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Two tests of the social cognitive model of well-being in Portuguese college students
Journal of Vocational Behavior 80 (2012) 362–371
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Two tests of the social cognitive model of well-being in Portuguese college students☆ Robert W. Lent a,⁎, Maria do Céu Taveira b, Cristina Lobo b a b
Department of Counseling, Higher Education, and Special Education, University of Maryland, USA Department of Psychology, University of Minho, Braga, Portugal
a r t i c l e
i n f o
Article history: Received 15 August 2011 Available online 2 September 2011 Keywords: Social cognitive career theory Self-efficacy Academic satisfaction Academic stress Life satisfaction Positive affect
a b s t r a c t A social cognitive model of well-being (Lent & Brown, 2006, 2008) was tested in two studies (one cross-sectional, one longitudinal) with Portuguese college students. Participants in Study 1 (N = 366) completed measures of academic self-efficacy, environmental support, goal progress, academic satisfaction and stress, trait positive affect, and overall life satisfaction at a single assessment. Those in Study 2 (N = 158) took the same measures at each of two time points, 15 weeks apart. A modified version of the model improved the fit to the data in Study 1. Optimal fit in Study 2 was achieved with a bidirectional version that modeled reciprocal relations of positive affect to both self-efficacy and support. The predictors in both studies accounted for substantial portions of the variance in academic domain satisfaction, academic stress, and life satisfaction. Implications for future research on the social cognitive model in educational and work settings are considered. © 2011 Elsevier Inc. All rights reserved.
1. Introduction In its original formulation, social cognitive career theory (SCCT; Lent, Brown, & Hackett, 1994) included models aimed at understanding educational/vocational interest, choice, and performance. The three models have received a good deal of empirical attention, with recent meta-analyses summarizing much of the research testing SCCT's interest, choice (Sheu et al., 2010) and performance hypotheses (Brown et al., 2008; Brown, Lent, Telander, & Tramayne, 2011). Lent and Brown (2006, 2008) recently proposed a fourth SCCT model, one focused on explaining satisfaction and other aspects of positive adjustment within educational and vocational domains. This latest model represents an effort to extend Lent's (2004) more general model of normative wellbeing to the specific contexts of school and work. Like the preceding SCCT models, the satisfaction or well-being model draws in part from general social cognitive theory (Bandura, 1986, 1997). It also attempts to integrate a variety of perspectives and constructs from the literatures on subjective and psychological well-being. By drawing together cognitive, behavioral, and social variables that have been shown to be linked to domain and overall life satisfaction, the model focuses on those aspects of the person (e.g., self-efficacy, goal setting and progress) and environment (access to goal-relevant resources, like tutoring and mentoring) over which students, workers, and their support systems can exert some degree of control — and that may, therefore, help to inform interventions for promoting well-being in academic and work settings. In addition, by including personality dispositions that have been linked to domain and overall well-being, the model also acknowledges sources of well-being that are typically thought to be relatively resistant to personal agency and traditional interventions. Lent's (2004) general model of normative well-being is illustrated in Fig. 1. According to this model, domain satisfaction and affect (e.g., low levels of perceived stress) are reciprocally related to overall life satisfaction (see the double arrows on path 1). In other words, feeling satisfied and comfortable within one's central life domains is likely to promote an overall sense of happiness, ☆ The findings reported herein were presented at the annual meeting of the American Psychological Association, Washington, DC, August, 2011. ⁎ Corresponding author at: Department of Counseling, Higher Education, and Special Education, University of Maryland, College Park, MD 20742, USA. E-mail address: [email protected] (R.W. Lent). 0001-8791/$ – see front matter © 2011 Elsevier Inc. All rights reserved. doi:10.1016/j.jvb.2011.08.009
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16
Personality Traits & Affective Dispositions (e.g., PA, NA, extraversion, neuroticism, optimism, GSE)
15 Self-Efficacy Expectations
5
14 Environmental Supports & Resources (e.g., goal-relevant resources, modeling, encouragement)
363
2
3
9
13
Participation in/ Progress at GoalDirected Activity
11
4
Domain-Specific Satisfaction, Situational Affect
1
Overall Life Satisfaction
10 12 Outcome Expectations
8
6 7
Fig. 1. Integrative model of well-being under normative life conditions. PA = positive affect; NA = negative affect; GSE = generalized self-efficacy. Adapted from “Toward a unifying theoretical and practical perspective on well-being and psychosocial adjustment,” by R.W. Lent, 2004, Journal of Counseling Psychology, 51, p. 500. Reprinted with permission.
and overall happiness is, in turn, likely to nurture well-being within one's more specific life domains. Both domain adjustment (i.e., satisfaction and stress) (path 2) and overall life satisfaction (path 3) are also assumed to be affected by personality/affective traits, such as positive and negative affect. That is, one's tendency to experience positive or negative affect across situations is likely to influence perceptions of well-being generally as well as in specific life domains. In addition to personality, domain adjustment is posited to be affected by (a) setting and making progress at personally relevant goals (path 4), (b) having strong self-efficacy beliefs in relation to one's task requirements and goals (path 5), (c) holding positive outcome expectations (e.g., favorable beliefs about the outcomes of one's educational efforts; path 6), and (d) having access to environmental resources for promoting self-efficacy and goal pursuit (path 7). Goal setting and progress are assumed to affect one's overall sense of life satisfaction both directly (path 8) as well as indirectly via domain adjustment. The model also posits ways in which the predictors of domain adjustment and life satisfaction interrelate. In particular, goal setting and progress are assumed to be influenced by self-efficacy (path 9), outcome expectations (path 10), and environmental support (path 11). For example, people are more likely to make progress on their valued goals to the extent that they possess favorable views of their task and goal-relevant self-efficacy, foresee positive outcomes as resulting from their efforts, and perceive that they have the supports needed to achieve their goals. Outcome expectations are bolstered by the presence of environmental supports (path 12) and self-efficacy (path 13). That is, people are more likely to hold optimistic beliefs about the consequences of their actions if they view their environments as supportive and themselves as efficacious. Like outcome expectations, self-efficacy is seen as responsive to environmental supports (path 14), as people derive a sense of their efficacy partly from the persuasive messages they receive about their capabilities and from other forms of support (e.g., modeling) provided by their interpersonal environments. Finally, personality traits such as positive and negative affect may influence domain and life satisfaction both directly, as noted earlier, and indirectly through their relations to self-efficacy (path 15) and environmental support (path 16). For example, because positive and negative affect incline people to view themselves and their environments in generally positive or negative ways, the cognitive manifestation of such tendencies may affect perceptions of self-efficacy and environmental support which, in turn, promote or deter domain adjustment and life satisfaction. Although the general, normative model of well-being (Lent, 2004) and its application to academic and work adjustment (Lent & Brown, 2006, 2008) are still relatively new, there have been several recent tests of the model in college and work settings. The first set of studies involved cross-sectional tests of the model in college samples. Lent et al. (2005) found that the model usefully predicted both academic and social domain satisfaction. Lent, Singley, Sheu, Schmidt, and Schmidt (2007) reported that an abbreviated form of the model fit the data well in predicting students' academic satisfaction. Ojeda, Flores, and Navarro (2011) tested the model in a sample of Mexican American college students, also finding good fit to the data in predicting academic satisfaction. A second set of cross-sectional studies tested the model in occupational samples. Studying U.S. and Italian school teachers, respectively, Duffy and Lent (2009) and Lent et al. (2011) found that the model accounted for substantial variation in job satisfaction. Those studies that included measures of life satisfaction reported that the model also explained sizable portions of the variance in overall satisfaction (Lent et al., 2005, 2011; Ojeda et al., 2011). There have also been several longitudinal tests of the model. In a study of U.S. college students, Singley, Lent, and Sheu (2010) collected data at two intervals 8 weeks apart. Lent, Taveira, Sheu, and Singley (2009) assessed students at two intervals, 15 weeks apart, in a sample of Portuguese college students. The latter study assessed academic adjustment as a latent variable, with academic satisfaction, stress, and perceived college adjustment as its observed indicators. Self-efficacy and environmental support were found to be significant predictors of subsequent goal progress in both studies. However, the predictors of satisfaction or
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adjustment varied across the two studies; goal progress and support were the key predictors in Singley et al. (2010), while selfefficacy and support were the significant predictors in Lent et al. (2009). Finally, Verbruggen and Sels (2010) used the model to study adult career clients in Belgium. They reported that the model predicted career satisfaction six months after counseling. In each of these studies, the model also predicted overall life satisfaction. The present set of studies was designed to replicate and extend existing research on domain-specific well-being model in several ways. First, there has thus far been only one cross-sectional study of the model outside of the U.S. (Lent et al., 2011) and only two non-U.S.-based longitudinal studies of the model (Lent et al., 2009; Verbruggen & Sels, 2010). The present studies were both conducted in Portugal. Study 1 employed a cross-sectional design to explore the concurrent relations among the theoretical variables. In Study 2, we used a longitudinal design to examine the nature of the temporal relations among the variables, as specified by the theory. Both studies were designed to examine the extent to which the findings of prior studies with U.S. college students would generalize to the Portuguese cultural context. Second, in both Studies 1 and 2, we sought to extend the findings of Lent et al. (2009) by “unpacking” college adjustment into two distinct but presumably interrelated indicators, academic satisfaction and stress. That is, Lent et al. had modeled satisfaction and stress (along with a measure of perceived adjustment) as observed indicators of a single latent adjustment construct. While the model generally fit the data well, such an approach did not examine the possibility that satisfaction and stress may each relate differentially to the variables that are posited to be their antecedents (e.g., self-efficacy) or consequents (overall life satisfaction). Thus, in both of the present studies, we modeled satisfaction and stress as separate but related aspects of academic domain adjustment (see Fig. 2). In sum, we tested the well-being model in two studies of Portuguese college students. Study 1 involved a straightforward cross-sectional test of the paths among the variables in Fig. 2. In Study 2, we tested the model with a longitudinal design. Specifically, as shown in Fig. 3, we examined the hypothesized lagged paths among the variables (e.g., the path from selfefficacy at time 1 to academic satisfaction at time 2), controlling for the autoregressive paths (e.g., the relation of academic satisfaction at time 1 with academic satisfaction at time 2). Inclusion of the autoregressive paths allows for the prediction of change in a dependent variable over time. We predicted that, with each of the autoregressive paths controlled: (a) life satisfaction at time 2 (T2) would be predicted by time 1 (T1) academic satisfaction, stress, goal progress, and trait positive affect; (b) academic satisfaction and stress at T2 would each be predicted by T1 progress at one's academic goals, self-efficacy, environmental supports, and positive affect; (c) T2 goal progress would be predicted by T1 self-efficacy and environmental supports; (d) T2 self-efficacy would be predicted by T1 environmental supports and positive affect; and (e) T2 environmental supports would be predicted by T1 positive affect. In keeping with the Lent et al. (2009) study that we sought to replicate and extend, we did not include outcome expectations in Study 1 or 2 because this variable has been found to be inconsistently predictive of goal progress and domain satisfaction in prior studies (e.g., Lent et al., 2005; Ojeda et al., 2011). Consistent with Lent et al. (2009), we also tested three sets of reciprocal relations, shown as dashed paths in Fig. 3: (a) T2 academic satisfaction and stress would be predicted by T1 life satisfaction, (b) T2 self-efficacy would be predicted by T1 goal progress; and (c) T2 positive affect would be predicted by T1 self-efficacy and environmental support. The first set of reciprocal relations reflects theoretical assumptions that domain and life satisfaction can affect one another. The second set is derived from general social cognitive theory, which holds that self-efficacy both enables and is strengthened by goal progress (i.e., where goal progress may be considered an aspect of performance accomplishment, one of the primary sources of efficacy information; Bandura, 1997). The third set of reciprocal paths address the possibility that self-efficacy and perceived
Positive Affect
.33* .44* Self-Efficacy Expectations
.24*
.14* .22* Environmental Support
.30*
.24*
Academic Satisfaction
.27*
.25* .23*
Academic Stress
.23*
Overall Life Satisfaction
.43* .10 .21*
.19*
.02
Goal Progress
Fig. 2. Adaptation of the social cognitive model of well-being to academic adjustment.
