When confidence comes and goes: How variation in self-efficacy moderates stressor-strain relationships.

Journal of Occupational Health Psychology 2015, Vol. 20, No. 3, 359 –376

© 2015 American Psychological Association 1076-8998/15/$12.00 http://dx.doi.org/10.1037/a0038588

When Confidence Comes and Goes: How Variation in Self-Efficacy Moderates Stressor–Strain Relationships Ann C. Peng

John M. Schaubroeck

Western University

Michigan State University

Jia Lin Xie This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.

University of Toronto Inconsistent published findings regarding a proposed buffering role of self-efficacy in stress coping led us to develop a model in which within-person variability in self-efficacy over time affects how individuals’ mean levels of self-efficacy moderate the relationship between demands and psychological symptoms. Results from two independent samples (manufacturing workers and college students) supported the hypothesized interaction between demands, self-efficacy mean level, and self-efficacy variability. Demands were more positively associated with psychological strain among those with high and stable self-efficacy than those with high and variable self-efficacy. We discuss the implications of intrapersonal variability in self-efficacy for research on stress coping. Keywords: efficacy variability, job demand, psychological strain, self-efficacy

tik, O’Driscoll, & Anderson, 2011), others did not (e.g., Jex & Gudanowski, 1992; Schreurs, van Emmerik, Notelaers, & De Witte, 2010). There are also studies suggesting that self-efficacy may exaggerate the adverse influence of stressors on health outcomes (Lu, Chang, & Lai, 2011; Toker, Gavish, & Biron, 2013; Xie, 2007). However, research to date has only examined the moderating role of self-efficacy level, which is often measured at a single point of time. We propose that this provides an incomplete picture of how self-efficacy impacts stress coping and health outcomes because it does not consider that individuals’ efficacy levels vary over time. Accumulating evidence suggests that temporal fluctuation in self-views, in particular self-esteem, may represent an individual characteristic that affects how individuals cope with demands (e.g., Crocker & Wolfe, 2001; Updegraff, Emanuel, Suh, & Gallagher, 2010; Wang, Hamaker, & Bergeman, 2012). This is consistent with broader perspectives on individual dispositions that recognize the importance of within-person variation in conjunction with mean levels in explaining individuals’ beliefs and behaviors (e.g., Fleeson, 2004; Fleeson & Leicht, 2006; Larsen, Augustine, & Prizmic, 2009), and with other research indicating that variability on dimensions of personality represent distinct, meaningful, and stable traits (Eid & Diener, 1999; Kuppens, Oravecz, & Tuerlinckx, 2010; Larsen, 1987; see Scott, Barnes, & Wagner, 2012). We sought to integrate research on intrapersonal variability in self-views with the literature examining self-efficacy as a moderating variable of stressor-strain relationships. We develop and test a model in which efficacy mean levels and variability jointly interact with demands in influencing health symptoms. Although the chief aim of this research is to determine whether self-efficacy variability plays a substantial role in resolving the ambiguity inherent in previous findings concerning the moderating role of self-efficacy level in stressor-strain relationships, it makes three additional contributions to the literature. First, whereas cross-

Self-efficacy refers to the belief that one can effectively utilize his or her resources to achieve certain outcomes (Bandura, 1997). This concept has been extended to individuals’ competency beliefs pertaining to broad sets of activities applicable to social roles (e.g., employee, leader, and student), or role-specific self-efficacy (see Schaubroeck, Kim, & Peng, 2012 for a review). In the literature on stress and health, self-efficacy is often conceptualized as an important personal resource that helps individuals cope with rolerelated demands (Hobfoll, 2002; Meier, Semmer, Elfering, & Jacobshagen, 2008; Rubino, Perry, Milam, Spitzmueller, & Zapf, 2012). Research suggests that individuals with higher self-efficacy are more likely to appraise demanding tasks as challenges rather than as threats and use more adaptive coping strategies (Chemers, Hu, & Garcia, 2001; Stumpf, Brief, & Hartman, 1987), and as a result they are less likely to experience health problems. Despite the theoretical foundation for self-efficacy as a factor that weakens stressor-strain relationships, empirical support for this hypothesis has been limited (see the review by Sonnentag & Frese, 2003). The research has been conducted primarily in relation to job self-efficacy, a type of role-specific self-efficacy that concerns employees’ beliefs about their competencies in accomplishing job-related tasks (Gist & Mitchell, 1992; Jex & Gudanowski, 1992). Whereas some studies supported the theorized moderating effect of self-efficacy (e.g., Jex & Bliese, 1999; Pana-

This article was published Online First January 19, 2015. Ann C. Peng, Richard Ivey School of Business, Western University; John M. Schaubroeck, Department of Management, Michigan State University; Jia Lin Xie, Joseph L. Rotman School of Management, University of Toronto. Correspondence concerning this article should be addressed to Ann C. Peng, Richard Ivey School of Business, Western University, London ONT, N6G 0N1, Canada. E-mail: [email protected] 359

This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.

