Personality and Individual Differences 35 (2003) 569–591
www.elsevier.com/locate/paid
A prospective longitudinal investigation of social problemsolving appraisals on adjustment to university, stress, health,
and academic motivation and performance
Sarah R. Baker*
Department of Psychology, Keele University, Staffordshire, ST5 5BG, UK
Received 2 November 2001; received in revised form 26 April 2002; accepted 23 July 2002
Abstract
A prospective longitudinal design was used to examine the predictive relations between social
problem-solving appraisals and subsequent adjustment, stress, health, motivation and performance in
a sample of university students during their three years at university. Controlling for gender, age and
prior academic aptitude, self-perceived problem-solving abilities, measured on entry to university,
had direct beneficial effects on psychosocial adjustment to university, perceived stress levels, selfdetermined motivational orientations, and academic performance during students’ second year of
study. Additionally, social problem-solving appraisals, adjustment to university and intrinsic motivation
towards accomplishment predicted higher marks over the course of students’ 3 years at university,
controlling for university entry qualifications. Gender differences emerged in perceived stress, selfdetermination profiles, and academic performance, with women displaying higher scores than men.
More specific analysis of problem-solving appraisals indicated that different dimensions (e.g. control,
confidence) had different long-range adaptational outcomes. Results are discussed with reference to
models of social problem-solving (D’Zurilla, 1986, 1990) and self-determination (Deci & Ryan, 1985, 1991)
and implications for interventions based on problem-solving training for stress management are
considered.
# 2003 Elsevier Ltd. All rights reserved.
Keywords: Social problem-solving appraisals; Adjustment; Motivation; Performance; Stress; Students
* Tel.: +44-1782-583392; fax: +44-1782-583387.
E-mail address: s.r.baker@keele.ac.uk (S.R. Baker).
0191-8869/03/$ - see front matter # 2003 Elsevier Ltd. All rights reserved.
PII: S0191-8869(02)00220-9
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S.R. Baker / Personality and Individual Differences 35 (2003) 569–591
1. Introduction
Self-appraised social problem solving ability is concerned with the way in which individuals
perceive and cope with problems encountered in everyday life. This conceptualisation of problem
solving appraisal is derived from the original problem solving model of D’Zurilla and Goldfried
(1971) which defined the problem solving process as a set of five interacting components: (1)
problem orientation, (2) problem definition and formulation, (3) generation of alternatives, (4)
decision making, and (5) verification. The first component problem orientation refers to the
motivational part of the problem solving process which includes generalised beliefs, appraisals,
and expectations about problems and an individual’s own problem solving abilities. The remaining four components include the actual problem solving skills required to define and resolve problems effectively.
One of the main assumptions of this and more contemporary formulations of the social problem-solving process (D’Zurilla, 1986, 1990) is that problem solving is a general coping strategy,
which facilitates and maintains general competence and adaptation. There has been much convergent evidence in support of this hypothesis. Social problem-solving appraisals have been
linked to indices of psychological distress including depression and anxiety (Elliott, Herrick,
MacNair, & Harkins 1994; Elliott, Sherwin, Harkins, & Marmarosh, 1995; Heppner, Hibel, Neal,
Weinstein, & Rabinowitz, 1982; Nezu, 1985, 1986), stress (Baker & Williams, 2001; Nezu & Perri,
1989; Nezu & Ronan, 1985, 1988), and adjustment (Heppner & Anderson, 1985), as well as
behavioural health outcomes such as, health complaints and symptoms (Elliott, 1992; Elliott &
Marmarosh, 1994). In addition, effective and ineffective self-appraised problem solvers have been
found to differ in relation to expectancies for control (Nezu, 1985), self-concept (Heppner,
Reeder, & Larson, 1983), and irrational beliefs (Heppner et al., 1983), whilst effective selfappraised problem solving has been associated with rational decision-making styles (Chartrand,
Rose, Elliott, Marmarosh, & Caldwell, 1993) and the use of problem-focussed coping strategies
(MacNair & Elliott, 1992).
Further investigations of the social problem-solving process indicate that different components
(problem orientation & problem-solving skills) may contribute to different adaptational outcomes. Self-appraised problem solving confidence and personal control, which are presumed to
reflect the motivational or problem orientation component of the problem solving model (c.f.
Elliott et al., 1995; Nezu, & Perri, 1989) have been linked to indices of subjective distress (Elliott,
Shewchuk, Richeson, Pickelman, & Franklin, 1996), and state and trait affectivity (Chartrand et
al., 1993; Elliott et al., 1994, 1995). In contrast, approach-avoidance appraisals, representing
specific cognitive-behavioural strategies as reflected in the problem solving skills component of
the social problem solving model, have been linked to rational decision-making strategies (Chartrand et al., 1993), assertion skills (Elliott, Godshall, Herrick, Witty, & Spruell, 1991), and
problem solving coping techniques (MacNair & Elliott, 1992), but have been found to be unrelated to self-report indices of distress or affect (Elliott et al., 1994, 1995a, 1995b, 1996).
Although many studies in the social problem-solving field have used undergraduate students as
convenient participants, there has been little research that has examined specifically the role of
social problem-solving appraisals in relation to adaptational and academic outcomes in educational settings. Yet, transition to university is a time when individuals are faced with many new
interpersonal, social, and academic demands. There is extensive evidence that such a time is
S.R. Baker / Personality and Individual Differences 35 (2003) 569–591
571
stressful for many individuals, and that adjustment during this period is linked to the way individuals cope with that stress which impacts on physical and psychological health (Aspinwall &
Taylor, 1992; van Rooijen, 1986), and academic motivation and performance (Sharma, 1973;
Zitzow, 1984). Within both the educational and psychology literatures, many factors thought to
influence adjustment to university and health-related and academic outcomes have been investigated including demographics (Halamandaris & Power, 1999; van Rooijen, 1986), university
entry qualifications and intellectual ability (Aspinwall & Taylor, 1992; Sternberg & Kaufman,
1998), personality variables such as the Big Five factors neuroticism and extraversion (DeRaad,
1996; Halamandaris & Power, 1997, 1999), motivational orientations (Vallerand & Bissonnette,
1992; Vallerand, Blais, Briere, & Pelletier, 1989; Vallerand, Pelletier, Blais, Briere, Senecal, &
Vallieres, 1992), and learning approaches (Minnaert & Janssen, 1992). The few studies that have
included a measure of social problem solving have found effective self-appraised problem-solving
to be associated with more adaptive study habits and attitudes (Elliott, Godshall, Shrout, &
Witty, 1990), and prospectively with lower psychological distress (Chang & D’Zurilla, 1996),
lower levels of stress associated with starting university (D’Zurilla & Sheedy, 1991), and better
academic performance (GPA) in the first year of college after accounting for prior academic
aptitude (D’Zurilla & Nezu, 1990; D’Zurilla & Sheedy, 1992; Rodriguez-Fornells & MaydeuOlivares, 2000).
