techniques have been used to provide preliminary scales and assess measurement properties. These techniques are useful in the early stages of empirical enquiry
where theoretical models do not exist and the basic purpose
is exploration. Exploratory methods can help in developing
hypothesized measurement models, which subsequently can
be tested via confirmatory analytic technique. None of the
EFA techniques employed to assess the psychometric properties of scales tests unidimensionality. Unidimensionality
can be defined as the existence of one latent trait or
construct underlying a set of measures (Koufteros 1999).
CFA affords a stricter interpretation of unidimensionality
than can be provided by EFA (O’Leary-Kelly & Vokurka
1998), thus providing different conclusions about the
acceptability of the instrument.
According to Kessler (1998), the CAHS was used in her
study because it measures the multiple dimensions of
appraisals associated with the diagnosis of breast cancer
and its treatment consequences. However, further evaluation was needed for the instrument because, although it was
derived from the Lazarus and Folkman (1984) transactional
model, it was not consistent with that model. Kessler
(1998) assumed that cognitive appraisal would be initiated
84
from non-stressful events as well as stressful ones. As
evidence of this inconsistency with Kessler’s assumption, a
study that aimed to measure how patients with prostate
cancer appraise their diagnosis, the benign/irrelevant
appraisal was found to have no effects on any of the
outcome measures (Bjorck et al. 1999). This result is
consistent with what Lazarus and Folkman (1984) reported,
namely that coping strategies are stimulated when an event
is appraised as stressful (threat, harm/loss, and or challenge)
but not as benign/irrelevant.
A three-factor solution was chosen for this study. The
widely accepted assumption of parsimony (Kim & Mueller
1978) supports this decision. Furthermore, Nunnally and
Bernstein (1994) suggest that each factor needs at least three
items, and the three factors have met this criterion.
Moreover, in the Principal Components with Varimax
rotation, the three factor model accounted for more than
half of the variance (55Æ48%). Our results show that the
reduced version of 13 items improves the validity. Furthermore, internal consistency coefficients were acceptable in
the reduced version. Therefore, we consider that it would be
advisable to recommend using the reduced version model of
CAHS, especially with people experiencing a stressful health
encounter.
Conclusion
A cancer diagnosis is still associated with a life-threatening
disease by many people. However, different types of cancer
induce different levels of fear among individuals (American
Cancer Society 1996). When informed that they have prostate
cancer, patients may view this as a threat to their lives.
However, in terms of appraisal, men in the present study
identified threat appraisal with less frequency than appraisals
of harm/loss or challenge. It is likely that the high survival
rate among and long survival time for patients with prostate
cancer make living with the disease less threatening than
doing so with other types of cancer having lower cure rates
and/or shorter life expectancies. Although prostate cancer
treatment leaves some permanent sexual and urological sideeffects (Davison et al. 1995), the overall effects of the disease,
beyond the obvious complications, may influence how
patients estimate the threat level presented by their prostate
cancer.
In order to help their patients, nurses need to develop an
understanding of how prostate cancer diagnosis affects
people’s appraisal of their situation. Thus, nurses need to
help patients perceive the disease in ways that are less
threatening or less associated with harm/loss and work to
empower patients with information to face the disease as a
2005 Blackwell Publishing Ltd, Journal of Advanced Nursing, 49(1), 78–86
Methodological issues in nursing research
What is already known about this topic
• Measurement of appraisal is a relatively new area in the
health studies literature.
• Cognitive appraisal of potentially stressful events becomes important when individuals face a crisis such as a
change in their health status.
• Confirmatory factor analysis has not been used to study
the factor structure of the Cognitive Appraisal of Health
Scale.
What this paper adds
• Confirmatory factor analysis for the factor structure of
the Cognitive Appraisal of Health Scale did not support
the previous four-factor model.
• Exploratory factor analysis and confirmatory factor
analysis indicated that the 13-item scale is better than
the 23-item scale in measuring the cognitive appraisals
of patients with prostate cancer.
• The three-factor model is parsimonious, valid, reliable,
and empirically supported, and supports the work on
cognitive appraisal derived from the transactional
model of Lazarus and Folkman.
challenge. Detailed analyses of appraisal, such as that in this
study, will facilitate this.
Acknowledgements
I am indebted to the University of Jordan, approval number
(184/2001–2002) for funding, and acknowledge the support
of the Deanship of Academic Research at the university. The
invaluable contributions of Dr Elizabeth Madigan and
Mr Rafat Qahoush are also appreciated.
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