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PENG, SCHAUBROECK, AND XIE

sectional studies have characterized the efficacy-stress research to date, in two separate studies we used longitudinal designs. In analyzing the data, we took a cross-level approach to examine both within-person variation in demands and between-person differences in coping with demands. This approach alleviates concerns about common method bias and potential nonindependence between demands and self-efficacy that weaken inferences one can draw from cross-sectional studies. Second, extant research on self-concept variability has focused on self-esteem. We extend this research by examining whether findings concerning global selfconcept (i.e., self-esteem) generalize to domain-specific selfefficacy. Because domain-specific efficacy beliefs are associated with specific life roles (i.e., work role and student role), their fluctuation may have more direct relevance to understanding how individuals cope with role-related demands (Meier et al., 2008). Finally, existing research on self-esteem variability has been conducted exclusively among college students. We present two studies, one examining the role of job self-efficacy variability among factory workers and the other seeking to replicate the findings using a student sample.

Demands, Self-Efficacy, and Psychological Health According to the job demands-resources model (JDR) (Demerouti, Bakker, Nachreiner, & Schaufeli, 2001), people are more prone to psychological distress when they perceive heavy job demands combined with few resources needed to cope effectively with these demands, such as ample job control. In the recent years, scholars have extended the JDR model by taking into consideration the role of personal resources such as efficacy beliefs (e.g., Rubino et al., 2012; Xanthopoulou, Bakker, Demerouti, & Schaufeli, 2009). Within this perspective, strong competency beliefs are a personal resource that enables individuals to better cope with demanding situations. In comparison with individuals with lower self-efficacy, individuals with higher self-efficacy are more likely to invest extra effort in mobilizing their resources toward their goals, and they ultimately channel their energies toward a successful resolution of the difficulties they encounter (Bandura, 1982, 1986). The cognitive theory of stress provides an additional rationale for the moderating role of self-efficacy in coping with demands (Lazarus & Folkman, 1984). It argues that individuals with high self-efficacy tend to appraise demands as challenges rather than as threats to their well-being. For example, they may view an increase in responsibility for others, such as being assigned responsibility for supervising additional employees, as an opportunity for personal and career development. Such challenge appraisals tend to elicit problem-focused coping in which individuals focus on developing and implementing solutions that aid in reducing or eliminating stressors. Problem-focused coping is often seen to be instrumental for maintaining well-being in the face of chronic stressors (see a review by Folkman & Moskowitz, 2004). Supporting this perspective, Chemers et al. (2001) found that students with high academic efficacy tended to regard their first year of college as a challenge rather than a threat. Other research has reported that high efficacy individuals used more adaptive coping strategies, whereas those with low self-efficacy were more likely to use emotion-focused coping (Chwalisz, Altmaier, & Russell, 1992; Cicognani, 2011; Stumpf et al., 1987). With emotion-focused

coping, people seek to alleviate or express their anxiety without dealing directly with the problem that is at the root of their distress. Both self-efficacy theory and cognitive theory of stress thus suggest that demands have a less negative impact on well-being among individuals with higher self-efficacy. However, the existing evidence for an interactive effect of self-efficacy and demands in predicting health has been weak and equivocal. Jex and Bliese (1999) found that job self-efficacy moderated the influences of indicators of job demand, such as work hours, work overload, and task significance, on employee psychological strain. The demand was positively associated with strain among workers reporting low self-efficacy, but there was no such relationship among highefficacy workers. In contrast, Jex and Gudanowski (1992) found that job self-efficacy did not interact with job demands in predicting strain outcomes. In addition, Toker and colleagues (2013) reported that higher work load was associated with an increased risk of physical health problems among individuals with high levels of general self-efficacy, an overall assessment of one’s competency across different domains (Chen, Gully, & Eden, 2004). Other published tests of the interactive effect of demands and self-efficacy have yielded similarly mixed results (e.g., Heuven, Bakker, Schaufeli, & Huisman, 2006; Lu, Siu, & Cooper, 2005; Schaubroeck, Lam, & Xie, 2000). Lu and colleagues (2005) examined the stress buffering effect of managerial self-efficacy, which refers to “managers’ beliefs in successfully accomplishing the specific managerial tasks that are applicable to different contexts” (p. 573). They found that managerial self-efficacy alleviated the negative influence of job stressors on physical strain, but it did not moderate the relationships between job stressors and psychological strain or job satisfaction. Schaubroeck et al. (2000) reported that self-efficacy interacted with job demands to predict depression among bank service employees, reflecting a considerably weaker positive influence of job demands among those with high job self-efficacy. However, this interaction did not extend to other outcomes, including anxiety and absenteeism. In summary, whereas theory suggests that individuals with high self-efficacy will exhibit a weaker stressor-strain relationship than individuals with low self-efficacy, the predicted interaction is not well supported in the literature. However, research on stress and self-efficacy has examined only the level of self-efficacy, which is typically measured at a single point of time. In the next section, we argue that jointly considering the mean level and the variability of self-efficacy over time helps better understand the process through which self-efficacy influences coping with role-related demands.