Although existing research suggests that self-appraised social problem-solving ability may be an
important tool in accounting for individual differences in adaptational outcomes in tertiary educational settings, in the majority of these studies, the variance accounted for by problem-solving
was rather modest. It may be, as suggested by D’Zurilla and Sheedy (1991), that the time-frame
over which these studies were conducted was too short (i.e. 6 weeks to 3 months) to allow students sufficient time to cope successfully with initial adjustment problems over the course of their
first year and the transition into their second year of college. Particularly as previous research
suggests that social problem solving facilitates effective problem resolution and adjustment over
time (D’Zurilla, 1986, 1990). Thus, more effective problem solvers ought to be able to effectively
solve their adjustment problems and patterns between study variables are more likely to emerge
in the longer-term. The only way in which to disentangle potential relationships between social
problem solving and key outcome variables is to collect data from a sample of students over the
entire course of their time at university.
Another limitation of previous studies is that they have examined the impact of social problem
solving on key outcome variables, such as adjustment and academic performance, in relative
isolation, rather than investigating the complexities of such variables simultaneously. Furthermore, there has been no research examining the influence of social problem solving on academic
motivation; yet, it is known that motivational orientations play a significant role in educational
outcomes (Deci & Ryan, 1985, 1991 for reviews). In general, this research suggests that intrinsic
motivation, that is doing an activity voluntarily for its own sake and the pleasure and satisfaction
derived from participation, has a positive influence on academic performance. In contrast,
extrinsic motivation, that is engaging in activities for external rewards and as a means to an end,
has been associated with lower academic attainment. Similarly, other research has found amotivation, whereby individuals are neither intrinsically or extrinsically motivated but rather they
perceive no contingencies between their behaviour and outcomes, to be negatively related to educational outcomes such as, perceptions of competence and dropout rates (Vallerand & Bissonnette,
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1992; Vallerand et al., 1989). In the light of these relationships, it is perhaps surprising that there
has been little research examining the role of different motivational orientations in longer-term
academic performance in university settings, nor the potential impact of individual difference factors on such motivational orientations. Given the influence of social problem-solving appraisals on
initial university adjustment and performance (e.g. D’Zurilla & Sheedy, 1992), there is a need to
assess the potential impact of such appraisals on different motivational orientations and their
longer-term consequences.
The present study was designed to overcome some of the problems discussed above by utilising
a longitudinal prospective design that allowed an examination of the temporal relations between
social problem-solving appraisals and future adjustment, motivational orientations, stress, health,
and performance whilst controlling for the influence of demographics (age, gender), initial academic aptitude, and prior health. The first goal of the study was to examine the degree to which
measures of social problem-solving appraisals, obtained on entry to university, predicted adjustment, motivational orientations, stress and health in individuals’ second year of study, and
overall academic performance whilst at university. A second goal was to assess whether levels of
adjustment, stress, health and motivational orientations predicted overall academic performance.
An additional aim was to explore these key outcome variables in relation to different dimensions
of problem solving reflecting the problem orientation and problem-solving skill components of
the social problem-solving model. Thus, the study incorporated three stages. Stage 1 involved the
assessment of social problem-solving appraisals, and baseline measures of physical and psychological health within two weeks of students’ arrival at university. Stage 2 assessed motivational
orientations, adjustment to university, academic performance, stress and health during students’
second year of study. Stage 3 involved the assessment of overall academic performance during
students’ three years at university obtained after they had finished their studies.
2. Methods
2.1. Participants
Participants at Stage 1 were 104 first year joint-honours Psychology undergraduates (81 women,
23 men) enrolled at a medium-sized campus-based university during the period 1998–2001. Ages
ranged from 18 to 36 years (M=19.39, SD=2.90). For the second and third stages of the study, 91
of the original respondents took part (71 women, 20 men), with a mean age of 19.46 (SD=3.08).
2.2. Response rates
Time 1. Of the 156 questionnaires distributed at Time 1, 132 (84.6%) were returned. Of these,
28 (21 women, seven men) subsequently left the course during their first two years at University.
For these students, no Time 2 or Time 3 data were available and they were dropped from the
study. Therefore, the final sample at Time 1 consisted of 104 students (81 women, 23 men). A
comparison of entry qualifications, age, social problem solving, and health scores for those
respondents who dropped out compared to those who finished their studies and completed Time 2
measures, yielded no significant differences on any measure.
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573
Time 2. Of the 104 original participants who received Time 2 questionnaires, 13 (10 women,
three men) did not complete the second questionnaire (response rate=87.5%). Comparisons on
age, Time 1 measures, entry qualifications, and academic performance revealed no significant
differences between those participants who did not complete the second assessment compared to
those that did. Time 3. Of the 91 respondents who completed questionnaires at Time 1 and 2, overall
academic performance data (1st, 2nd and 3rd year marks) were available for all participants.
2.3. Procedure
Questionnaires were distributed to all first-year psychology students during the first 2 weeks
following arrival at the university. Participants were given the questionnaire during scheduled
classes after having been informed about the nature and aims of the study, and assuring them
that all responses were confidential. Participants completed the questionnaire during the class,
and when completed returned them to a collection box. Respondents were asked to provide their
student identification numbers in order to allow matching of questionnaires with Time 2 measures and academic performance data. All participants received course participation credits for
taking part in the first stage of the study.
During the second semester of students’ second year of study (17 months from Time 1), a second questionnaire was distributed to all students who responded to the first questionnaire.
Questionnaires were distributed and completed in scheduled classes. As at Time 1, respondents
were asked to provide their student identification numbers, and again assured that all responses
were confidential. Participants received no incentives (course credits or payments) for taking part
in the second stage of the study.
Following the second semester of students’ third year of study (14 months from Time 2), participants’ entry qualifications and academic performance data were obtained from the departmental database.