.17*
-.10
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365
Time 2
Time 1 .47
Positive Affect
Positive Affect .15
.16
Environmental Resources
.60
Environmental Resources
.67
Self-Efficacy
.18 Self-Efficacy
.17
.15 Goal Progress
Goal Progress .50 -.16 .20
Academic Satisfaction
.60
Academic Satisfaction
.23 .45
Academic Stress .18
Academic Stress .12 Life Satisfaction
Life Satisfaction .62
Fig. 3. Significant coefficients in the path analysis of the bidirectional model in Study 2 (p b .05, 1-tailed). Note. Dotted lines = autoregressive paths; dashed lines = hypothesized reciprocal paths. Covariances among variables at time 1 and time 2 are not shown.
environmental support can promote change in positive affect. These paths are consistent with assumptions that positive affect, though trait-like, is responsive to social and other factors (Watson, 2002) and that self-efficacy beliefs both contribute to and result partly from affective experience (Bandura, 1997). 2. Study 1 2.1. Method 2.1.1. Participants Participants were 366 students (94 women, 271 men, 1 did not specify gender) enrolled in undergraduate studies in engineering (n = 311), psychology (n = 48), or education (n = 7) at a university in northern Portugal. The sample included 125 freshmen, 132 sophomores, 83 juniors, 14 seniors, and 12 special status students. Their mean age was 22.91, SD = 4.35. Ninety-six percent of the participants were Portuguese citizens. 2.1.2. Procedure and instruments Students were recruited for participation within intact classes. Data were gathered during the 1st week of a Spring academic semester. Spring semester was selected so that all students, including freshmen, would have some prior college experience as a basis for their responses to the assessment. The measures, administered in Portuguese, included academic self-efficacy, goal progress, environmental support, satisfaction, and stress; life satisfaction and trait positive affect; and demographic and academic status information. The Portuguese versions of each scale were originally employed by Lent et al. (2009). Students did not receive incentives to participate in the study. For each scale, total scores were obtained by summing item responses and dividing by the number of items on the scale. Higher scores on all scales reflected more positive expectations or experiences (e.g., stronger selfefficacy, greater satisfaction, lower stress). Coefficient alpha estimates for each scale in the current sample are shown in Table 1; except for the academic stress scale, all values were above .80. Self-efficacy was assessed with an 11-item scale asking students to indicate their confidence in their ability to perform well academically and to cope with barriers to academic success (e.g., “do your best … during the next semester,” “deal with lack of support by the teachers or supervisors”). Responses were obtained along a 10-point scale, ranging from no confidence (0) to complete confidence (9). Prior versions of this scale have yielded adequate reliability estimates and theory-consistent relations with
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Table 1 Means, standard deviations, correlations, and internal consistency estimates: Study 1. Variable
1
1. 2. 3. 4. 5. 6. 7.
– .48 .28 .46 .46 .48 .27
Self-efficacy Goal progress Support Acad. satisfac. Stress Positive affect Life satisfac.
2 – .33 .40 .43 .46 .18
3
– .44 .23 .33 .31
4
– .46 .48 .37
5
– .45 .40
6
– .42
7
M
SD
α
–
6.05 3.34 3.62 3.60 3.21 3.30 4.43
1.27 .53 .57 .57 .59 .53 1.13
.88 .86 .81 .83 .62 .85 .85
Note. N = 366. Acad. satisfac. = academic satisfaction; Life satisfac. = life satisfaction. All correlations are significant, p b .001.
measures of academic outcomes (e.g., Lent et al., 2005). Lent et al. (2009) reported coefficient alphas for the Portuguese version of the academic self-efficacy scale of .87 and .90. Goal progress was assessed with an 8-item measure asking students to indicate the amount of progress they were making at a variety of academic goals (e.g., “actively participate in class”). Each goal statement was rated along a scale from 1 (no progress) to 5 (excellent progress). Lent et al. (2005) reported that this measure produced internal consistency reliability estimates of .84 to .86 and correlated with measures of academic self-efficacy, outcome expectations, environmental supports, and domain satisfaction. The Portuguese version of this scale has yielded coefficient alpha values of .80 and .85 (Lent et al., 2009). Environmental supports in the academic domain were assessed with a 9-item measure listing a variety of conditions that may support students' academic progress (e.g., “I am encouraged by my friends to go on with my studies”.) Participants indicated their level of agreement with each statement, from 1 (strongly disagree) to 5 (strongly agree). Lent et al. (2005) found internal consistency reliability estimates of .81 and .84 and reported that the scale correlated with measures of self-efficacy, outcome expectations, goal progress, and domain satisfaction. The Portuguese version of the scale has yielded internal consistency estimates of .76 and .81 (Lent et al., 2009). Academic domain satisfaction was assessed with a 7-item measure that asked students to indicate their level of satisfaction with various aspects of their academic life (e.g., “In general, I am satisfied with my academic life”) using a 1 (strongly disagree) to 5 (strongly agree) scale. Lent et al. (2005) reported reliability estimates of .86 and .87 and found that the scale correlated with measures of positive affect, social domain satisfaction, and overall life satisfaction. The Portuguese version of this scale has produced alpha coefficients of .85 and .89 (Lent et al., 2009). Academic stress was assessed with a Portuguese version of the Perceived Stress Scale (PSS; Cohen, Kamarck, & Mermelstein, 1983). Lent et al. (2009) modified this 4-item general stress measure by linking the items specifically to stress experienced in relation to academics (e.g., “How often did you feel that academic difficulties were piling up in such a way that you could not overcome them?”). Ratings were made on a 1–5 scale, from 1 = never to 5 = frequently. Item responses were reversed so that higher scores would reflect less stress. Cohen et al. (1983) found that the PSS yielded reliability estimates of .84 and above and correlated with indicators of general distress (e.g., depression) and physical problems. Other researchers have also used it to reflect college adjustment (e.g., Aspinwall & Taylor, 1992). The Portuguese version employed by Lent et al. yielded alpha values of .75 and .76. The Positive Affect (PA) scale of the Positive and Negative Affect Schedule (Watson, Clark, & Tellegen, 1988) was used to assess the tendency to experience positive emotions. Participants indicated the extent to which they generally feel 10 positive emotions (e.g., “proud”) using a 5-point continuum (1 = very little or not at all; 5 = extremely). This scale has yielded adequate internal consistency and stability coefficients and correlated as expected with measures of emotional distress and extraversion (Watson et al., 1988; Watson & Clark, 1992). Lent et al. (2005) found that the PA scale was related to life satisfaction as well as academic selfefficacy and environmental supports. The internal consistency estimates of this scale in Lent et al. (2009) were .86 at each of two assessment intervals. Overall life satisfaction was measured with the Satisfaction With Life Scale (SWLS; Diener, Emmons, Larsen, & Griffen, 1985). The scale asks participants to rate their level of agreement with five statements (e.g., “I am satisfied with my life”) along a 7-point scale (1= strongly disagree; 7 = strongly agree). The SWLS has yielded adequate internal consistency, temporal stability, and validity estimates (e.g., Compton, Smith, Cornish, & Qualls, 1996; Diener et al., 1985). The coefficient alpha estimates of the Portuguese version used by Lent et al. (2009) were .88 and .91.
2.2. Results Table 1 presents the means, standard deviations, coefficient alpha values, and correlations among the scales in Study 1. To test the well-being model, we performed a path analysis using the covariance matrices of the observed variables and maximum likelihood estimation in EQS Version 6.1 (Bentler & Wu, 2005). The fit of the hypothesized model, shown in Fig. 2, was assessed with the comparative fit index (CFI), the standardized root mean squared residual (SRMR) index, and the root mean square error of approximation (RMSEA). Hu and Bentler (1999) suggested that SRMR values close to .08 in combination with CFI values close to .95 or RMSEA values close to .06 imply good model-data fit. Given indications of multivariate non-normality (Mardia's normalized estimate N 5), the robust method was used to calculate CFI and RMSEA values.