Self-Concept Variability and Health Self-efficacy and self-esteem are both aspects of the broader metaconstruct of self-concept, which refers to “totality of an individual’s thoughts and feelings having reference to himself as an object” (Rosenberg, 1979, p. 7). However, they are distinct constructs. Self-esteem reflects a broader domain in that it refers to not only one’s belief in oneself as a competent person, as does general self-efficacy, but also a sense of overall self-worth (see Chen et al., 2004). Role-specific self-efficacy, on the other hand, is anchored on specific roles (e.g., employee, manager, or student). Therefore, role-specific self-efficacy may have more direct implications for coping with role-related demands than self-esteem (Chen, Casper,

SELF-CONCEPT VARIABILITY AND STRESS COPING

& Cortina, 2001; Judge, Jackson, Shaw, Scott, & Rich, 2007). We suggest that the principles underlying the influence of self-esteem variability on coping and health may apply to temporal variability in role specific self-efficacy. In the sections below we briefly review the existing research, and then present our theoretical development and hypotheses.


Intrapersonal Variability in Self-Esteem and Coping Although high self-esteem is often proposed as an indicator of better adjustment and health (Diener, 1984; Tennen & Affleck, 1993), it can adversely influence coping when it is unstable (Kernis, Lakey, & Heppner, 2008). Conceptualized as the fluctuation of one’s daily self-esteem over time, self-esteem variability is viewed a trait-like construct that is associated with maladaptive behavior and poor psychological health (Butler, Hokanson, & Flynn, 1994; Campbell et al., 1996; Roberts & Monroe, 1992). Studies have found that daily hassles were more strongly related to depression symptoms among persons with variable self-esteem than those with stable self-esteem (Butler et al., 1994; Kernis et al., 1998). A study by Meier, Semmer, and Hupfeld (2009) found that unfair treatment was more positively associated with depressive moods among individuals with high and variable self-esteem than among those with high and stable self-esteem. Other studies have tested the interaction between the mean level of self-esteem and the variability of self-esteem without considering perceived stressors. These studies found that variability in self-esteem is more positively related to psychological symptoms among high self-esteem people than among low self-esteem people (e.g., de Man, Gutiérrez, & Sterk, 2001; Kernis, Grannemann, & Mathis, 1991; Kernis et al., 2008). This literature overall suggests that self-esteem variability adversely influences coping with daily stressors, but primarily among individuals with high levels of self-esteem. Different perspectives have been advanced to explain how selfesteem variability arises, and some empirical evidence has supported each perspective (see Kernis et al., 2008 for a review). Some scholars have argued that temporal variation in self-esteem reflects self-uncertainty or lack of self-concept clarity (Baumgardner, 1990; Campbell, 1990; Campbell et al., 1996). Persons who have less crystallized self-knowledge are more likely to seek external feedback for self-evaluation, and reliance on such feedback introduces variation in their self-evaluations. A slightly different explanation, called the self-contingency perspective (Crocker & Wolfe, 2001), argues that the salience of the particular domain (e.g., work) to one’s self-concept and the consistency of the feedback from that domain (e.g., performance evaluation from supervisor) jointly determine the variation in one’s self-evaluation. More recently, Updegraff et al. (2010) proposed that individuals who base self-worth on general and abstract attributes (e.g., I am intelligent) have more stable self-esteem compared with those who construe self-worth based on concrete and specific facts (e.g., My performance evaluation is high). This is because individuals with more abstract bases of self-worth are less likely to view any specific events as disconfirming their general self-views which thus tend to remain stable over time. Despite the differences among these perspectives, they all suggest that individuals with variable self-esteem are more sensitive to feedback than individuals with stable self-esteem. Given that competence evaluation is

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central to the construct of self-esteem, we propose below that self-efficacy variability has similar implications for stress coping.

Intrapersonal Variability in Self-Efficacy and Coping In this study, we note that both the demands in a life domain (e.g., work load or responsibility for others) and the external resources individuals can use (e.g., instrumental support from others) may change over time. These changes can presage success or failure in coping, which may in turn influence efficacy beliefs in the same domain. Although mastery experiences often enhance self-efficacy and failure experiences reduce self-efficacy (Bandura, 1986, 1997), we propose that individuals differ in terms of the extent to which they adjust role-specific efficacy beliefs based on their experiences of coping with role-related demands. We argue that the amount of variability in one’s efficacy reports over time is a dispositional construct that represents an individual’s propensity to change efficacy beliefs when confronted with a more or less challenging environment. The study finding with older adults reported by Lang, Featherman, and Nesselroade (1997) is consistent with this view, in that within-person variability in social self-efficacy over a 7-month period differed systematically across individuals and was related to fluctuations in perceived availability for support from their social partners. In light of the evidence that self-efficacy has a significant degree of within-person variability over time, indexing self-efficacy at a single point in time may be insufficient for testing how selfefficacy influences coping and health over extended periods. We conceptualize efficacy variability as a dispositional tendency to reevaluate and change the level of confidence one has in his or her competencies over time. We argue in the next section that this variability has implications for understanding how individuals cope with role-related demands.