2.4. Time 1 measures
Social problem-solving appraisals. Self-appraised social problem solving was measured using the
Cassidy-Long Problem-Solving Questionnaire (Cassidy & Long, 1996). This questionnaire was
originally developed in order to assess more fully the multidimensional nature of problem-solving
appraisals as identified in the social problem-solving model (D’Zurilla, 1986, 1990) by incorporating dimensions both from the commonly used Problem Solving Inventory (PSI; Heppner,
1988), as well as additional dimensions identified in the problem-solving literature (e.g. Nezu,
1987). Original factor analysis of the questionnaire (Cassidy & Long, 1996) indicated six factors;
three replicating those from the PSI (problem-solving confidence, control, approach-avoidance
style), although the analysis revealed that the latter was two separate factors, avoidance style and
approach style, and two further factors, problem-solving helplessness and creative style. In line
with previous research (Elliott et al., 1995; Nezu & Perri, 1989), it is reasoned that the problemsolving confidence (a self-assurance and trust in one’s ability to solve problems) and personal
control dimensions (a sense of control over emotions and behaviours in problem solving situations) reflect aspects of the problem orientation component of the social problem solving model
(D’Zurilla, 1986, 1990), whilst approach (a general tendency to approach problem situations and
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confront them head on) and avoidance style (a general tendency to avoid problem situations and
put off dealing with them) represent facets of the problem-solving skills component. In addition, it
is tentatively reasoned, as outlined by Cassidy and Long (1996), that problem-solving creativity,
which measures planning and consideration of alternative solutions, represents aspects of the
problem-solving skills component, whilst problem-solving helplessness reflects a general problem
orientation. Each item has a true/?/false response format and is scored on a 0–2 scale with a
higher score indicating more effective problem-solving on each of the dimensions, with the
exception of the helplessness and avoidance factors which are reverse scored (i.e. the higher the
score, the lower the amount of perceived helplessness and avoidance). The problem-solving
questionnaire is scored for each of the six sub-scales and the total questionnaire. The internal
reliability of the problem-solving measure, as measured in the present sample, was high =0.80.
Psychological and physical health. The 12-item General Health Questionnaire (GHQ-12; Goldberg, 1972) was used to assess psychological health. Respondents rate how much they agree with
each statement, with a higher score indicating greater self-reported psychological distress. Cronbach’s alpha for the GHQ-12 in this study was 0.88.
Participants’ perceptions of their current physical health or healthiness were measured with a
16-item rating scale using items from Cassidy’s Healthiness Rating Scale and Frequency of Ill
Health Scale (Cassidy, 2000). The scale consisted of items such as ‘‘Do you regularly take non-prescription painkillers?’’ and ‘‘ Do you suffer from colds?’’. Respondents rated each item from 1 (never)
to 5 (a lot), with a higher score indicating worse self-reported physical health. The internal reliability
of the physical health measure in the present study (=0.56) was not as high as one might hope,
although it is above the 0.50 that Nunnally (1967) ascertains is sufficient for research purposes.
2.5. Time 2 measures
Adjustment to university. To assess adjustment to university, two outcome measures were used in
order to increase the reliability of the information provided. The first measure, the College Adaptation Questionnaire (CAQ; Crombag, 1968; Vlaander & van Rooijen, 1981) consists of 18 statements
scored on a 7-point scale, which measure individuals’ psychological, social and interpersonal adaptation to university life. Ten of the items reflect poor adjustment (e.g. ‘‘I find it hard to get used to life
here’’) and 18 items reflect good adjustment (e.g. ‘‘I am glad that I came to study here’’). The score for
the CAQ is the sum of the item scores after reflection of the 10 items indicating poor adjustment.
Previous studies have reported the CAQ to be highly reliable, =0.83 (van Rooijen, 1986).
The second measure, self-reported adjustment (SRA), was assessed using items from a previous
study on college students’ adjustment and performance (Aspinwall & Taylor, 1992). Three items
asked students to rate their academic, social and overall adjustment (e.g. ‘‘Overall, how well do
you think you’ve adjusted to University?’’) on a 7-point scale (1=not at all well, 7=extremely
well). Next, students were asked to rate their overall adjustment to the university compared to an
average second-year undergraduate on a 7-point scale (1=much less adjusted, 7=much more
adjusted), and their overall satisfaction with the university. Self-reported adjustment is the sum of
the five item scores, with higher scores indicating more successful adjustment to university.
Cronbach’s alpha indicated the scale to be highly reliable, =0.83.
Academic motivation. To assess students’ motivation to succeed at university, two outcome
measures were used. The first, the academic motivation scale (AMS; Vallerand et al., 1992) is a
S.R. Baker / Personality and Individual Differences 35 (2003) 569–591
575
28-item measure of motivation based on the theoretical model of motivation postulated by
Deci and Ryan (1985). The scale has seven sub-scales assessing three types of intrinsic
motivation (intrinsic motivation to know, to accomplish things, and to experience stimulation), three types of extrinsic motivation (external, introjected, and identified regulation), and
amotivation. Respondents are asked to rate each item on a 7-point scale (1=does not correspond at all, 7=corresponds exactly) to the extent to which it corresponds to the reasons
why they are at university. The AMQ is scored for each of the seven sub-scales with higher
scores indicating greater intrinsic, extrinsic and amotivation. Previous studies using the AMQ
reveal high internal (=0.81) and test-retest reliability (r=0.79) (Vallerand et al., 1989,
1992).
The second measure, self-reported motivation (SRM), included one item from Mallinckrodt
(1988) to assess drop-out intention, ‘‘I am certain I will obtain my degree from the university’’
scored on 7-point scale (1=strongly disagree, 7=strongly agree), and three items from Aspinwall
and Taylor (1992) to assess expectation of success (e.g. ‘‘I expect to do well at university’’) and
educational aspiration. For the latter, respondents indicated the lowest level of degree classification that they would be satisfied with ranging from a third-class degree to a first-class degree.
Self-reported motivation is the sum of the four item scores, with higher scores indicating higher
academic motivation. This four item measure of motivation showed satisfactory reliability,
=0.67.
Academic performance. Two measures of academic performance were obtained from the
departmental database. The first, entry qualifications (EQ) represented respondents’ A-level point
average gained prior to their entry to university and was obtained to control for respondents’
previous academic performance. The second measure represented respondents’ grade point average (GPA) for the four modules taken in their first year at university.
Psychological and physical health. Health was assessed as at Time 1.
Stress. Perceived stress was assessed using two measures; the Perceived Stress Scale (PSS;
Cohen, Kamarck, & Mermelstein, 1983) and a modified version of the Daily Hassles Index
(DHI; Schafer, 1998) The PSS-4 consists of four items rated on a 4-point scale (0=never,
4=very often), with a higher score indicating greater perceived stress. Previous studies indicate the scale has good reliability, =0.73 (Aspinwall & Taylor, 1992). The second stress
measure included 20 items from the Daily Hassles Index, which is designed to assess academic, social, financial and interpersonal stress in university students. Respondents indicated
on a 5-point scale (1=never, 4=very often) how often each event was a source of stress (e.g.
‘‘managing own finances’’ and ‘‘taking exams/tests’’). The measure, scored so that higher
scores indicated greater self-perceived stress, was highly reliable as measured in the present
study, =0.84.
2.6. Time 3 measures
Two measures of academic performance were obtained from the departmental database. Entry
qualifications (EQ) were assessed as at Time 2 in order to control for respondents’ initial level of
academic achievement. Second, overall academic performance (OAP) was assessed as respondents’ average mark for the 12 modules taken in Years 1–3.