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Results of the path analysis indicated that the model of well-being provided acceptable fit to the data on the SRMR (.05) and CFI (.94) indices, Satorra–Bentler (S–B) χ2 (3, N = 366) = 32.92, p b .001. The RMSEA value (.16) was, however, higher than optimal. Fig. 2 contains the path coefficients for this analysis. The Lagrange Multiplier (LM) test revealed that the addition of a path from positive affect to goal progress could improve model fit. Such a path may be considered conceptually reasonable given that the tendency toward optimism associated with positive affect could promote more favorable appraisals of one's goal progress. In addition, prior research has found that positive affect correlates substantially with measures of goal progress (e.g., Lent et al., 2009). When this path was added to the original model, all of the fit indices improved (SRMR = .02, CFI = .99, RMSEA = .10, Satorra–Bentler scaled χ2 [2, N = 366] = 9.10, p b .05), though the RMSEA value suggested that there was still room for improvement in fit. The path coefficients, shown in Fig. 2, revealed that most of the theoretical predictors accounted for unique variance in academic satisfaction, academic stress, and life satisfaction. In particular, academic satisfaction was significantly predicted by selfefficacy, environmental support, and positive affect; only goal progress did not produce a significant path to academic satisfaction. Academic stress was significantly predicted by self-efficacy, goal progress, and positive affect. The error covariance between academic satisfaction and stress was significant. The relation of support to stress was indirect only (via self-efficacy, goal progress, and academic satisfaction). Overall life satisfaction was, as expected, significantly predicted by academic satisfaction, stress, and positive affect. Goal progress was, however, related to life satisfaction only indirectly, via academic stress. The set of predictors accounted for 37%, 29%, and 27% of the variance, respectively, in academic satisfaction, stress, and life satisfaction. Goal progress was significantly predicted by both self-efficacy and support (R2 = .28); the addition of a path to goal progress from positive affect, suggested by the LM test, explained an additional 5% of the variance in goal progress. Both support and positive affect produced significant paths to self-efficacy support (R2 = .26), and positive affect was also related to support (R2 = .11). On balance, positive affect produced significant direct paths to each of the other variables in the model. However, each of the indicators of wellbeing (academic satisfaction, stress, and life satisfaction) was also predicted by additional, non-trait variables. Thus, the findings support the view that well-being is linked to trait as well as to cognitive, behavioral, and environmental variables. They also support the view that part of the relation of traits to well-being is mediated by these other mechanisms (Lent, 2004). 3. Study 2 3.1. Method 3.1.1. Participants Participants were 158 students (91 women, 67 men) enrolled in undergraduate studies in either psychology (n = 80) or engineering (n = 78) at a university in northern Portugal. The sample included 85 freshmen and 73 sophomores, with a mean age of 21.33, SD = 4.77. Ninety-five percent of the participants were Portuguese citizens. 3.1.2. Procedure and instruments Students were recruited for participation within intact classes and did not receive incentives to participate in the study. Data were gathered at each of two assessments, during the 1st and 16th weeks of the same (Spring) academic semester. We assumed that a 15week interval might allow us to capture some variability in domain and life satisfaction (which are often fairly stable over brief time periods) in freshmen and sophomores, many of whom might still be adjusting to the academic demands of college life. Participants were administered all measures (the same ones used in Study 1) at both time points. Internal consistency (coefficient alpha) reliability estimates for each scale at T1 and T2, respectively, were as follows: academic self-efficacy (.90, .92), goal progress (.87, .86), environmental support (.79, .81), academic satisfaction (.87, .88), academic stress (.66, .61), life satisfaction (.87, .87), and trait positive affect (.85, .87). Demographic and academic status information was also gathered. 3.2. Results Table 2 contains the means, standard deviations, and correlations among the measures at T1 and T2. Similar to the procedures of Lent et al. (2009), we tested the temporal predominance among the variables in the well-being model by comparing the fit of three model variations. The first was a base model containing covariances among the T1 variables, covariances among the errors of the T2 variables, and autoregressive paths, indexing the relation of each T1 variable to the same variable at T2. The second, unidirectional model, included the base model plus the hypothesized lagged paths from the T1 to T2 variables. The third, bidirectional model, shown in Fig. 3, added to the unidirectional model the hypothesized reciprocal paths from T1 goal progress to T2 selfefficacy, T1 life satisfaction to T2 academic satisfaction and stress, and T1 self-efficacy and supports to T2 positive affect. The second and third models were used to test whether particular sets of relationships are unidirectional (e.g., positive affect is an antecedent of self-efficacy but not vice versa) or bidirectional (e.g., self-efficacy and positive affect relate to one another reciprocally). All models were tested using the path analysis procedures of EQS 6.1 (Bentler & Wu, 2005), covariance matrices, and observed variables representing each theoretical construct. As in Study 1, three primary fit indices were employed: SRMR, CFI, and RMSEA. We used robust maximum likelihood estimation to calculate CFI and RMSEA values given indications of multivariate nonnormality (Mardia's normalized estimate was slightly elevated). The base model yielded adequate CFI (.95) but non-optimal SRMR (.12) and RMSEA (.09) values; Satorra–Bentler scaled χ 2 (42) = 95.23, p b .001. The autoregressive path coefficients for positive affect, environmental support, self-efficacy, goal progress, academic satisfaction, academic stress, and life satisfaction were, respectively, .58, .65, .66, .65, .72, .57, and .62. Thus, scores on these variables were fairly stable over the 15-week interval.
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Table 2 Means, standard deviations, and correlations among predictor and criterion variables: Study 2. Variable
1
1. T1 Self-efficacy 2. T1 Goal progress 3. T1 Support 4. T1 Acad. satisfac. 5. T1 Stress 6. T1 Positive affect 7. T1 Life satisfac. 8. T2 Self-efficacy 9. T2 Goal progress 10. T2 Support 11. T2 Acad. satisfac. 12. T2 Stress 13. T2 Positive affect 14. T2 Life satisfac.
– .60 .47 .54 .52 .57 .39 .72 .54 .37 .48 .50 .54 .41
2 – .56 .61 .46 .68 .49 .46 .68 .50 .53 .38 .54 .39
3
– .65 .35 .48 .53 .39 .52 .69 .63 .24 .48 .48
4
– .40 .61 .51 .48 .54 .57 .77 .30 .46 .45
5
– .48 .40 .50 .34 .39 .42 .62 .48 .40
6
– .51 .48 .55 .45 .46 .36 .65 .39
7
– .29 .42 .44 .46 .37 .39 .69
8
– .58 .44 .53 .48 .62 .41
9
– .43 .53 .33 .63 .42
10
– .64 .30 .49 .50
11
– .36 .54 .55
12
– .44 .50
13
– .49
14
M
SD
–
6.35 3.44 3.80 3.79 3.35 3.43 4.70 5.94 3.35 3.81 3.79 3.36 3.38 4.64
1.26 .56 .54 .63 .63 .51 1.16 1.31 .52 .51 .60 .60 .55 1.16
Note. N = 158. T1 = time 1; T2 = time 2; acad. = academic; satisfac. = satisfaction. All correlations are significant, p b .001.
The unidirectional model fit the data well on the CFI (.97) and SRMR (.07) indices, though the RMSEA value suggested less than optimal fit (.09), Satorra–Bentler scaled χ 2 (25) = 55.89, p b .001. A comparison of the unidirectional and base models using the corrected difference in Satorra–Bentler χ 2 test (Satorra & Bentler, 2001) revealed that the unidirectional model fit the data better than did the base model, Δχ 2 (17) = 39.37, p b .05. The bidirectional model produced good fit to the data across all three indices, CFI = .99, SRMR = .04, RMSEA = .07; Satorra–Bentler scaled χ 2 (20) = 36.95, p b .05. It also demonstrated improved fit relative both to the base model, Δχ 2 (22) = 58.37, p b .05, and the unidirectional model, Δχ 2 (5) = 18.52, p b .05. Thus, these findings suggest that the bidirectional model provides the best fit to the data. Figure 3 presents the path coefficients in the bidirectional model. Consistent with expectations, and controlling for the autoregressive paths, T2 social support was predicted by positive affect (.16) and T2 goal progress was predicted by environmental supports (.17) and self-efficacy (.15); T2 academic satisfaction was predicted by supports (.20); T2 stress was predicted by selfefficacy (.23); and T2 life satisfaction was predicted by academic satisfaction (.12). Contrary to expectations, however, T1 positive affect did not produce significant paths to T2 self-efficacy, academic satisfaction, stress, or life satisfaction. Likewise, T1 goal progress did not explain additional significant variance in T2 academic satisfaction, stress, or life satisfaction. Thus, neither positive affect nor goal progress added directly to the prediction of the positive adjustment outcomes, above and beyond the other variables. In addition, only support (but not self-efficacy) was significantly predictive of academic satisfaction, and only self-efficacy (but not support) yielded a significant, positive path coefficient predicting stress. The significant negative path from support to stress (−.16) was likely the result of statistical suppression given the positive bivariate relationship between these two variables (r = .24). Among the bidirectional paths, we observed that self-efficacy (.18) and support (.15) were predictive of positive affect; and life satisfaction was predictive of stress (.18) but not academic satisfaction (.05). Goal progress did not yield a significant reciprocal path to self-efficacy (−.05). Finally, we tested a modified version of the bidirectional model, adding a direct path from positive affect to goal progress. This path, suggested by the LM indices in Study 1, was added because of the possibility that accounting for such a direct relationship could enhance model fit. Although this modified model generally produced good fit indices (CFI = .99, SRMR = .04, RMSEA = .07; Satorra–Bentler scaled χ 2 (19) = 35.09, p b .05), it did not improve upon the fit of the original bidirectional model, Δχ 2 (1) = 1.86, p N .05; neither was the path from T1 positive affect to T2 goal progress significant (.10). Thus, the addition of a path from positive affect to goal progress did not improve the fit of the well-being model in the longitudinal analysis. 4. General discussion The present set of studies offered two methodological approaches — one cross-sectional, one longitudinal — to testing the social cognitive model of well-being. Both studies extended prior research with Portuguese college students by disaggregating the construct of college adjustment into the distinct but related components of academic satisfaction and academic stress. We will consider findings that were unique to each study as well as those that were consistent across studies, focusing, in turn, on (a) the more global, cross-domain variables (positive affect and life satisfaction), (b) the domain-specific outcomes (academic satisfaction and stress), and (c) relations among the social cognitive predictors. 4.1. Positive affect and life satisfaction: unidirectional versus bidirectional relations Although the data generally fit the model well in both studies, there were notable variations across the two studies in terms of which specific variables produced significant paths to particular well-being outcomes. Consistent with theoretical assumptions that satisfaction in central life domains is a source of overall life satisfaction (Lent, 2004), both studies found support for paths from academic satisfaction to life satisfaction. However, T1 life satisfaction did not produce a significant reciprocal path to T2