Interaction of Efficacy Mean Level, Efficacy Variability, and Demands Extrapolating from the self-esteem variability literature (e.g., Campbell, 1990; Kernis et al., 2008), efficacy variability may be seen to reflect psychological processes that are pertinent to coping with stressors in the domain corresponding the efficacy construct of interest. For example, academic self-efficacy variability is pertinent to coping with academic demands, whereas job self-efficacy variability affects coping with job demands. From this point forward, we use “efficacy mean level” and “efficacy variability” in lieu of “role-specific efficacy mean level” and “role-specific efficacy variability.” Similar to unstable self-esteem, variable selfefficacy connotes an aspect of competency evaluation that is not well anchored and is, therefore, sensitive to events and experiences that may have self-evaluative implications (Jordan, Spencer, Zanna, Hoshino-Browne, & Correll, 2003; Seery, Blascovich, Weisbuch, & Vick, 2004). People with more variable self-efficacy may modify their perceptions of competency in a domain when their prevailing beliefs are not confirmed by external cues. For example, variability could be triggered by struggles to perform well when facing high role demands even when one’s mean level of efficacy over time is high. This reliance on external cues may create uncertainty about one’s competency and diminish one’s sense of personal control over the outcomes, leaving one more


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vulnerable to stressors and in turn poor psychological health (Baumgardner, 1990; Campbell, 1990; Setterlund & Niedenthal, 1993). Individuals with either stable low or variable low efficacy have little confidence in their abilities to cope with demanding tasks, although those with variable low efficacy may experience a lift in efficacy beliefs when demands are low. Because their efficacy levels tend to be low when demands are high, individuals with either stable low or variable low efficacy may appraise high demands as a threat to well-being whereby they perceive insufficient resources to successfully manage the demands (Chemers et al., 2001; Folkman, 1984). Consequently, they engage in less adaptive and often maladaptive coping behaviors such as avoidance or emotion-focused coping (Chwalisz et al., 1992; Cicognani, 2011; Stumpf et al., 1987). Both avoidance and emotion-focused coping lead individuals to pursue temporary psychological comfort instead of dealing directly with the problems presented by the demands. As a result, low efficacy individuals may have difficulty coping with high demands and experience psychological symptoms such as depression and anxiety. Therefore, we expect that self-efficacy variability has a weaker moderating effect on relationships between demands and strain among individuals with lower efficacy mean levels. An individual with a high efficacy mean level may exhibit a very different reaction toward high demands than an individual with a low efficacy mean level, but we expect that this would depend on the variability of efficacy beliefs over time. Compared with persons with stable high efficacy, persons with variable high efficacy are more likely to reevaluate their competencies based on their experiences in coping with the demands. When managing high demands, persons with variable self-efficacy may question their existing efficacy beliefs as difficulties arise, and consequently lower their state efficacy beliefs. A reduced efficacy belief in turn may elicit an appraisal of the demand as more threatening to one’s well-being, making avoidance or emotion-focused coping strategies more likely. Conversely, persons with high and stable selfefficacy beliefs are more certain about their competence and are less reactive to such external cues as difficulties in performing a more demanding task. This firm self-view enables persons with stable high efficacy to remain confident about their capabilities when demands are high. They tend to interpret the demanding situation as a challenge and an opportunity to grow, and consequently engage in problem-focused coping strategies. Problemfocused coping enables individuals to actively manage the demands and often lead to better outcomes and less psychological strain (Folkman & Moskowitz, 2004). On these bases we hypothesize that higher efficacy variability is associated with stronger relationships between stressors and psychological strain among individuals who report a high efficacy mean level. Furthermore, we hypothesize that, regardless of the level of efficacy variability, individuals with low efficacy mean levels in a life domain will report poorer well-being when demands within that domain are higher. This suggests the following hypothesis: Hypothesis 1: Role-related demands, efficacy mean level, and efficacy variability interactively predict psychological strain. Hypothesis 1a: Among individuals with a low efficacy mean level, demands are positively associated with psychological strain regardless of the level of variability in efficacy beliefs.

Hypothesis 1b: Among individuals with a high efficacy mean level, demands are more strongly associated with psychological strain when efficacy beliefs are more variable.

Overview of Studies We tested the above hypothesis in two separate studies. In Study 1, we examined how the mean level and the variability of job self-efficacy jointly influenced coping with job demands among manufacturing workers over a 3-year period. We examined two types of job demands: work load and job responsibilities. In Study 2, we sought to replicate the results of Study 1 among college students over one academic semester, by examining the three-way interaction between their academic self-efficacy mean level, its variability, and school-related hassles in predicting well-being outcomes.

Study 1 Sample and Procedure Workers from a large state-owned manufacturing company in China were recruited as part of a study of job stress and employee well-being. The third author administrated the surveys in person within the factory over a 3-year period. Consent forms were distributed to 800 employees in the organization. Of these, 496 volunteered to participate after being informed of the purpose and procedure of the study. Participants were guaranteed that their responses would be kept confidential and that they could choose not to participate at any time. A monetary incentive was provided to each participant to encourage participation. Participants completed the surveys in relatively large groups inside the organization. Respondents returned their surveys directly to the researcher. Among the 496 participants who completed the Time 1 questionnaire, 73% were men, 77% performed operational jobs, and 23% were managers or professionals. On average, they were 39 years old and had worked for 13.5 years in this company. The data were collected over a 3-year period. The three waves of data collection were spaced approximately one year apart. Six respondents did not participate at Time 2 and another 13 people did not participate in the Time 3 survey, yielding a final analysis sample of 477 respondents who completed all three surveys.