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2.7. Overview of analysis
In order to assess the relationships between social problem-solving appraisals, psychosocial
adjustment, academic motivation, health, stress and academic performance, a series of multiple
regression analyses were performed in two stages, summarised in the path diagram shown as
Fig. 1. Those variables with an arrow pointing to it acted as dependent variables in a separate
regression analysis, whilst variables from which the arrow originates were used as an independent
variable (cf. Wong & Csikszentmihalyi, 1991). It should be noted, as can be seen in Fig. 1, that
measures of adjustment, stress, health, motivation, and performance whilst studying acted as
dependent variables in the first stages of the analyses, but as independent variables in the second
stages when predicting performance.
Stage 1 of the analyses assessed the role of problem-solving appraisals (measured at Time 1) in
predicting adjustment, academic motivation, stress, health, and academic performance (measured
at Time 2). In all analyses, age and gender were entered as the first step to control for demographics. Indeed, preliminary analyses revealed that there were gender differences on a number of
Time 1 and 2 measures. In Step 2, entry qualification score was entered in order to control for
Fig. 1. Path diagram indicating hypothesised relationships between social problem-solving appraisals, adjustment,
motivation, stress and health while studying, and academic performance.
S.R. Baker / Personality and Individual Differences 35 (2003) 569–591
577
academic performance prior to arrival at the university. Self-appraised problem solving was then
entered as the third step. For those analyses involving health as the dependent variable, the
baseline (Time 1) score on the relevant questionnaire was entered as a separate step prior to the
social problem-solving measure, in order to control for prior health status. In Stage 2 of the
analyses, to assess predictors of overall academic performance (measured at Time 3), two sets of
hierarchical regressions were performed. First, to assess whether social problem-solving appraisals significantly predicted overall performance. Second, to assess whether measures of adjustment, academic motivation, stress, health, and academic performance at Time 2 predicted overall
academic performance. In both sets of analyses, age, gender and entry qualifications were entered
in the first and second steps in order to control for demographics and prior academic achievement. In all Stage 1 and 2 analyses, where total problem-solving appraisal scores were significant
predictors, further exploratory regression analyses were conducted entering each of the six
problem-solving subscales (e.g. control, confidence) in a stepwise incremental fashion.
3. Results
3.1. Descriptive statistics and correlations
The means, standard deviations and ranges for all study variables are presented in Table 1, and
intercorrelations between social problem solving appraisal dimensions in Table 2. As can be seen
in Table 2, in general, the problem-solving appraisal dimensions were significantly related to one
another, with the exception of avoidance style. Table 3 presents the inter-correlations between all
study variables. As can be seen, problem-solving appraisals correlated positively with adjustment
to university (CAQ and SRA) and intrinsic motivation (to know and experience stimulation), and
negatively with the amotivation subscale of the AMQ, both measures of perceived stress (PSS-4,
DHI), and the GHQ-12. With regard to academic performance, problem-solving appraisals were
not significantly related to either GPA or OAP. Of the Time 2 measures, only GPA was significantly related to overall academic performance. Entry qualification scores, a measure of students’ aptitude prior to university, were positively related to the external regulation subscale of
the AMQ and negatively correlated with the intrinsic motivation (to accomplish) subscale. In
addition, scores were positively related to both academic performance measures taken during
students’ university careers (GPA, OAP).
Interrelationships between the two adjustment (SRA and CAQ), health (GHQ and Physical
health) and stress (PSS and DHI) outcome measures were significant, whilst between the two
motivation measures only the relationship between self-reported motivation and two of the AMQ
subscales (IMTA and EMIN) was significant.
3.2. Self-appraised social problem solving as a predictor of adjustment to university, academic
motivation, stress, health, and academic performance while studying
To assess whether social problem-solving appraisals measured at the start of students’ academic
career predicted adjustment to university, academic motivation, stress, health, and academic
performance in their second year of study, a series of hierarchical multiple regression analyses
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Table 1
Means, standard deviations, and ranges of all study variables at Time 1, 2 and 3
Variable
Mean
Time 1
Total SPSA
Helplessness
Control
Avoidance
Approach
Creativity
Confidence
GHQ
Physical health
30.74
6.39
4.79
4.65
5.85
4.80
4.27
15.40
37.45
7.99
1.65
2.20
2.08
2.09
2.03
2.30
6.43
6.53
6–46
0–8
0–8
0–8
0–8
0–8
0–8
4–32
22–54
104
104
104
104
104
104
104
104
104
Time 2
Adjustment
CAQ
SRA
88.37
23.17
16.85
4.67
50–120
9–31
91
91
19.40
15.62
13.96
21.34
18.71
19.59
8.02
15.73
4.72
4.69
4.65
4.47
4.82
5.40
5.04
1.69
8–28
7–26
4–27
5–28
8–26
4–28
4–25
11–21
91
91
91
91
91
91
91
91
13.92
22.18
5.41
7.71
6–42
8–46
91
91
6.59
37.71
2.97
11.17
0–13
12–68
91
91
58.90
5.78
41.3–72.0
91
58.24
21.93
5.58
10.14
44.9–70.7
0–40
91
104
Academic motivation
AMQ
IMTK
IMTA
IMES
EMID
EMIN
EMER
AMOT
SRM
Health
GHQ
Physical health
Stress
PSS
DHI
Academic performance
GPA
SD
Range
N
Time 3
OAP
EQ
AMQ=Academic Motivation Questionnaire; IMTK, IMTA, IMES=intrinsic motivation-to know, to
accomplish, to experience stimulation; EMID, EMIN, EMER=extrinsic motivation-identified regulation,
introjected regulation, external regulation; AMOT=amotivation; GPA=Grade Point Average; EQ=entry
qualifications; PSS=Perceived Stress Scale; CAQ=College Adjustment Questionnaire; DHI=Daily Hassles
Index; SRM=self-reported motivation; SRA=self-reported adjustment; GHQ=general health questionnaire;
SPSA=social problem-solving appraisals.
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Table 2
Intercorrelations between the social problem-solving appraisal dimensions
Variable
1
2
3
4
5
6
1. Helplessness
2. Control
3. Creativity
4. Confidence
5. Avoidance
6. Approach
–
0.50**
–
0.19
0.19
–
0.31**
0.45**
0.44**
–
0.15
0.13
0.23*
0.09
–
0.32**
0.48**
0.41**
0.50**
0.07
–
Higher scores indicate more effective problem-solving on each of the dimensions, with the exception of the helplessness
and avoidance factors whereby the higher the score, the lower the amount of perceived helplessness and avoidance.
* P< 0.05.