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academic satisfaction in Study 2. Thus, as in Lent et al. (2009), our results favor the “bottom–up” over the “top–down” conception whereby domain-specific satisfaction contributes to rather than merely reflects overall life satisfaction (Heller, Watson, & Ilies, 2004). Study 1 found that, in addition to academic satisfaction, life satisfaction was predicted by low levels of academic stress, though this pattern was not confirmed in Study 2. Rather, in the later study, the relation between the two constructs appeared to be unidirectional, with greater life satisfaction at T1 predicting lower stress at T2. The paths involving trait positive affect were also noteworthy in the two studies. In Study 1, positive affect was found to be linked to academic satisfaction, stress, and life satisfaction both directly as well as indirectly, via each of the social cognitive variables. However, positive affect appeared to be a much less central predictor in Study 2, where it produced no direct relations to the academic and life functioning criteria. The relations of positive affect to self-efficacy and social support in Study 2 were particularly interesting. The cross-sectional findings in Study 1 established relations of positive affect both to self-efficacy and social support, but Study 2 suggested that at least some of these relations were bidirectional. Specifically, T1 positive affect was significantly predictive of T2 support (but not self-efficacy), whereas T1 self-efficacy and support each predicted T2 positive affect. These findings are partly consistent with those of Lent et al. (2009), who also found that self-efficacy and social support were each predictive of change in positive affect. 4.2. Prediction of academic satisfaction and stress The social cognitive variables (support, self-efficacy, and goal progress) were fairly consistent predictors of academic satisfaction and/or stress in Study 1. In particular, self-efficacy and support (though not goal progress) produced significant paths to academic satisfaction, and self-efficacy and goal progress (though not support) were significantly predictive of academic stress. Academic satisfaction and stress were, as expected, also related to one another. However, a more restricted set of relations was obtained in Study 2's longitudinal design, where only T1 support produced a significant path to T2 academic satisfaction, and only T1 self-efficacy yielded a significant path to T2 academic stress. T1 goal progress did not explain additional significant variation in either T2 academic satisfaction or stress. These findings are partly consistent with those of Lent et al.'s (2009) longitudinal study, which also found that self-efficacy and support were both good predictors of academic adjustment, but that goal progress did not explain unique outcome variance. 4.3. Relations among the social cognitive variables The relations among the three social cognitive predictors were fully consistent with theoretical predictions in Study 1, but only partially so in Study 2. Specifically, in Study 1, social support predicted goal progress both directly as well as indirectly through self-efficacy. In Study 2, both T1 support and self-efficacy produced direct paths to T2 goal progress, but the path from T1 support to T2 self-efficacy was not significant. Thus, support was linked to goal progress only directly in Study 2. Although we had expected the paths between self-efficacy and goal progress to be bidirectional, we only found support for a unidirectional path from self-efficacy to goal progress. Except for the non-significant path from T1 support to T2 self-efficacy, the relations among support, self-efficacy, and goal progress are consistent with those reported by Lent et al. (2009). 4.4. Cross-sectional versus longitudinal findings Taken together, the two sets of findings confirm the view that cross-sectional designs have a tendency to overestimate the relations among variables, for example, by failing to account for autoregressive paths. They also do not enable an examination of temporal predominance between predictors and criterion variables because all variables are assessed at a single point in time. Longitudinal designs can overcome such drawbacks by assessing the variables at more than one time point. This is consistent with the logic of causation in that predictors, or putative causes, precede outcome variables, or presumed effects, in time. Although longitudinal designs are not sufficient to establish causality, they can better test hypothesized temporal relations among variables than can cross-sectional designs. They also address the issue of whether the predictors can forecast change in a dependent variable. This is a particularly rigorous test in situations, such as the present one, where the dependent variables are relatively stable over time. Several sets of relations were consistently found across the two studies and may, therefore, be considered particularly robust. In particular, academic satisfaction predicted overall life satisfaction; social support predicted academic satisfaction and goal progress; self-efficacy predicted academic stress and goal progress; and positive affect predicted social support. Positive affect, as noted earlier, appeared to be a more central predictor of the well-being outcomes and social cognitive variables in the cross-sectional than in the longitudinal design. Moreover, the longitudinal design revealed that the relation of positive affect to self-efficacy and support was not merely unidirectional: self-efficacy and support were, in fact, predictive of change in positive affect. Thus, contrary to the common conception of positive affect as a trait, the present results suggest that positive affect may be responsive to, as well as predictive of, cognitive and social variables. When coupled with the findings of Lent et al. (2009), these results may have useful implications for the conceptualization and modification of trait positive affect, as discussed below. 4.5. Limitations and implications On balance, the present findings extend those of Lent et al. (2009) in several ways. For example, the current studies examined the social cognitive model of well-being both cross-sectionally and longitudinally; tested model fit in samples composed of
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students who were relatively new to college (Study 2) as well as students of varying college tenure (Study 1); and employed samples with larger percentages of male participants (74% and 42% in Study 1 and 2, respectively) than was the case in the Lent et al. study (14%). The current studies also examined domain-specific functioning in a more nuanced fashion than did Lent et al. by assessing academic satisfaction and stress as separate indicators of well-being. The present findings may offer several useful implications for theory, research, and practice. At a theoretical level, they suggest that well-being can usefully be conceived as a multidimensional concept that includes aspects of domain-specific satisfaction, affect, and overall life satisfaction. They also demonstrate the value of integrating social cognitive and personality predictors, rather than assuming that domain and life satisfaction are merely reflections of hard-wired and relatively static traits (Lykken & Tellegen, 1996). Such an integrative view may help to explain the ways in which personality operates with other variables to determine well-being and also suggests ways in which certain traits may change over time. Along with other recent research on the social cognitive model (see Sheu & Lent, 2009), these findings also point to the merit of future studies testing the model in diverse cultural contexts. While additional cross-sectional and longitudinal studies are warranted, experimental studies would be particularly valuable both to test the model's causal hypotheses and to assess the efficacy of model-derived intervention elements. Although practice implications must be offered tentatively, the current findings suggest that efforts to promote academic selfefficacy and social supports may be good ways to reduce stress and bolster satisfaction related to the student role. Perhaps because academics occupy such a central place in most college students' lives, increased satisfaction with one's academic life may, in turn, boost overall life satisfaction. That is, if students are happy in the academic domain, they are more likely to be happy overall. While some prior findings suggest a predominant unidirectional path from life satisfaction to domain satisfaction (Judge & Watanabe, 1993), the current findings and those of Lent et al. (2009) suggest that the temporal path can, under certain conditions, proceed from domain satisfaction to life satisfaction. On the other hand, in Study 2 we did find a significant path from T1 life satisfaction to (lower) stress at T2, suggesting that students who were happier overall were less likely to experience academic stress. Given our findings, and those of Lent et al. (2009), that self-efficacy and social support each predict change in positive affect, it may make sense to conceptualize positive affect as an indicator (rather than only as a predictor) of college adjustment. That is, efforts to promote academic self-efficacy and social support may not only help to regulate domain-specific affective outcomes, such as academic satisfaction and stress; they may also have the potential to facilitate more global affective outcomes, such as trait positive affect. Other researchers have noted the potential for positive affect to be modified by various (e.g., cognitive, social, physical) means (e.g., Watson, 2002), and some findings confirm that positive affect is not immutable (e.g., Veenhoven, 1994). Whether efficacy- and support-building interventions can, in fact, elevate positive affect is a topic worthy of further research. Contrary to hypotheses, goal progress was predictive only of academic stress in Study 1 and yielded no significant unique paths to the well-being outcomes in Study 2. Lent et al. (2009) similarly found that goal progress possessed limited predictive utility relative to the other social cognitive variables. By contrast, other studies have found that goal progress does sometimes uniquely predict domain and/or life satisfaction (e.g., Lent et al., 2005, 2007; Singley et al., 2010). It is possible that methodological variations among studies help to account for the inconsistency of goal progress as a predictor. For example, in some studies, like the present ones, goal progress is assessed in terms of normative behaviors required for college success. In other studies, it is measured more idiographically in relation to students' personally-defined goals (e.g., Singley et al.). Goal progress is generally regarded as a potentially valuable predictor of well-being (e.g., Ryan & Deci, 2000). It would, therefore, be useful for further research to identify measurement and other methodological conditions (e.g., the time frame within which goal progress and well-being indices are assessed) that may moderate the relation of goal progress to well-being. The current findings should be viewed in light of the limitations of the two studies. In particular, all variables were assessed subjectively and from a single vantage point — although it may be argued that individuals comprise the best source of data regarding their own sense of well-being (cf. Irwin, Kammann, & Dixon, 1979) and social cognitive expectancies. Second, it would be inappropriate to make causal inferences either from cross-sectional or longitudinal designs, although the latter can test whether particular variables are consistent with temporal ordering assumptions. Third, the academic stress scale produced marginal internal consistency reliability estimates in both studies. Fourth, although the bidirectional version of the well-being model provided good overall fit to the data in Study 2, several of the individual paths did not receive support. Further research using different operationalizations of certain variables (e.g., goal progress, academic stress) would be useful in order to determine whether model revision (e.g., trimming of non-essential predictors) would be in order. These limitations notwithstanding, the present findings, along with those of Lent et al. (2009), suggest that the integrative social cognitive model helps explain aspects of both domain-specific and overall well-being in Portuguese college students. Additional research is needed to further test the predictive utility of the model in samples of students and workers at different levels of educational and career development and in diverse cultural contexts. It would also be useful to study the restorative version of the well-being model (Lent, 2004) to examine the extent to which it helps to capture the process of wellbeing recovery when persons are faced with role transitions (e.g., retirement) or negative life events (e.g., involuntary job loss) that can destabilize domain or life satisfaction. References Aspinwall, L. G., & Taylor, S. E. (1992). Modeling cognitive adaptation: A longitudinal investigation of the impact of individual differences and coping on college adjustment and performance. Journal of Personality and Social Psychology, 63, 989–1003. Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ: Prentice-Hall. Bandura, A. (1997). 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Two tests of the social cognitive model of well-being in Portuguese college students☆ Robert W. Lent a,⁎, Maria do Céu Taveira b, Cristina Lobo b a b
Department of Counseling, Higher Education, and Special Education, University of Maryland, USA Department of Psychology, University of Minho, Braga, Portugal
a r t i c l e
i n f o
Article history: Received 15 August 2011 Available online 2 September 2011 Keywords: Social cognitive career theory Self-efficacy Academic satisfaction Academic stress Life satisfaction Positive affect
a b s t r a c t A social cognitive model of well-being (Lent & Brown, 2006, 2008) was tested in two studies (one cross-sectional, one longitudinal) with Portuguese college students. Participants in Study 1 (N = 366) completed measures of academic self-efficacy, environmental support, goal progress, academic satisfaction and stress, trait positive affect, and overall life satisfaction at a single assessment. Those in Study 2 (N = 158) took the same measures at each of two time points, 15 weeks apart. A modified version of the model improved the fit to the data in Study 1. Optimal fit in Study 2 was achieved with a bidirectional version that modeled reciprocal relations of positive affect to both self-efficacy and support. The predictors in both studies accounted for substantial portions of the variance in academic domain satisfaction, academic stress, and life satisfaction. Implications for future research on the social cognitive model in educational and work settings are considered. © 2011 Elsevier Inc. All rights reserved.