Measures We used the conventional back translation procedure (Brislin, Lonner, & Thorndike, 1973) to translate the questionnaire items from English to Chinese. Focus group interviews were conducted before the administration of the initial survey to ensure the items were understandable to the participants. Job demands. We examined two types of job demands for this study: work load and job responsibility. The responses were anchored on a 5-point scale ranging from 1 (extremely rare) to 5 (very often). Work load was assessed using a four-item scale developed by Caplan, Cobb, French, Van Harrison, and Pinneau (1975). Sample items are How often does your job require you to work very fast? and How often does your job require you to complete tasks with little time? The coefficient alpha reliabilities for this scale were .79 (Time 1), .79 (Time 2), and .84 (Time 3).



Job responsibility was measured by the scale developed by French, Caplan, and Van Harrison (1982). Sample items are My job requires me to be responsible for the future of others and My job requires me to be responsible for others’ well-being. This fouritem scale yielded alpha reliabilities of .84 (Time 1), .81 (Time 2), and .86 (Time 3). Job self-efficacy (JSE). JSE beliefs were measured using four items adapted from the Personal Efficacy Beliefs Scale (Riggs, Warka, Babasa, Betancourt, & Hooker, 1994). A sample item is I have confidence in my ability to do my job. Participants rated these items on a 5-point scale from 1 (very inaccurate) to 5 (very accurate). The alpha reliabilities for this scale were .74 (Time 1), .73 (Time 2), and .72 (Time 3). JSE mean level and JSE variability were indexed, respectively, by the grand mean and the variance of JSE scores over the three waves, yielding one JSE mean level score and one JSE variability score for each participant. Psychological strain. Anxiety and depression were measured using items adapted from the Hospital Anxiety and Depression (HAD) scale (Zigmond & Snaith, 1983). Respondents were asked to report how often they had experienced specific symptoms of anxiety and depression over the three months leading up to each survey administration. The scales range from 1 (extremely rare) to 5 (very often). The six-item anxiety scale consisted of items such as I have felt fidgety or nervous as a result of my job. The alpha reliabilities were .75 (Time 1), .84 (Time 2), and .83 (Time 3). Ten items were used to measure depression (e.g., I feel down-hearted). The alpha reliabilities for the depression scale were .86, .89, and .89 for Times 1, 2, and 3, respectively. Control variables. We did not anticipate that individuals’ reports of any of the focal measures in this study would be biased by demographic characteristics. Our hypothesis also refers to a three-way interaction rather than one or more main effects, and we could not identify variables that may serve to confound the interaction. Thus, following the recommendations of Spector and Brannick (2011), we did not control for such variables. We nevertheless conducted supplementary analyses which incorporated participants’ gender, age, and education. The inferences from these analyses were no different from those drawn from analyses that did not include these control variables in the equations.

Results We conducted measurement invariance tests using LISREL 8.80 (Jöreskog & Sörbom, 2006). These tests determined whether each measure represented the same latent construct across the three measurement points. We followed the conventional procedure for testing measurement invariance (e.g., Vandenberg & Lance, 2000) for all measures. This included testing configural invariance (i.e., equivalent factor structures over time, M1), factor loading (or metric) invariance (i.e., equivalent factor loadings, M2), and intercept invariance (i.e., changes in the means of the items are adequately captured by the changes in the means of the latent construct, M3). Factor loading invariance was also tested in M3. Results (see Table 1) supported configural and factor loading invariance for all measures, whereas intercept invariance was supported only for work load. Scholars have suggested that deviations from intercept invariance can be tolerated, as they do not necessarily affect the inferences made about the covariation of underlying constructs (Garst, Frese, & Molenaar, 2000; Pentz &

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Chou, 1994). Our measures meet conventional standards to demonstrate measurement invariance, and therefore testing relationships among these constructs over time is not complicated by changes in how participants conceptualized or calibrated the variables. Confirmatory factor analyses also showed that the factor structure of the measurements was consistent with our hypothesized factor structure at each measurement point. We tested the hypotheses using Hierarchical Linear Modeling (HLM 6; Raudenbush, Bryk, Cheong, Congdon, & du Toit, 2004). We first tested null models in HLM to determine the extent to which the outcome variables exhibited significant within-person and between-person variability. The ICC(1) values for anxiety and depression were .51 and .56, respectively, and the ICC(2) values were .75 and .79. These results indicated substantial variability both within-persons and between-persons for both health outcome measures. Thus, it was appropriate to use HLM to examine determinants of these strain variables at both Level 1 (within-person) and Level 2 (between-person). In addition, the ICC(1) for job self-efficacy (JSE) was .44, meaning that 56% of the variance in JSE was attributable to within-person change over time. The ICC(2) value for JSE was .70. Some degree of transient error is unavoidable in any longitudinal design that examines change (Schmidt, Le, & Ilies, 2003). Although transient error is random error that less likely affects relationship in a systematic way, we conducted analyses to determine whether there was substantial and meaningful variation in JSE scores that was not attributable to transient error. Using the general formula for reliability (Nunnally & Bernstein, 1994), the estimated error variance was .07. As the average within-individual variance in JSE (.23) was much larger than the expected error variance, this suggested that there was more variance across time than would be expected based on transient error. Similarly, Schmidt et al. (2003) found that a total of five percent of the variance in general self-efficacy (GSE) in their data was due to transient error. Although we measured JSE and not GSE, we have no reason to anticipate that transient error would be greater for JSE than has been found for GSE. For these reasons, we believe that the observed within-person variance in JSE was due mainly to changes in participants’ self-perceptions rather than to transient error. Table 2 presents the means, standard deviations, and correlations for the variables examined in Study 1. The correlation between JSE variability and JSE mean level, r ⫽ ⫺.22, p ⬍ .01, was consistent with the findings in the self-esteem variability literature (Okada, 2010). Work load and job responsibility were positively and significantly correlated with anxiety, but not significantly correlated with depression. Table 3 presents the HLM results testing the hypotheses. We specified different models by examining the effects of work load and job responsibility separately. For all the models, the job demand construct was entered at Level 1 to predict strain variables in order to account for the time-varying (within-person) effects of job demands. JSE mean level, JSE variability, and JSE mean level ⫻ JSE variability were entered in the Level 2 intercept-asoutcome and slope-as-outcome equations to test the three-way interaction hypotheses. In all analyses, we centered the variables by their respective grand means. Chi-square tests indicated that there was a significant amount of variance in the slopes representing the strength of the relationships between the two job demands