** P< 0.01.
were carried out. The results of these analyses are presented in Table 4, which shows R2, R2
change, and F values for the final models. Problem-solving had significant effects on a number of
Time 2 measures, after controlling for age, gender and entry qualifications; adjustment to university, academic motivation, perceived stress, and academic performance. Total problem-solving
scores accounted for an additional 14 and 11% of variance in self-reported adjustment (Fchange
(1, 86)=14.49, P<0.001) and CAQ scores (Fchange (1, 86)=11.02, P<0.001), 7 and 12% in
extrinsic motivation (identified regulation) (Fchange (1, 86)=7.49, P< 0.01) and amotivation
(Fchange (1, 86)=9.97, P <0.01) respectively, 5 and 7% in PSS (Fchange (1, 86)=4.83, P<0.05)
and DHI (Fchange (1, 86)=8.75, P<0.01) respectively, and 7% in academic performance scores
(GPA) (Fchange (1, 86)=7.18, P<0.01). Examination of the direction of the beta values indicates
that individuals with higher problem-solving appraisals report better psychosocial adjustment to
university life, are more self-determined (as measured by the extrinsic motivation-identification
scale), are less amotivated, and have lower levels of stress while studying, and have higher average
marks in their first year of study when compared to those with lower self-appraised problemsolving.
It is important to note significant predictors other than problem solving in these regression
analyses. Gender was a significant predictor in the final models for self-reported stress (DHI),
extrinsic motivation (identified regulation) and GPA, with the positive beta values indicating that
women reported high levels of stress and self-determination, and had higher average first year
marks compared to men. In terms of academic performance, in addition to gender, both age and
entry qualifications had significant effects. Together, demographics and entry qualifications
accounted for 16% of the variance in student’s GPA, with older students, women, and those
with higher entry qualifications prior to university having higher average marks after their first
year of study. In relation to the health measures, the only significant predictor of physical
health whilst studying was prior physical health scores measured at Time 1, accounting for an
additional 19% of the variance after controlling for demographics and EQ. Prior psychological
health, as measured by the GHQ, was not a significant predictor of psychological well-being
while studying.
580
Table 3
Intercorrelations between the study variablesa
Variable
1 2
3
4
a
See Table 1 for abbreviations.
* P <0.05.
** P <0.01.
*** P <0.001.
6
7
8
9
10
0.18
0.22*
0.42***
0.60***
0.24*
0.19
0.32**
0.76***
0.19
0.17
0.25*
0.22*
0.16
0.03
0.12
0.10
0.06
0.03
0.09
0.14
–
0.61*** 0.28** 0.41***
–
11
12
13
14
0.31**
0.63***
0.60***
0.37***
0.10
0.01
0.14
0.15
0.22*
0.57***
0.44***
0.33**
0.35**
0.23*
0.46*** 0.19
0.44*** 0.17
0.08
0.10
0.04
0.17
0.26*
0.24*
0.06
0.05
0.17
0.22*
0.09
0.19
0.07
0.23*
–
0.32**
0.45***
0.21*
0.08
0.01
0.12
–
0.38*** 0.12
0.21*
0.03
0.19
–
0.06
0.06
0.26*
0.02
0.32**
–
0.05
–
0.05
–
0.07
0.09
0.03
15
0.16
0.27*
0.29**
0.08
0.04
16
17
18
0.19
0.07
0.16
0.01
0.13
0.05
0.21
0.04
0.03
0.07
0.14
0.10
0.08
0.17
0.12
0.02
0.12
0.06
0.07 0.01
0.03
0.09
0.10
0.10
0.27*
0.30**
0.28** 0.04
0.11
0.09
0.07
0.10
0.30**
0.05
0.23*
0.17
0.02
0.02
0.10
0.07
0.09
0.03
0.05
0.05
0.07
0.04
0.01
0.19
0.11
0.10
0.15
–
0.89*** 0.23*
–
0.30**
–
0.08
0.03
0.21*
0.32**
0.21*
0.06
0.02
0.10
0.10
0.18
0.07
0.22*
S.R. Baker / Personality and Individual Differences 35 (2003) 569–591
1. SPS
– 0.35** 0.39*** 0.29**
2. CAQ
–
0.74*** 0.34**
3. SRA
–
0.48***
4. AMQ–
IMTK
5. AMQIMTA
6. AMQIMES
7. AMQEMID
8. AMQEMIN
9. AMQEMER
10. AMQAMOT
11. SRM
12. PSS
13. DHI
14. GHQ
15. Physical
health
16. GPA
17. OAP
18. EQ
5
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S.R. Baker / Personality and Individual Differences 35 (2003) 569–591
Table 4
Hierarchical regression analyses predicting adjustment to university life, academic motivation, health, stress, GPA, and
overall academic performance from social problem-solving appraisalsa
Dependent variables
R2
R2 change
F
Beta values
SPS
Age
Gender
EQ
Prior
health
0.10
0.11
–
–
Adjustment
CAQ
SRA
0.13
0.18
0.11
0.14
30.32*
40.77**
0.34***
0.38***
0.04
0.07
0.04
0.06
Motivation
AMQb- EMID
- AMOT
SRM
0.18
0.12
0.06
0.07
0.10
0.01
40.72**
30.05*
10.29
0.28**
0.33**
0.07
0.03
0.14
0.21
0.37***
0.07
0.12
0.17
0.12
–
–
–
Stress
PSS
DHI
0.11
0.18
0.05
0.08
20.70*
40.85**
0.23*
0.30**
0.19
0.05
0.07
0.23*
0.09
0.16
–
–
Health
GHQ-12
Physical health
0.07
0.26
0.04
0.00
10.24
50.82***
0.24
0.07
0.01
0.03
0.19
0.06
0.42
0.01
0.46***
Performance
GPA
OAP
0.22
0.28
0.07
0.05
60.11***
80.37***
0.26**
0.22*
0.25*
0.18
0.32**
0.40***
0.39***
0.46***
–
–
a
See Table 1 for abbreviations.
Significant subscales only.
* P< 0.05.
** P< 0.01.
*** P< 0.001.
b
3.3. Self-appraised social problem solving as a predictor of overall academic performance
To examine whether social problem-solving appraisals measured on arrival at university predicted overall academic performance achieved at the end of students’ academic careers, a hierarchical regression analysis was conducted. As can be seen in Table 4, after controlling for
demographics and entry qualifications, the relation between self-appraised problem-solving and
academic performance was significant. Problem solving appraisals accounted for an additional
5% of the variance in students’ average marks over their three years of study. In addition, both
gender and entry qualifications had a significant effect on academic performance, accounting for
an additional 9 and 14% of the variance respectively. Examination of the beta values indicates
that individuals with higher self-appraised problem-solving, women, and students with higher
entry qualifications had higher overall marks.