1. Introduction In its original formulation, social cognitive career theory (SCCT; Lent, Brown, & Hackett, 1994) included models aimed at understanding educational/vocational interest, choice, and performance. The three models have received a good deal of empirical attention, with recent meta-analyses summarizing much of the research testing SCCT's interest, choice (Sheu et al., 2010) and performance hypotheses (Brown et al., 2008; Brown, Lent, Telander, & Tramayne, 2011). Lent and Brown (2006, 2008) recently proposed a fourth SCCT model, one focused on explaining satisfaction and other aspects of positive adjustment within educational and vocational domains. This latest model represents an effort to extend Lent's (2004) more general model of normative wellbeing to the specific contexts of school and work. Like the preceding SCCT models, the satisfaction or well-being model draws in part from general social cognitive theory (Bandura, 1986, 1997). It also attempts to integrate a variety of perspectives and constructs from the literatures on subjective and psychological well-being. By drawing together cognitive, behavioral, and social variables that have been shown to be linked to domain and overall life satisfaction, the model focuses on those aspects of the person (e.g., self-efficacy, goal setting and progress) and environment (access to goal-relevant resources, like tutoring and mentoring) over which students, workers, and their support systems can exert some degree of control — and that may, therefore, help to inform interventions for promoting well-being in academic and work settings. In addition, by including personality dispositions that have been linked to domain and overall well-being, the model also acknowledges sources of well-being that are typically thought to be relatively resistant to personal agency and traditional interventions. Lent's (2004) general model of normative well-being is illustrated in Fig. 1. According to this model, domain satisfaction and affect (e.g., low levels of perceived stress) are reciprocally related to overall life satisfaction (see the double arrows on path 1). In other words, feeling satisfied and comfortable within one's central life domains is likely to promote an overall sense of happiness, ☆ The findings reported herein were presented at the annual meeting of the American Psychological Association, Washington, DC, August, 2011. ⁎ Corresponding author at: Department of Counseling, Higher Education, and Special Education, University of Maryland, College Park, MD 20742, USA. E-mail address: [email protected] (R.W. Lent). 0001-8791/$ – see front matter © 2011 Elsevier Inc. All rights reserved. doi:10.1016/j.jvb.2011.08.009
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Personality Traits & Affective Dispositions (e.g., PA, NA, extraversion, neuroticism, optimism, GSE)
15 Self-Efficacy Expectations
5
14 Environmental Supports & Resources (e.g., goal-relevant resources, modeling, encouragement)
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2
3
9
13
Participation in/ Progress at GoalDirected Activity
11
4
Domain-Specific Satisfaction, Situational Affect
1
Overall Life Satisfaction
10 12 Outcome Expectations
8
6 7
Fig. 1. Integrative model of well-being under normative life conditions. PA = positive affect; NA = negative affect; GSE = generalized self-efficacy. Adapted from “Toward a unifying theoretical and practical perspective on well-being and psychosocial adjustment,” by R.W. Lent, 2004, Journal of Counseling Psychology, 51, p. 500. Reprinted with permission.
and overall happiness is, in turn, likely to nurture well-being within one's more specific life domains. Both domain adjustment (i.e., satisfaction and stress) (path 2) and overall life satisfaction (path 3) are also assumed to be affected by personality/affective traits, such as positive and negative affect. That is, one's tendency to experience positive or negative affect across situations is likely to influence perceptions of well-being generally as well as in specific life domains. In addition to personality, domain adjustment is posited to be affected by (a) setting and making progress at personally relevant goals (path 4), (b) having strong self-efficacy beliefs in relation to one's task requirements and goals (path 5), (c) holding positive outcome expectations (e.g., favorable beliefs about the outcomes of one's educational efforts; path 6), and (d) having access to environmental resources for promoting self-efficacy and goal pursuit (path 7). Goal setting and progress are assumed to affect one's overall sense of life satisfaction both directly (path 8) as well as indirectly via domain adjustment. The model also posits ways in which the predictors of domain adjustment and life satisfaction interrelate. In particular, goal setting and progress are assumed to be influenced by self-efficacy (path 9), outcome expectations (path 10), and environmental support (path 11). For example, people are more likely to make progress on their valued goals to the extent that they possess favorable views of their task and goal-relevant self-efficacy, foresee positive outcomes as resulting from their efforts, and perceive that they have the supports needed to achieve their goals. Outcome expectations are bolstered by the presence of environmental supports (path 12) and self-efficacy (path 13). That is, people are more likely to hold optimistic beliefs about the consequences of their actions if they view their environments as supportive and themselves as efficacious. Like outcome expectations, self-efficacy is seen as responsive to environmental supports (path 14), as people derive a sense of their efficacy partly from the persuasive messages they receive about their capabilities and from other forms of support (e.g., modeling) provided by their interpersonal environments. Finally, personality traits such as positive and negative affect may influence domain and life satisfaction both directly, as noted earlier, and indirectly through their relations to self-efficacy (path 15) and environmental support (path 16). For example, because positive and negative affect incline people to view themselves and their environments in generally positive or negative ways, the cognitive manifestation of such tendencies may affect perceptions of self-efficacy and environmental support which, in turn, promote or deter domain adjustment and life satisfaction. Although the general, normative model of well-being (Lent, 2004) and its application to academic and work adjustment (Lent & Brown, 2006, 2008) are still relatively new, there have been several recent tests of the model in college and work settings. The first set of studies involved cross-sectional tests of the model in college samples. Lent et al. (2005) found that the model usefully predicted both academic and social domain satisfaction. Lent, Singley, Sheu, Schmidt, and Schmidt (2007) reported that an abbreviated form of the model fit the data well in predicting students' academic satisfaction. Ojeda, Flores, and Navarro (2011) tested the model in a sample of Mexican American college students, also finding good fit to the data in predicting academic satisfaction. A second set of cross-sectional studies tested the model in occupational samples. Studying U.S. and Italian school teachers, respectively, Duffy and Lent (2009) and Lent et al. (2011) found that the model accounted for substantial variation in job satisfaction. Those studies that included measures of life satisfaction reported that the model also explained sizable portions of the variance in overall satisfaction (Lent et al., 2005, 2011; Ojeda et al., 2011). There have also been several longitudinal tests of the model. In a study of U.S. college students, Singley, Lent, and Sheu (2010) collected data at two intervals 8 weeks apart. Lent, Taveira, Sheu, and Singley (2009) assessed students at two intervals, 15 weeks apart, in a sample of Portuguese college students. The latter study assessed academic adjustment as a latent variable, with academic satisfaction, stress, and perceived college adjustment as its observed indicators. Self-efficacy and environmental support were found to be significant predictors of subsequent goal progress in both studies. However, the predictors of satisfaction or
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adjustment varied across the two studies; goal progress and support were the key predictors in Singley et al. (2010), while selfefficacy and support were the significant predictors in Lent et al. (2009). Finally, Verbruggen and Sels (2010) used the model to study adult career clients in Belgium. They reported that the model predicted career satisfaction six months after counseling. In each of these studies, the model also predicted overall life satisfaction. The present set of studies was designed to replicate and extend existing research on domain-specific well-being model in several ways. First, there has thus far been only one cross-sectional study of the model outside of the U.S. (Lent et al., 2011) and only two non-U.S.-based longitudinal studies of the model (Lent et al., 2009; Verbruggen & Sels, 2010). The present studies were both conducted in Portugal. Study 1 employed a cross-sectional design to explore the concurrent relations among the theoretical variables. In Study 2, we used a longitudinal design to examine the nature of the temporal relations among the variables, as specified by the theory. Both studies were designed to examine the extent to which the findings of prior studies with U.S. college students would generalize to the Portuguese cultural context. Second, in both Studies 1 and 2, we sought to extend the findings of Lent et al. (2009) by “unpacking” college adjustment into two distinct but presumably interrelated indicators, academic satisfaction and stress. That is, Lent et al. had modeled satisfaction and stress (along with a measure of perceived adjustment) as observed indicators of a single latent adjustment construct. While the model generally fit the data well, such an approach did not examine the possibility that satisfaction and stress may each relate differentially to the variables that are posited to be their antecedents (e.g., self-efficacy) or consequents (overall life satisfaction). Thus, in both of the present studies, we modeled satisfaction and stress as separate but related aspects of academic domain adjustment (see Fig. 2). In sum, we tested the well-being model in two studies of Portuguese college students. Study 1 involved a straightforward cross-sectional test of the paths among the variables in Fig. 2. In Study 2, we tested the model with a longitudinal design. Specifically, as shown in Fig. 3, we examined the hypothesized lagged paths among the variables (e.g., the path from selfefficacy at time 1 to academic satisfaction at time 2), controlling for the autoregressive paths (e.g., the relation of academic satisfaction at time 1 with academic satisfaction at time 2). Inclusion of the autoregressive paths allows for the prediction of change in a dependent variable over time. We predicted that, with each of the autoregressive paths controlled: (a) life satisfaction at time 2 (T2) would be predicted by time 1 (T1) academic satisfaction, stress, goal progress, and trait positive affect; (b) academic satisfaction and stress at T2 would each be predicted by T1 progress at one's academic goals, self-efficacy, environmental supports, and positive affect; (c) T2 goal progress would be predicted by T1 self-efficacy and environmental supports; (d) T2 self-efficacy would be predicted by T1 environmental supports and positive affect; and (e) T2 environmental supports would be predicted by T1 positive affect. In keeping with the Lent et al. (2009) study that we sought to replicate and extend, we did not include outcome expectations in Study 1 or 2 because this variable has been found to be inconsistently predictive of goal progress and domain satisfaction in prior studies (e.g., Lent et al., 2005; Ojeda et al., 2011). Consistent with Lent et al. (2009), we also tested three sets of reciprocal relations, shown as dashed paths in Fig. 3: (a) T2 academic satisfaction and stress would be predicted by T1 life satisfaction, (b) T2 self-efficacy would be predicted by T1 goal progress; and (c) T2 positive affect would be predicted by T1 self-efficacy and environmental support. The first set of reciprocal relations reflects theoretical assumptions that domain and life satisfaction can affect one another. The second set is derived from general social cognitive theory, which holds that self-efficacy both enables and is strengthened by goal progress (i.e., where goal progress may be considered an aspect of performance accomplishment, one of the primary sources of efficacy information; Bandura, 1997). The third set of reciprocal paths address the possibility that self-efficacy and perceived
Positive Affect
.33* .44* Self-Efficacy Expectations
.24*
.14* .22* Environmental Support
.30*
.24*
Academic Satisfaction
.27*
.25* .23*
Academic Stress
.23*
Overall Life Satisfaction
.43* .10 .21*
.19*
.02
Goal Progress
Fig. 2. Adaptation of the social cognitive model of well-being to academic adjustment.