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Table 1 Measurement Invariance Tests for Variables Assessed in Study 1 ␹2

df

RSMEA

CFI

⌬␹2 (df)b

and item intercepts

597.13 127.13 139.94 201.99

54 39 47 55

.145 .069 .067 .075

.86 .98 .99 .96

— 470.00ⴱⴱ (15) 12.81 (8) 62.05ⴱⴱ (8)

and item intercepts

719.67 106.31 116.23 125.09

54 39 47 55

.161 .060 .056 .052

.87 .99 .99 .99

— 613.36ⴱⴱ (15) 9.92 (8) 8.86 (8)

and item intercepts

730.89 156.37 162.30 193.65

54 39 47 55

.163 .080 .072 .073

.89 .98 .98 .98

— 574.52ⴱⴱ (15) 5.93 (8) 31.35ⴱⴱ (8)

and item intercepts

1335.16 259.32 279.33 304.11

135 110 122 134

.135 .053 .052 .052

.88 .98 .98 .98

— 1075.84ⴱⴱ (25) 20.01 (12) 24.78ⴱ (12)

and item intercepts

1899.51 779.51 808.52 1498.63

405 372 391 403

.088 .048 .047 .076

.94 .98 .98 .96

— 1120.00ⴱⴱ (25) 29.01 (19) 690.11ⴱⴱ (12)


Modelsa JSE M0: Null model M1: Unconstrained model M2: Equal factor loadings M3: Equal factor loadings Work load M0: Null model M1: Unconstrained model M2: Equal factor loadings M3: Equal factor loadings Job responsibility M0: Null model M1: Unconstrained model M2: Equal factor loadings M3: Equal factor loadings Anxiety M0: Null model M1: Unconstrained model M2: Equal factor loadings M3: Equal factor loadings Depression M0: Null model M1: Unconstrained model M2: Equal factor loadings M3: Equal factor loadings

Note. n ⫽ 477. In the null model (M0), latent factors are independent and the same indicators at each time point are not permitted to covary. In the unconstrained model (M1), latent factors are allowed to covary and their indicators at each time point covary to indicate the “method” factor. No equality constraints are imposed regarding to equal factor loadings or item intercepts. This model tests factor configural invariance. In M2, factor loadings are constrained to be equal across waves. M3 further constrains the item intercepts to be equal. b⌬␹2 was calculated based on the differences in ␹2 values of Mn and Mn⫺1. ⴱ p ⬍ .05. ⴱⴱ p ⬍ .01. a

and the two health outcomes (p ⬍ .01), with one exception. The exception was for job responsibility in predicting depression. The variance of this slope approached a statistically significant level (p ⬍ .09). On the basis of this generally supportive evidence concerning variance in the slope coefficients across individuals, we proceeded to test the interaction hypothesis. Our hypothesis predicted that efficacy variability and efficacy mean level jointly moderate the relationships between demands and psychological strain. As shown in Table 3, there was a consistent pattern of significant three-way interaction effects between each job demand variable, JSE mean level, and JSE variability in predicting anxiety and depression. Following the procedure suggested by Hofmann, Griffin, and Gavin (2000), we estimated the effect sizes of the three-way interactions. These results showed that around seven percent of the variance in the job demand slope was explained by the interaction of JSE mean level ⫻ JSE variability, and the three-way interaction term explained an additional one percent of the variance in each health outcome variable. To determine the patterns of the four significant three-way interactions detailed in Table 3, we plotted them using the procedure outlined by Preacher, Curran, and Bauer (2006). As shown in Figures 1 and 2, the patterns of the four interactions share common features. We describe the figures below and identify the commonalities and differences among the interactions. Figure 1a depicts the three-way interaction of JSE variability, JSE mean level, and work load in predicting depression. Partici-