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3.4. Adjustment, academic motivation, stress and health as predictors of overall academic performance
To examine whether the variables measured during students’ second year of study (Time 2)
predicted overall academic performance at the end of their university careers, a series of hierarchical multiple regressions were conducted. After controlling for demographics and entry qualifications, only intrinsic motivation (to accomplish), self-reported adjustment, and academic
performance during their first year of study (GPA) had a significant effect on students’ overall
marks; intrinsic motivation (to accomplish) (R2 change=0.05, (Fchange (1, 74)=4.62, P< 0.05),
GPA (R2 change=0.58, (Fchange (1, 74)=263.40, P<0.001), self-reported adjustment (R2
change=0.06, (Fchange (1, 74)=5.68, P<0.05). Examination of the beta values indicate that
students with higher intrinsic motivation accomplishment scores (beta=0.22, P<0.05), higher
levels of self-reported adjustment to university (beta=0.24, P<0.05), and a higher GPA in their
first year of study (beta=0.86, P<0.001) had higher overall marks at the end of their university
careers.
3.5. Relations among adjustment, academic motivation, stress, GPA, overall academic performance
and social problem solving appraisal dimensions
To further clarify the relationships between different problem-solving appraisal dimensions,
Time 2 measures and overall academic performance, further exploratory regression analyses were
conducted using the problem-solving appraisal subscales. For those measures that showed a significant relationship with total problem-solving scores (see Table 4), a further stepwise regression
analysis was conducted entering each of the six problem-solving subscales in an incremental
fashion to assess which dimensions contributed most to the relation between problem-solving
appraisals and adjustment, motivation, stress, and academic performance. The results of these
Table 5
Stepwise multiple regression analyses predicting adjustment to university life, academic motivation, stress, GPA and
overall academic performance from problem-solving appraisal subscalesa
Independent Dependent variables
variablesb
CAQ
SRA
Helplessness
Control
Creativity
Confidence
Approach
Avoidance
a
AMQAMOT
beta
R2ch beta R2ch beta
R2ch beta
–
–
–
0.34**
–
–
–
–
–
0.11
–
–
–
–
–
0.07
–
–
–
–
0.27*
0.22*
–
–
–
–
0.11
0.04
–
–
See Table 1 for abbreviations.
Only significant results shown.
* P< 0.05.
** P< 0.01.
b
AMQEMID
–
–
–
0.27**
–
–
–
PSS
DHI
R2ch beta
R2ch beta
–
0.23* 0.07
–
–
–
–
–
–
0.22* 0.05
–
–
–
–
–
–
0.23* 0.05
–
–
–
–
–
–
–
–
–
GPA
R2ch beta
0.33** 0.10
–
–
–
–
–
–
0.25**
–
–
–
–
OAP
R2ch beta R2ch
–
0.06
–
–
–
–
–
–
–
–
0.19*
–
–
–
–
–
0.04
–
S.R. Baker / Personality and Individual Differences 35 (2003) 569–591
583
analyses are shown in Table 5. As can be seen, problem-solving creativity and confidence were
significant predictors of self-reported adjustment to university, accounting for an additional 15%
of the variance after controlling for demographics and entry qualifications (R2=0.20, F(5,
90)=4.12, P<0.01). Problem-solving confidence was also a significant predictor of adjustment to
university (CAQ), extrinsic motivation (identified regulation), and perceived stress (PSS)
accounting for an additional 11% (R2=0.13, F(4, 90)=3.24, P<0.05), 7% (R2=0.18, F(4,
90)=4.60, P<0.01) and 5% (R2=0.11, F(5, 90)=2.72, P< 0.05) of the variance respectively.
Examination of the beta values indicates that those individuals who had greater confidence in
their ability to solve problems reported better adjustment to university, greater self-determination
(as measured by the identified regulation subscale), and lower levels of stress while studying than
did individuals with less problem-solving confidence. In addition, individuals who were more
creative in solving problems reported better adjustment to university compared to less creative
problem-solvers. Self-reported stress (DHI) was also associated with greater feelings of helplessness in problem situations (R2=0.21, F(4, 90)=5.53, P=0.001). With regard to amotivation,
problem-solving control and avoidance had significant effects, accounting for an additional 12%
of the variance (R2=0.13, F(5, 90)=2.64, P<0.05), with feelings of uncontrollability and the
tendency to avoid problems associated with greater amotivation while studying. After controlling
for demographics and entry qualifications, two problem-solving appraisal dimensions were associated with academic performance; problem-solving control with GPA (R2=0.22, F(4, 90)=6.01,
P<0.001), and approach with overall academic performance (R2=0.27, F(4, 90)=7.91,
P<0.001), both contributing 4% of the variance to their respective measures. Examination of the
beta values indicates that those individuals with greater feelings of control in problem situations
had higher marks in their first year of study, whilst the tendency to approach problem situations
was associated with higher overall marks over students’ three years of study.
4. Discussion
The results of the study suggest that social problem-solving appraisals were an important predictor of individuals’ psychosocial adjustment to university, perceived stress levels, motivational
orientations (identified extrinsic motivation and amotivation), and academic performance whilst
studying, and overall academic performance over the course of students’ university careers. In
relation to overall academic performance, gender, entry qualifications, intrinsic motivation (to
accomplish), and levels of adjustment, in addition to social problem-solving appraisals, were all
important in predicting performance over the course of students’ time at university. Additionally,
different dimensions of self-appraised problem solving were related to different adaptational and
academic outcomes.
Within a social problem-solving framework (D’Zurilla, 1986, 1990), social problem solving is a
process that enables individuals to effectively identify and implement coping strategies to deal
with problems and demands encountered in daily living. In this way, problem solving should act
to increase general competence and adaptation and reduce levels of stress by facilitating more
effective problem resolution, and minimising the emotional effects of problem situations. The
present findings are consistent with this hypothesis, and with previous studies in educational settings (Chang & D’Zurilla, 1996; D’Zurilla & Sheedy, 1991). That is, effective self-appraised
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S.R. Baker / Personality and Individual Differences 35 (2003) 569–591
problem solving, measured on entry to university, was associated with better subsequent psychosocial adjustment and lower levels of perceived stress.
Furthermore, social problem-solving appraisals had a direct effect on academic motivation;
effective self-appraised problem solving was linked to greater identified extrinsic motivation and
lower levels of amotivation. In accordance with Deci and Ryan’s (1985, 1991) self-determination
framework, these data suggest that individuals who had greater self-perceived problem-solving
abilities had a more self-determined profile, therefore were more likely to undertake academicrelated behaviours (e.g. extra reading) because they were valued and judged as intrinsically
important, rather than for the attainment of external rewards (e.g. teacher approval), and that they
were less amotivated, compared to individuals with lower self-perceived problem-solving abilities.
That is, they were more likely to perceive contingencies between their behaviours (e.g. more time
spent studying) and academic outcomes (e.g. better grades). Although no previous studies have
examined specifically relations between social problem-solving appraisals and motivational orientations, the present data are in line with differences reported in the literature between self-appraised
effective and ineffective problem solvers on a number of dimensions which might characterise intrinsically motivated behaviours; enjoyment of cognitive activities (Heppner et al., 1983), more adaptive
study habits and attitudes (Elliott et al., 1990), and higher expectancies for control (Nezu, 1985), as
well as those potentially associated with amotivated behaviours namely, a general tendency toward
passivity, and shirking of responsibilities and decision-making (Maydeu-Olivares & D’Zurilla, 1996).