.17*
-.10
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365
Time 2
Time 1 .47
Positive Affect
Positive Affect .15
.16
Environmental Resources
.60
Environmental Resources
.67
Self-Efficacy
.18 Self-Efficacy
.17
.15 Goal Progress
Goal Progress .50 -.16 .20
Academic Satisfaction
.60
Academic Satisfaction
.23 .45
Academic Stress .18
Academic Stress .12 Life Satisfaction
Life Satisfaction .62
Fig. 3. Significant coefficients in the path analysis of the bidirectional model in Study 2 (p b .05, 1-tailed). Note. Dotted lines = autoregressive paths; dashed lines = hypothesized reciprocal paths. Covariances among variables at time 1 and time 2 are not shown.
environmental support can promote change in positive affect. These paths are consistent with assumptions that positive affect, though trait-like, is responsive to social and other factors (Watson, 2002) and that self-efficacy beliefs both contribute to and result partly from affective experience (Bandura, 1997). 2. Study 1 2.1. Method 2.1.1. Participants Participants were 366 students (94 women, 271 men, 1 did not specify gender) enrolled in undergraduate studies in engineering (n = 311), psychology (n = 48), or education (n = 7) at a university in northern Portugal. The sample included 125 freshmen, 132 sophomores, 83 juniors, 14 seniors, and 12 special status students. Their mean age was 22.91, SD = 4.35. Ninety-six percent of the participants were Portuguese citizens. 2.1.2. Procedure and instruments Students were recruited for participation within intact classes. Data were gathered during the 1st week of a Spring academic semester. Spring semester was selected so that all students, including freshmen, would have some prior college experience as a basis for their responses to the assessment. The measures, administered in Portuguese, included academic self-efficacy, goal progress, environmental support, satisfaction, and stress; life satisfaction and trait positive affect; and demographic and academic status information. The Portuguese versions of each scale were originally employed by Lent et al. (2009). Students did not receive incentives to participate in the study. For each scale, total scores were obtained by summing item responses and dividing by the number of items on the scale. Higher scores on all scales reflected more positive expectations or experiences (e.g., stronger selfefficacy, greater satisfaction, lower stress). Coefficient alpha estimates for each scale in the current sample are shown in Table 1; except for the academic stress scale, all values were above .80. Self-efficacy was assessed with an 11-item scale asking students to indicate their confidence in their ability to perform well academically and to cope with barriers to academic success (e.g., “do your best … during the next semester,” “deal with lack of support by the teachers or supervisors”). Responses were obtained along a 10-point scale, ranging from no confidence (0) to complete confidence (9). Prior versions of this scale have yielded adequate reliability estimates and theory-consistent relations with
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Table 1 Means, standard deviations, correlations, and internal consistency estimates: Study 1. Variable
1
1. 2. 3. 4. 5. 6. 7.
– .48 .28 .46 .46 .48 .27
Self-efficacy Goal progress Support Acad. satisfac. Stress Positive affect Life satisfac.
2 – .33 .40 .43 .46 .18
3
– .44 .23 .33 .31
4
– .46 .48 .37
5
– .45 .40
6
– .42
7
M
SD
α
–
6.05 3.34 3.62 3.60 3.21 3.30 4.43
1.27 .53 .57 .57 .59 .53 1.13
.88 .86 .81 .83 .62 .85 .85
Note. N = 366. Acad. satisfac. = academic satisfaction; Life satisfac. = life satisfaction. All correlations are significant, p b .001.
measures of academic outcomes (e.g., Lent et al., 2005). Lent et al. (2009) reported coefficient alphas for the Portuguese version of the academic self-efficacy scale of .87 and .90. Goal progress was assessed with an 8-item measure asking students to indicate the amount of progress they were making at a variety of academic goals (e.g., “actively participate in class”). Each goal statement was rated along a scale from 1 (no progress) to 5 (excellent progress). Lent et al. (2005) reported that this measure produced internal consistency reliability estimates of .84 to .86 and correlated with measures of academic self-efficacy, outcome expectations, environmental supports, and domain satisfaction. The Portuguese version of this scale has yielded coefficient alpha values of .80 and .85 (Lent et al., 2009). Environmental supports in the academic domain were assessed with a 9-item measure listing a variety of conditions that may support students' academic progress (e.g., “I am encouraged by my friends to go on with my studies”.) Participants indicated their level of agreement with each statement, from 1 (strongly disagree) to 5 (strongly agree). Lent et al. (2005) found internal consistency reliability estimates of .81 and .84 and reported that the scale correlated with measures of self-efficacy, outcome expectations, goal progress, and domain satisfaction. The Portuguese version of the scale has yielded internal consistency estimates of .76 and .81 (Lent et al., 2009). Academic domain satisfaction was assessed with a 7-item measure that asked students to indicate their level of satisfaction with various aspects of their academic life (e.g., “In general, I am satisfied with my academic life”) using a 1 (strongly disagree) to 5 (strongly agree) scale. Lent et al. (2005) reported reliability estimates of .86 and .87 and found that the scale correlated with measures of positive affect, social domain satisfaction, and overall life satisfaction. The Portuguese version of this scale has produced alpha coefficients of .85 and .89 (Lent et al., 2009). Academic stress was assessed with a Portuguese version of the Perceived Stress Scale (PSS; Cohen, Kamarck, & Mermelstein, 1983). Lent et al. (2009) modified this 4-item general stress measure by linking the items specifically to stress experienced in relation to academics (e.g., “How often did you feel that academic difficulties were piling up in such a way that you could not overcome them?”). Ratings were made on a 1–5 scale, from 1 = never to 5 = frequently. Item responses were reversed so that higher scores would reflect less stress. Cohen et al. (1983) found that the PSS yielded reliability estimates of .84 and above and correlated with indicators of general distress (e.g., depression) and physical problems. Other researchers have also used it to reflect college adjustment (e.g., Aspinwall & Taylor, 1992). The Portuguese version employed by Lent et al. yielded alpha values of .75 and .76. The Positive Affect (PA) scale of the Positive and Negative Affect Schedule (Watson, Clark, & Tellegen, 1988) was used to assess the tendency to experience positive emotions. Participants indicated the extent to which they generally feel 10 positive emotions (e.g., “proud”) using a 5-point continuum (1 = very little or not at all; 5 = extremely). This scale has yielded adequate internal consistency and stability coefficients and correlated as expected with measures of emotional distress and extraversion (Watson et al., 1988; Watson & Clark, 1992). Lent et al. (2005) found that the PA scale was related to life satisfaction as well as academic selfefficacy and environmental supports. The internal consistency estimates of this scale in Lent et al. (2009) were .86 at each of two assessment intervals. Overall life satisfaction was measured with the Satisfaction With Life Scale (SWLS; Diener, Emmons, Larsen, & Griffen, 1985). The scale asks participants to rate their level of agreement with five statements (e.g., “I am satisfied with my life”) along a 7-point scale (1= strongly disagree; 7 = strongly agree). The SWLS has yielded adequate internal consistency, temporal stability, and validity estimates (e.g., Compton, Smith, Cornish, & Qualls, 1996; Diener et al., 1985). The coefficient alpha estimates of the Portuguese version used by Lent et al. (2009) were .88 and .91.
2.2. Results Table 1 presents the means, standard deviations, coefficient alpha values, and correlations among the scales in Study 1. To test the well-being model, we performed a path analysis using the covariance matrices of the observed variables and maximum likelihood estimation in EQS Version 6.1 (Bentler & Wu, 2005). The fit of the hypothesized model, shown in Fig. 2, was assessed with the comparative fit index (CFI), the standardized root mean squared residual (SRMR) index, and the root mean square error of approximation (RMSEA). Hu and Bentler (1999) suggested that SRMR values close to .08 in combination with CFI values close to .95 or RMSEA values close to .06 imply good model-data fit. Given indications of multivariate non-normality (Mardia's normalized estimate N 5), the robust method was used to calculate CFI and RMSEA values.