pants with a lower mean level of JSE reported higher levels of depression regardless of the level of JSE variability. Simple slope analyses indicated that higher work load had little effect among persons with higher JSE mean level and more variable JSE (simple-slope ⫽ ⫺.05, t ⫽ ⫺0.70, ns). Conversely, persons with stable high JSE were more adversely affected by higher work load (simple-slope ⫽ .20, t ⫽ 3.42, p ⬍ .01). In addition, work load was positively related to depression among individuals with variable low JSE (simple-slope ⫽ .08, t ⫽ 2.10, p ⬍ .05) and those with stable low JSE (simple-slope ⫽ .09, t ⫽ 2.84, p ⬍ .01). The slope difference test indicates no significant difference among individuals with a lower mean level of JSE (t ⫽ 0.29, ns) depending on their JSE variability. Figure 1b describes the three-way interaction in which work load, JSE mean level, and JSE variability predict anxiety. As with Figure 1a, work load was less strongly related to anxiety among persons with variable high JSE (simple-slope ⫽ .13, t ⫽ 2.01, p ⬍ .05) relative to those with stable high JSE (simple-slope ⫽ .37, t ⫽ 6.10, p ⬍ .01). Again, work load was positively associated with anxiety among individuals with low mean levels of JSE, and the stress-strain slopes were not significantly different between stable low JSE persons and variable low JSE persons (t ⫽ 0.91, ns). In contrast to the interaction in which depression was dependent, JSE mean level accounted for little variation in anxiety. Virtually the same pattern as that of the first interaction (see Figure 1a) is shown in Figure 2a, which depicts the interaction of


365

Table 2 Means, Standard Deviations, and Correlations Among Studied Variables in Study 1


Variable Between-individuals 1. Work load (WL) 2. Job responsibility (JR) 3. Anxiety (ANX) 4. Depression (DEP) 5. Gender 6. Age 7. Education 8. JSE mean level 9. JSE variability Within-individualsa 1. WLy1 2. WLy2 3. WLy3 4. JRy1 5. JRy2 6. JRy3 7. JSEy1 8. JSEy2 9. JSEy3 10. ANXy1 11. ANXy2 12. ANXy3 13. DEPy1 14. DEPy2 15. DEPy3

Mean

SD

1

2

3

4

5

6

7

8

3.34 2.15 2.08 2.05 1.27 38.68 12.15 3.94 .23

.70 .81 .57 .54 .44 6.44 2.65 .51 .32

.38 .41 .07 ⫺.11 ⫺.01 .15 .30 ⫺.03

.35 .00 ⫺.23 .16 .11 .19 ⫺.05

.56 ⫺.18 .00 .12 .09 .03

.01 ⫺.15 ⫺.08 ⫺.18 .11

.06 .01 ⫺.16 .02

⫺.09 .07 .02

.14 ⫺.10

⫺.22

3.36 3.36 3.31 2.15 2.15 2.16 3.91 3.99 3.94 2.11 2.05 2.09 2.06 2.03 2.07

.91 .80 .83 1.02 .94 .97 .75 .64 .60 .65 .74 .70 .63 .67 .63

.53 .52 .25 .23 .22 .23 .16 .17 .30 .18 .25 .05 .02 .05

.57 .22 .30 .25 .23 .16 .25 .28 .34 .31 ⫺.02 .06 .05

.24 .26 .36 .17 .11 .22 .25 .26 .40 .03 .07 .13

.49 .57 .15 .05 .16 .28 .16 .19 ⫺.04 ⫺.03 ⫺.02

.58 .14 .15 .15 .33 .25 .20 ⫺.01 .05 ⫺.02

.11 .05 .16 .27 .21 .23 ⫺.02 .02 .05

.38 .44 .10 .09 .14 ⫺.10 ⫺.09 ⫺.11

.51 .03 ⫺.01 .05 ⫺.10 ⫺.17 ⫺.10

9

10

11

12

13

14

.02 .01 .01 ⫺.13 ⫺.17 ⫺.23

.52 .51 .44 .31 .31

.53 .29 .61 .34

.26 .34 .55

.55 .58

.57

Note. n ⫽ 477; ⫾r ⫽ .09, p ⬍ .05, ⫾r ⫽ .13, p ⬍ .01; JSE refers to job self-efficacy. Gender was coded as 1 ⫽ male and 2 ⫽ female. Education was measured by the total number of years of formal education the participant had received. JSE variability was computed by calculating the variance in JSE across the three measurement periods for each individual. JSE mean level is the mean of JSE scores over time. a JSEy1–JSEy3 denote JSE scores measured across three years. Similarly, WLy1–WLy3, JRy1–JRy3 denote yearly reports on work load and job responsibility, ANXy1–ANXy3 denote yearly anxiety scores across three years, and DEPy1–DEPy3 denote yearly depression scores.

JSE variability, JSE mean level and job responsibility in predicting depression. Persons with high JSE mean levels and low JSE variability reported higher levels of depression as job responsibility increased (simple-slope ⫽ .10, t ⫽ 2.15, p ⬍ .05), whereas job responsibility was not associated with depression among persons with variable high JSE (simple-slope ⫽ ⫺.05, t ⫽ ⫺0.87, ns). Figure 2b depicts the interaction of JSE variability, JSE mean level, and job responsibility in predicting anxiety. Consistent with the findings concerning work load (Figure 1b), job responsibility was positively associated with anxiety among employees with high JSE mean levels and less variable JSE (simple-slope ⫽ .20, t ⫽ 3.68, p ⬍ .01), but it was unrelated to anxiety among employees with high and more variable JSE (simple-slope ⫽ .04, t ⫽ 0.70, ns).