In relation to academic performance, effective social problem-solving appraisals predicted
higher grades, findings that support and extend those of previous studies with American and
Spanish students (D’Zurilla & Nezu, 1990; D’Zurilla & Sheedy, 1992; Rodriguez-Fornells &
Maydeu-Olivares, 2000). After controlling for prior academic aptitude, problem-solving appraisals were found to have a direct positive effect on academic performance not just in students’ first
year as reported in previous studies but throughout their university careers. However, although
the variance accounted for by social problem solving in academic performance scores was more
than that reported in these previous studies (2.7–3.7%), it was still rather modest (5–7%). There
are a number of possible reasons for this. It may be that in addition to exerting a direct effect,
social problem solving appraisals had an indirect impact on performance via other factors. There
are a number of factors identified in the relevant literature that may potentially moderate this
relationship (e.g. personality; Halamandaris & Power, 1997, 1999). Of those factors included in
the present study, motivational orientation, gender, entry qualifications and psychosocial adjustment were all associated with academic performance; women, and those individuals who reported
better adjustment to university, were more intrinsically motivated toward accomplishment, and
had higher academic entry qualifications obtained significantly higher marks during their three
years at university. It may be, for example, that effective problem-solvers had better levels of
adjustment to university life or were more intrinsically motivated toward their academic studies,
which in turn, led to better academic performance. Such an explanation would be supported by
relationships observed between social problem solving appraisals and both psychosocial adjustment and academic motivation. It is also consistent with previous research in a range of educational settings that supports the notion that better adjustment and more intrinsically motivated
behaviours are linked to greater academic success (e.g. Deci & Ryan, 1985, 1991).
However, what is not possible to ascertain from the present study is the cause and effect relationship between social problem solving appraisals and motivational orientations. Since the
S.R. Baker / Personality and Individual Differences 35 (2003) 569–591
585
motivation measure was not administered at Time 1, it is not possible to determine whether
individuals displayed the same pattern of motivational orientations prior to arrival at university,
or whether these patterns developed in response to social problem solving skills and abilities.
Moreover, whilst social problem solving appraisals in the present study have been shown to
influence students’ academic motivation and performance, it is highly likely that reciprocal relationships also exist. That is, changes in motivation over time as a result of experiences whilst
studying, as well as ongoing performance, may impact on the implementation and effectiveness of
social problem solving skills. Other factors are also more than likely to influence these relationships. For example, women students had higher identified extrinsic motivation scores and better
academic performance than did men in this study. These data are in line with previous studies
which have found that women were generally more motivated toward academic activities (Karsenti & Thibert, 1994), displayed a more self-determined motivational profile (Vallerand & Bissonnette, 1992), and tended to have higher levels of desire to finish university and persistence
behaviour than men (Allen, 1999). It is not clear whether these differences were related to social
problem-solving appraisals; although there were no gender differences in self-appraised social
problem solving, previous studies have indicated such differences do exist (D’Zurilla, MaydeuOlivares, & Kant, 1998). Given the difference in sample size between men and women in the
present study, reflecting the gender imbalance of British psychology courses, these data should be
interpreted with caution until further research has explored these potential differences further.
Furthermore, future research needs to assess the influence of other factors not measured in this
study. For example, personality variables such as neuroticism and extraversion, social support,
and study habits, have all been shown to impact on key adaptational and educational outcomes
(e.g. DeRaad, 1996; Elliott et al., 1990; Halamandaris & Power, 1997, 1999). It is possible that
such factors may have influenced the pattern of results observed here given that the data were not
in all cases consistent with the hypothesised relations depicted in Fig. 1, for example in relation to
self-reported health. Additionally, whilst there are a number of strengths in the design and conclusions that can be drawn from prospective longitudinal studies such as this, the only way in
which to assess the complexities of the interrelationships between social problem-solving appraisals and key outcome variables, including both direct and moderating pathways, is via structural
equation modelling. Due to the number of participants this was beyond the scope of the present
study. Additional data is currently being collected from two cohorts of students giving data over
a 5-year time-span, which should enable a more detailed investigation of these relationships. In
addition, key adaptational outcomes are being measured at different time points throughout
students’ three years at university, which will allow an examination of the potential differential
effects of social problem-solving appraisals on stress, adjustment and motivation in relation to
new demands and problems experienced over students’ entire university careers.
An additional aim of the study was to explore the different dimensions of problem solving
appraisals and their relation to different long-term outcomes. It was reasoned that the problemsolving confidence, personal control, and problem-solving helplessness dimensions would be
associated with measures of subjective well-being (adjustment and stress), whilst the dimensions
of problem-solving creativity, and approach and avoidance style would associated with behaviourally-based outcomes such as academic performance, but unrelated to subjective indices of
distress. These predictions were based on the premise that problem-solving confidence, personal
control, and problem-solving helplessness represent facets of the problem orientation component
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S.R. Baker / Personality and Individual Differences 35 (2003) 569–591
of the problem solving process, whilst problem-solving creativity, avoidance and approach style
reflect aspects of the problem solving skills component (c.f. Cassidy & Long, 1996; Elliott et al.,
1995a,b). The present data, obtained using the Cassidy-Long Problem-solving questionnaire, are
partially consistent with these predictions.
Consistent with the functions of the problem orientation component, problem-solving helplessness and confidence were significant predictors of the wellbeing measures, psychosocial
adjustment to university and perceived stress. However, problem-solving creativity (planning and
consideration of alternative solutions), operationally defined as a problem solving skills factor,
was also a significant predictor of psychosocial adjustment. Although largely unexpected, previous work in a university setting has reported an association between the problem-solving skills
component, specifically the generation of alternative solutions dimension, and perceived stress
levels (D’Zurilla & Sheedy, 1991). Taken together, these findings indicate that aspects of both
problem orientation and problem solving skills may be important for successful adaptation to the
problems encountered in university life in the longer-term.
According to D’Zurilla and Sheedy, stronger relations between the problem-solving skill
dimension and indices of subjective wellbeing might be observed over a longer timeframe than
measured in their study (i.e. 3 months). However, the present study, which included a 2-year
period, provides no support for such an assertion. It could be that whilst individuals may have
had sufficient time to cope with initial adjustment problems encountered in starting university,
new demands and problems might have emerged during the course of the first year and transition
into the second, which required a different problem orientation in order to implement effective
problem resolution, and minimise their emotional effects. Furthermore, the present data provide
no support for D’Zurilla and Sheedy’s assertion that problem orientation reduces stress indirectly
(of problem solving skills) through mechanisms of perceived control, an explanation in accordance with much of the stress and health literatures which suggest that enhanced control perceptions reduce stress via, for example, a reduction in uncertainty in stressful situations (e.g.