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Results of the path analysis indicated that the model of well-being provided acceptable fit to the data on the SRMR (.05) and CFI (.94) indices, Satorra–Bentler (S–B) χ2 (3, N = 366) = 32.92, p b .001. The RMSEA value (.16) was, however, higher than optimal. Fig. 2 contains the path coefficients for this analysis. The Lagrange Multiplier (LM) test revealed that the addition of a path from positive affect to goal progress could improve model fit. Such a path may be considered conceptually reasonable given that the tendency toward optimism associated with positive affect could promote more favorable appraisals of one's goal progress. In addition, prior research has found that positive affect correlates substantially with measures of goal progress (e.g., Lent et al., 2009). When this path was added to the original model, all of the fit indices improved (SRMR = .02, CFI = .99, RMSEA = .10, Satorra–Bentler scaled χ2 [2, N = 366] = 9.10, p b .05), though the RMSEA value suggested that there was still room for improvement in fit. The path coefficients, shown in Fig. 2, revealed that most of the theoretical predictors accounted for unique variance in academic satisfaction, academic stress, and life satisfaction. In particular, academic satisfaction was significantly predicted by selfefficacy, environmental support, and positive affect; only goal progress did not produce a significant path to academic satisfaction. Academic stress was significantly predicted by self-efficacy, goal progress, and positive affect. The error covariance between academic satisfaction and stress was significant. The relation of support to stress was indirect only (via self-efficacy, goal progress, and academic satisfaction). Overall life satisfaction was, as expected, significantly predicted by academic satisfaction, stress, and positive affect. Goal progress was, however, related to life satisfaction only indirectly, via academic stress. The set of predictors accounted for 37%, 29%, and 27% of the variance, respectively, in academic satisfaction, stress, and life satisfaction. Goal progress was significantly predicted by both self-efficacy and support (R2 = .28); the addition of a path to goal progress from positive affect, suggested by the LM test, explained an additional 5% of the variance in goal progress. Both support and positive affect produced significant paths to self-efficacy support (R2 = .26), and positive affect was also related to support (R2 = .11). On balance, positive affect produced significant direct paths to each of the other variables in the model. However, each of the indicators of wellbeing (academic satisfaction, stress, and life satisfaction) was also predicted by additional, non-trait variables. Thus, the findings support the view that well-being is linked to trait as well as to cognitive, behavioral, and environmental variables. They also support the view that part of the relation of traits to well-being is mediated by these other mechanisms (Lent, 2004). 3. Study 2 3.1. Method 3.1.1. Participants Participants were 158 students (91 women, 67 men) enrolled in undergraduate studies in either psychology (n = 80) or engineering (n = 78) at a university in northern Portugal. The sample included 85 freshmen and 73 sophomores, with a mean age of 21.33, SD = 4.77. Ninety-five percent of the participants were Portuguese citizens. 3.1.2. Procedure and instruments Students were recruited for participation within intact classes and did not receive incentives to participate in the study. Data were gathered at each of two assessments, during the 1st and 16th weeks of the same (Spring) academic semester. We assumed that a 15week interval might allow us to capture some variability in domain and life satisfaction (which are often fairly stable over brief time periods) in freshmen and sophomores, many of whom might still be adjusting to the academic demands of college life. Participants were administered all measures (the same ones used in Study 1) at both time points. Internal consistency (coefficient alpha) reliability estimates for each scale at T1 and T2, respectively, were as follows: academic self-efficacy (.90, .92), goal progress (.87, .86), environmental support (.79, .81), academic satisfaction (.87, .88), academic stress (.66, .61), life satisfaction (.87, .87), and trait positive affect (.85, .87). Demographic and academic status information was also gathered. 3.2. Results Table 2 contains the means, standard deviations, and correlations among the measures at T1 and T2. Similar to the procedures of Lent et al. (2009), we tested the temporal predominance among the variables in the well-being model by comparing the fit of three model variations. The first was a base model containing covariances among the T1 variables, covariances among the errors of the T2 variables, and autoregressive paths, indexing the relation of each T1 variable to the same variable at T2. The second, unidirectional model, included the base model plus the hypothesized lagged paths from the T1 to T2 variables. The third, bidirectional model, shown in Fig. 3, added to the unidirectional model the hypothesized reciprocal paths from T1 goal progress to T2 selfefficacy, T1 life satisfaction to T2 academic satisfaction and stress, and T1 self-efficacy and supports to T2 positive affect. The second and third models were used to test whether particular sets of relationships are unidirectional (e.g., positive affect is an antecedent of self-efficacy but not vice versa) or bidirectional (e.g., self-efficacy and positive affect relate to one another reciprocally). All models were tested using the path analysis procedures of EQS 6.1 (Bentler & Wu, 2005), covariance matrices, and observed variables representing each theoretical construct. As in Study 1, three primary fit indices were employed: SRMR, CFI, and RMSEA. We used robust maximum likelihood estimation to calculate CFI and RMSEA values given indications of multivariate nonnormality (Mardia's normalized estimate was slightly elevated). The base model yielded adequate CFI (.95) but non-optimal SRMR (.12) and RMSEA (.09) values; Satorra–Bentler scaled χ 2 (42) = 95.23, p b .001. The autoregressive path coefficients for positive affect, environmental support, self-efficacy, goal progress, academic satisfaction, academic stress, and life satisfaction were, respectively, .58, .65, .66, .65, .72, .57, and .62. Thus, scores on these variables were fairly stable over the 15-week interval.
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Table 2 Means, standard deviations, and correlations among predictor and criterion variables: Study 2. Variable
1
1. T1 Self-efficacy 2. T1 Goal progress 3. T1 Support 4. T1 Acad. satisfac. 5. T1 Stress 6. T1 Positive affect 7. T1 Life satisfac. 8. T2 Self-efficacy 9. T2 Goal progress 10. T2 Support 11. T2 Acad. satisfac. 12. T2 Stress 13. T2 Positive affect 14. T2 Life satisfac.
– .60 .47 .54 .52 .57 .39 .72 .54 .37 .48 .50 .54 .41
2 – .56 .61 .46 .68 .49 .46 .68 .50 .53 .38 .54 .39
3
– .65 .35 .48 .53 .39 .52 .69 .63 .24 .48 .48
4
– .40 .61 .51 .48 .54 .57 .77 .30 .46 .45
5
– .48 .40 .50 .34 .39 .42 .62 .48 .40
6
– .51 .48 .55 .45 .46 .36 .65 .39
7
– .29 .42 .44 .46 .37 .39 .69
8
– .58 .44 .53 .48 .62 .41
9
– .43 .53 .33 .63 .42
10
– .64 .30 .49 .50
11
– .36 .54 .55
12
– .44 .50
13
– .49
14
M
SD
–
6.35 3.44 3.80 3.79 3.35 3.43 4.70 5.94 3.35 3.81 3.79 3.36 3.38 4.64
1.26 .56 .54 .63 .63 .51 1.16 1.31 .52 .51 .60 .60 .55 1.16
Note. N = 158. T1 = time 1; T2 = time 2; acad. = academic; satisfac. = satisfaction. All correlations are significant, p b .001.
The unidirectional model fit the data well on the CFI (.97) and SRMR (.07) indices, though the RMSEA value suggested less than optimal fit (.09), Satorra–Bentler scaled χ 2 (25) = 55.89, p b .001. A comparison of the unidirectional and base models using the corrected difference in Satorra–Bentler χ 2 test (Satorra & Bentler, 2001) revealed that the unidirectional model fit the data better than did the base model, Δχ 2 (17) = 39.37, p b .05. The bidirectional model produced good fit to the data across all three indices, CFI = .99, SRMR = .04, RMSEA = .07; Satorra–Bentler scaled χ 2 (20) = 36.95, p b .05. It also demonstrated improved fit relative both to the base model, Δχ 2 (22) = 58.37, p b .05, and the unidirectional model, Δχ 2 (5) = 18.52, p b .05. Thus, these findings suggest that the bidirectional model provides the best fit to the data. Figure 3 presents the path coefficients in the bidirectional model. Consistent with expectations, and controlling for the autoregressive paths, T2 social support was predicted by positive affect (.16) and T2 goal progress was predicted by environmental supports (.17) and self-efficacy (.15); T2 academic satisfaction was predicted by supports (.20); T2 stress was predicted by selfefficacy (.23); and T2 life satisfaction was predicted by academic satisfaction (.12). Contrary to expectations, however, T1 positive affect did not produce significant paths to T2 self-efficacy, academic satisfaction, stress, or life satisfaction. Likewise, T1 goal progress did not explain additional significant variance in T2 academic satisfaction, stress, or life satisfaction. Thus, neither positive affect nor goal progress added directly to the prediction of the positive adjustment outcomes, above and beyond the other variables. In addition, only support (but not self-efficacy) was significantly predictive of academic satisfaction, and only self-efficacy (but not support) yielded a significant, positive path coefficient predicting stress. The significant negative path from support to stress (−.16) was likely the result of statistical suppression given the positive bivariate relationship between these two variables (r = .24). Among the bidirectional paths, we observed that self-efficacy (.18) and support (.15) were predictive of positive affect; and life satisfaction was predictive of stress (.18) but not academic satisfaction (.05). Goal progress did not yield a significant reciprocal path to self-efficacy (−.05). Finally, we tested a modified version of the bidirectional model, adding a direct path from positive affect to goal progress. This path, suggested by the LM indices in Study 1, was added because of the possibility that accounting for such a direct relationship could enhance model fit. Although this modified model generally produced good fit indices (CFI = .99, SRMR = .04, RMSEA = .07; Satorra–Bentler scaled χ 2 (19) = 35.09, p b .05), it did not improve upon the fit of the original bidirectional model, Δχ 2 (1) = 1.86, p N .05; neither was the path from T1 positive affect to T2 goal progress significant (.10). Thus, the addition of a path from positive affect to goal progress did not improve the fit of the well-being model in the longitudinal analysis. 4. General discussion The present set of studies offered two methodological approaches — one cross-sectional, one longitudinal — to testing the social cognitive model of well-being. Both studies extended prior research with Portuguese college students by disaggregating the construct of college adjustment into the distinct but related components of academic satisfaction and academic stress. We will consider findings that were unique to each study as well as those that were consistent across studies, focusing, in turn, on (a) the more global, cross-domain variables (positive affect and life satisfaction), (b) the domain-specific outcomes (academic satisfaction and stress), and (c) relations among the social cognitive predictors. 4.1. Positive affect and life satisfaction: unidirectional versus bidirectional relations Although the data generally fit the model well in both studies, there were notable variations across the two studies in terms of which specific variables produced significant paths to particular well-being outcomes. Consistent with theoretical assumptions that satisfaction in central life domains is a source of overall life satisfaction (Lent, 2004), both studies found support for paths from academic satisfaction to life satisfaction. However, T1 life satisfaction did not produce a significant reciprocal path to T2
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academic satisfaction in Study 2. Thus, as in Lent et al. (2009), our results favor the “bottom–up” over the “top–down” conception whereby domain-specific satisfaction contributes to rather than merely reflects overall life satisfaction (Heller, Watson, & Ilies, 2004). Study 1 found that, in addition to academic satisfaction, life satisfaction was predicted by low levels of academic stress, though this pattern was not confirmed in Study 2. Rather, in the later study, the relation between the two constructs appeared to be unidirectional, with greater life satisfaction at T1 predicting lower stress at T2. The paths involving trait positive affect were also noteworthy in the two studies. In Study 1, positive affect was found to be linked to academic satisfaction, stress, and life satisfaction both directly as well as indirectly, via each of the social cognitive variables. However, positive affect appeared to be a much less central predictor in Study 2, where it produced no direct relations to the academic and life functioning criteria. The relations of positive affect to self-efficacy and social support in Study 2 were particularly interesting. The cross-sectional findings in Study 1 established relations of positive affect both to self-efficacy and social support, but Study 2 suggested that at least some of these relations were bidirectional. Specifically, T1 positive affect was significantly predictive of T2 support (but not self-efficacy), whereas T1 self-efficacy and support each predicted T2 positive affect. These findings are partly consistent with those of Lent et al. (2009), who also found that self-efficacy and social support were each predictive of change in positive affect. 4.2. Prediction of academic satisfaction and stress The social cognitive variables (support, self-efficacy, and goal progress) were fairly consistent predictors of academic satisfaction and/or stress in Study 1. In particular, self-efficacy and support (though not goal progress) produced significant paths to academic satisfaction, and self-efficacy and goal progress (though not support) were significantly predictive of academic stress. Academic satisfaction and stress were, as expected, also related to one another. However, a more restricted set of relations was obtained in Study 2's longitudinal design, where only T1 support produced a significant path to T2 academic satisfaction, and only T1 self-efficacy yielded a significant path to T2 academic stress. T1 goal progress did not explain additional significant variation in either T2 academic satisfaction or stress. These findings are partly consistent with those of Lent et al.'s (2009) longitudinal study, which also found that self-efficacy and support were both good predictors of academic adjustment, but that goal progress did not explain unique outcome variance. 4.3. Relations among the social cognitive variables The relations among the three social cognitive predictors were fully consistent with theoretical predictions in Study 1, but only partially so in Study 2. Specifically, in Study 1, social support predicted goal progress both directly as well as indirectly through self-efficacy. In Study 2, both T1 support and self-efficacy produced direct paths to T2 goal progress, but the path from T1 support to T2 self-efficacy was not significant. Thus, support was linked to goal progress only directly in Study 2. Although we had expected the paths between self-efficacy and goal progress to be bidirectional, we only found support for a unidirectional path from self-efficacy to goal progress. Except for the non-significant path from T1 support to T2 self-efficacy, the relations among support, self-efficacy, and goal progress are consistent with those reported by Lent et al. (2009). 4.4. Cross-sectional versus longitudinal findings Taken together, the two sets of findings confirm the view that cross-sectional designs have a tendency to overestimate the relations among variables, for example, by failing to account for autoregressive paths. They also do not enable an examination of temporal predominance between predictors and criterion variables because all variables are assessed at a single point in time. Longitudinal designs can overcome such drawbacks by assessing the variables at more than one time point. This is consistent with the logic of causation in that predictors, or putative causes, precede outcome variables, or presumed effects, in time. Although longitudinal designs are not sufficient to establish causality, they can better test hypothesized temporal relations among variables than can cross-sectional designs. They also address the issue of whether the predictors can forecast change in a dependent variable. This is a particularly rigorous test in situations, such as the present one, where the dependent variables are relatively stable over time. Several sets of relations were consistently found across the two studies and may, therefore, be considered particularly robust. In particular, academic satisfaction predicted overall life satisfaction; social support predicted academic satisfaction and goal progress; self-efficacy predicted academic stress and goal progress; and positive affect predicted social support. Positive affect, as noted earlier, appeared to be a more central predictor of the well-being outcomes and social cognitive variables in the cross-sectional than in the longitudinal design. Moreover, the longitudinal design revealed that the relation of positive affect to self-efficacy and support was not merely unidirectional: self-efficacy and support were, in fact, predictive of change in positive affect. Thus, contrary to the common conception of positive affect as a trait, the present results suggest that positive affect may be responsive to, as well as predictive of, cognitive and social variables. When coupled with the findings of Lent et al. (2009), these results may have useful implications for the conceptualization and modification of trait positive affect, as discussed below. 4.5. Limitations and implications On balance, the present findings extend those of Lent et al. (2009) in several ways. For example, the current studies examined the social cognitive model of well-being both cross-sectionally and longitudinally; tested model fit in samples composed of
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students who were relatively new to college (Study 2) as well as students of varying college tenure (Study 1); and employed samples with larger percentages of male participants (74% and 42% in Study 1 and 2, respectively) than was the case in the Lent et al. study (14%). The current studies also examined domain-specific functioning in a more nuanced fashion than did Lent et al. by assessing academic satisfaction and stress as separate indicators of well-being. The present findings may offer several useful implications for theory, research, and practice. At a theoretical level, they suggest that well-being can usefully be conceived as a multidimensional concept that includes aspects of domain-specific satisfaction, affect, and overall life satisfaction. They also demonstrate the value of integrating social cognitive and personality predictors, rather than assuming that domain and life satisfaction are merely reflections of hard-wired and relatively static traits (Lykken & Tellegen, 1996). Such an integrative view may help to explain the ways in which personality operates with other variables to determine well-being and also suggests ways in which certain traits may change over time. Along with other recent research on the social cognitive model (see Sheu & Lent, 2009), these findings also point to the merit of future studies testing the model in diverse cultural contexts. While additional cross-sectional and longitudinal studies are warranted, experimental studies would be particularly valuable both to test the model's causal hypotheses and to assess the efficacy of model-derived intervention elements. Although practice implications must be offered tentatively, the current findings suggest that efforts to promote academic selfefficacy and social supports may be good ways to reduce stress and bolster satisfaction related to the student role. Perhaps because academics occupy such a central place in most college students' lives, increased satisfaction with one's academic life may, in turn, boost overall life satisfaction. That is, if students are happy in the academic domain, they are more likely to be happy overall. While some prior findings suggest a predominant unidirectional path from life satisfaction to domain satisfaction (Judge & Watanabe, 1993), the current findings and those of Lent et al. (2009) suggest that the temporal path can, under certain conditions, proceed from domain satisfaction to life satisfaction. On the other hand, in Study 2 we did find a significant path from T1 life satisfaction to (lower) stress at T2, suggesting that students who were happier overall were less likely to experience academic stress. Given our findings, and those of Lent et al. (2009), that self-efficacy and social support each predict change in positive affect, it may make sense to conceptualize positive affect as an indicator (rather than only as a predictor) of college adjustment. That is, efforts to promote academic self-efficacy and social support may not only help to regulate domain-specific affective outcomes, such as academic satisfaction and stress; they may also have the potential to facilitate more global affective outcomes, such as trait positive affect. Other researchers have noted the potential for positive affect to be modified by various (e.g., cognitive, social, physical) means (e.g., Watson, 2002), and some findings confirm that positive affect is not immutable (e.g., Veenhoven, 1994). Whether efficacy- and support-building interventions can, in fact, elevate positive affect is a topic worthy of further research. Contrary to hypotheses, goal progress was predictive only of academic stress in Study 1 and yielded no significant unique paths to the well-being outcomes in Study 2. Lent et al. (2009) similarly found that goal progress possessed limited predictive utility relative to the other social cognitive variables. By contrast, other studies have found that goal progress does sometimes uniquely predict domain and/or life satisfaction (e.g., Lent et al., 2005, 2007; Singley et al., 2010). It is possible that methodological variations among studies help to account for the inconsistency of goal progress as a predictor. For example, in some studies, like the present ones, goal progress is assessed in terms of normative behaviors required for college success. In other studies, it is measured more idiographically in relation to students' personally-defined goals (e.g., Singley et al.). Goal progress is generally regarded as a potentially valuable predictor of well-being (e.g., Ryan & Deci, 2000). It would, therefore, be useful for further research to identify measurement and other methodological conditions (e.g., the time frame within which goal progress and well-being indices are assessed) that may moderate the relation of goal progress to well-being. The current findings should be viewed in light of the limitations of the two studies. In particular, all variables were assessed subjectively and from a single vantage point — although it may be argued that individuals comprise the best source of data regarding their own sense of well-being (cf. Irwin, Kammann, & Dixon, 1979) and social cognitive expectancies. Second, it would be inappropriate to make causal inferences either from cross-sectional or longitudinal designs, although the latter can test whether particular variables are consistent with temporal ordering assumptions. Third, the academic stress scale produced marginal internal consistency reliability estimates in both studies. Fourth, although the bidirectional version of the well-being model provided good overall fit to the data in Study 2, several of the individual paths did not receive support. Further research using different operationalizations of certain variables (e.g., goal progress, academic stress) would be useful in order to determine whether model revision (e.g., trimming of non-essential predictors) would be in order. These limitations notwithstanding, the present findings, along with those of Lent et al. (2009), suggest that the integrative social cognitive model helps explain aspects of both domain-specific and overall well-being in Portuguese college students. 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