Baker & Stephenson, 2000a, 2000b). Personal control beliefs were not linked to either psychosocial adjustment or perceived stress in the present study.
Although previous studies have not investigated specifically relationships between social problem-solving components and motivational orientations, it could be tentatively reasoned that
facets of problem orientation, which reflect the motivational component of the model, would be
linked to academic motivation. Partially supportive of these predictions, problem-solving confidence was associated with identified extrinsic motivational orientation, whilst both personal
control and avoidance style were related to amotivation. In relation to problem-solving confidence the findings are in accordance with previous research, which has found that more confident problem-solvers, who have more self-assurance and trust in their abilities to solve problems
effectively, are more motivated to solve problems, more systematic and persistent, and have
higher expectations of success (Heppner et al., 1982). Within Deci and Ryan’s (1985, 1991) selfdetermination framework, individuals who are more amotivated are likely to experience feelings
of lack of control since they perceive no contingencies between their behaviours and outcomes.
Over time, such individuals are likely to avoid or cease participation in such activities. In line with
this framework, in this study, those individuals who reported lower personal control beliefs and a
tendency to avoid problem situations had higher amotivation scores. With regard specifically to
problem-solving avoidance, several studies have linked problem-solving avoidance to behaviours
S.R. Baker / Personality and Individual Differences 35 (2003) 569–591
587
that might characterise amotivation; general lack of decision-making (Maydeu-Olivares &
D’Zurilla, 1996), greater levels of boredom (Elliott et al., 1990) and lethargy (Elliott et al., 1995a,
1995b), as well social withdrawal (D’Zurilla & Chang, 1995).
According to predictions, behaviourally-based outcomes, such as academic performance,
should be linked to facets of the problem-solving skill component (Godshall & Elliott, 1997). In
line with this, previous studies in university settings have found problem solving skills, particularly decision-making or impulsive/carelessness subscales, to be more predictive of higher marks
than problem orientation (D’Zurilla & Nezu, 1990; D’Zurilla & Sheedy, 1992; Rodriguez-Fornells & Maydeu-Olivares, 2000). The present findings provide mixed support for these predictions. Approach style was related to overall academic performance throughout student’s three
years at university, whilst personal control, a problem orientation dimension, was the best predictor of academic success in students’ first year at university. These data might indicate that
whilst motivational aspects of the problem solving process might be important initially in relation
to success in the first academic year, performance in the longer-term is related to the effectiveness
of individuals’ actual problem-solving skills and abilities, particularly whether or not they have a
tendency to approach and confront problems encountered in university life. Such an interpretation is tentative, however, given the relatively minimal amount of variance accounted for by the
problem-solving dimensions in the two performance measures (both 4%). Particularly when
compared to the highly significant relationship between prior academic aptitude (entry qualifications) and academic performance. Further research is needed to confirm the performance effects
observed here, and to explore the potential role of other factors in moderating the relationship
between different problem-solving appraisal dimensions and success at university. For example,
previous literature indicates that individuals learning habits and approaches may have a mediating effect on the relation of control perceptions on educational performance (Drew & Watkins,
1998). Similarly, consistent with the observed relationship between personal control beliefs and
amotivation in this study, and with previous findings linking motivational orientations to persistence behaviour and academic performance (e.g. Deci & Ryan, 1985; Vallerand & Bissonnette,
1992), direct tests of the effects of motivational orientations on the social problem-solving
appraisal-academic performance relationship are required.
4.1. Potential limitations
Some potential limitations and qualifications concerning the results of this study need to be
highlighted, in addition to those mentioned above. First, nearly all of the Time 1 and 2 measures
were self-report which can be subjected to a number of criticisms, including the impact of
response bias and the role of negative affectivity, particularly in the reporting of health symptoms
and levels of stress (e.g. Watson & Clark, 1984). The performance part of the study is not subject
to this criticism, as individuals’ marks were obtained from departmental records. Second, given
that some of the constructs measured at Time 2 were similar to one another and were highly
correlated (e.g. psychosocial adjustment and perceived stress), it is possible that their variance
overlapped. Further research is needed which utilises a multimethod approach in order to clarify
the relationships observed here. Third, the measure of social problem-solving appraisal used in
this study is a relatively recent development within the problem-solving literature, with few
studies reporting its use. Interpretation of the present pattern of results are based on the premise
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S.R. Baker / Personality and Individual Differences 35 (2003) 569–591
that the six factors measured by the questionnaire do operate in a manner consistent with the
social problem solving model. Although the present results are generally congruent with the
operational definitions of each dimension in relation to components of the model, further data
needs to be obtained detailing the construct validity of the six factors, particularly in the light of
significant intercorrelations between many of the problem-solving dimensions. Most notably,
convergent evidence needs to be provided between this questionnaire and other measures of
problem-solving appraisal, which tap directly the components identified in the model (e.g. Social
Problem-Solving Inventory-Revised; D’Zurilla, Nezu, & Maydeu-Olivares, 1997). Fourth, the
participant sample used in this study was selected from one British University, and included only
joint-honours psychology students. It could be argued that there may be different demands placed
on students in different universities within the UK, between UK universities and those in other
countries, or between psychology and other academic disciplines, which may have had an impact
on the pattern of results observed here. Caution should therefore be exercised in generalising the
current findings beyond this student population, or indeed to other wider populations.
4.2. Implications for interventions
Insomuch as the present data lend support to the social problem-solving model and suggest that
effective problem-solving appraisals have a positive impact on a range of educational outcomes,
they also provide useful guidance in developing preventive and remedial interventions in tertiary
educational settings. Currently, there is little in the way of interventions designed to facilitate
students’ adjustment to university or mitigate stress and its associated effects, however research
which has been carried out suggests that identification and early intervention can improve
reported adjustment, with less-adjusted students showing the most improvement (Baker & Siryk,
1986). Given the beneficial effect of social problem-solving appraisals observed here, interventions based on problem-solving training for stress management and prevention might be potentially useful. Research support has been growing in recent years for problem-solving training as a
viable and successful approach to stress management (D’Zurilla, 1990 for a review). Whilst there
have been no intervention studies based on a problem-solving training model in educational settings, the model may have value for both individuals who are already experiencing difficulties as
well as those identified as ‘high risk’ in order to improve adjustment and prevent high levels of
stress during the course of their time at university. Given the present results and previous research
(e.g. D’Zurilla & Sheedy, 1991) which suggest that social problem-solving involves a number of
different components (problem orientation and problem-solving skills) which have different
adaptational outcomes, the most beneficial intervention strategy would appear to be to train
individuals in all problem-solving techniques in order to deal effectively with both general and
specific stressful situations, and to develop a repertoire of problem-solving skills to successfully
adjust to university, which in turn, may impact on academic performance.
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