Study 1 Discussion We observed four significant three-way interactions of JSE mean level, JSE variability, and each of the two job demand variables across two indexes of psychological strain. The patterns of these interactions (Figures 1 and 2) are largely the same. Supporting H1a, job demand was positively related to strain among persons with stable low JSE, and the same relationship occurred to those with variable low JSE. Contrary to H1b, job demands had the strongest positive relationship with strain among those with stable high JSE, whereas persons with variable high JSE were least affected by higher job demands. Compared with persons

with variable high JSE, persons with stable high JSE reported more symptoms of depression and anxiety when they perceived high job demands. In sum, although the interaction was consistently observed in four cases, the pattern is contrary to our hypothesis with respect to the prediction for high JSE employees. A possible explanation for the unexpected aspect of these interactions lies in a trade-off between performance strivings and well-being outcomes as individuals confront higher demands. Both the self-efficacy perspective and the cognitive appraisal model of stress suggest that individuals with high self-efficacy tend to perceive high demands as a challenge and use problem-focused coping. The combination of a firm belief in one’s competence and a devoted effort to overcome any tendency for demands to inhibit performance may lead employees with stable high efficacy to sacrifice well-being in the service of their problem-focused pursuits (Lu et al., 2011; Toker et al., 2013). In contrast, individuals with variable high self-efficacy may adjust their competency assessments based on the external feedback they receive. For example, they may perceive themselves as less competent when they make little progress on a demanding new task despite having worked harder than they typically devote to similar tasks. Their tendency to lower efficacy beliefs when tasks become more demanding may compromise their performance strivings. Yet, in this way, they are able to reserve important personal resources, such as energy. Because they occasionally acknowledge that their competency levels are not sufficient to manage the high demands, indi-


366

Table 3 HLM Results From Testing the Three-Way Interaction of Job Demands, JSE Mean Level, and JSE Variability in Study 1 Psychological strain Anxiety a


Models

Work load ⫻ JSE mean level ⫻ JSE variability Intercept (␥00) Work load (␥10, WL) JSE mean level (␥01, JSEM) JSE variability (␥02, JSEV) WL ⫻ JSEM (␥11) WL ⫻ JSEV (␥12) JSEM ⫻ JSEV (␥03) WL ⫻ JSEM ⫻ JSEV (␥13) Job responsibility ⫻ JSE mean level ⫻ JSE variability Intercept (␥00) Job responsibility (␥10, JR) JSE mean level (␥01, JSEM) JSE variability (␥02, JSEV) JR ⫻ JSEM (␥11) JR ⫻ JSEV (␥03) JSEM ⫻ JSEV (␥12) JR ⫻ JSEM ⫻ JSEV (␥13)

Depression

Coef.

SE

t ratio

Coef.

SE

t ratio

2.06 .23 ⫺.02 .03 .04 ⫺.16 ⫺.14 ⫺.43

.02 .02 .05 .11 .05 .10 .18 .15

86.32ⴱⴱ 10.02ⴱⴱ ⫺.35 .27 .83 ⫺1.57 ⫺.78 ⫺2.91ⴱⴱ

2.05 .08 ⫺.23 .13 ⫺.01 ⫺.19 .06 ⫺.40

.02 .02 .05 .11 .05 .10 .19 .16

83.54ⴱⴱ 3.71ⴱⴱ ⫺4.50ⴱⴱ 1.11 ⫺.26 ⫺1.85 .33 ⫺2.56ⴱ

2.08 .13 .04 .02 ⫺.02 ⫺.07 ⫺.31 ⫺.33

.02 .02 .05 .11 .04 .09 .18 .13

84.08ⴱⴱ 6.19ⴱⴱ .81 .15 ⫺.45 ⫺.76 ⫺1.73 ⫺2.47ⴱ

2.06 .05 ⫺.20 .12 ⫺.05 ⫺.11 ⫺.03 ⫺.25

.02 .02 .05 .12 .04 .09 .21 .13

84.17ⴱⴱ 2.53ⴱ ⫺3.94ⴱⴱ 1.02 ⫺1.29 ⫺1.25 ⫺.16 ⫺1.95ⴱ

Note. n ⫽ 1431 (Level 1), n ⫽ 477 (Level 2). JSE ⫽ job self-efficacy. ⴱ p ⬍ .05. ⴱⴱ p ⬍ .01.

viduals with variable high self-efficacy may also seek instrumental or emotional support from others (e.g., coworkers, spouse, supervisor), which helps alleviate their distress (Beehr, King, & King, 1990; Viswesvaran, Sanchez, & Fisher, 1999). For these reasons, those with high and more variable self-efficacy may less likely experience strain when confronted with heavy role-related demands. Although consistent with research demonstrating a trade-off between performance strivings and health (Lu et al., 2011; Toker et al., 2013), the above interpretation is nevertheless post hoc. It is also possible that the findings from testing a three-way interaction effect may be idiosyncratic. Therefore, we conducted Study 2 to determine whether we could replicate the findings obtained from Study 1 with a different sample, and to conduct supplementary analyses that support our conjecture.

Study 2

directly with the demands, regardless of the variability of their self-efficacy beliefs. Persons with low self-efficacy may also perceive a demanding task as more formidable than it is (Bandura, 1982), leading to ineffective coping tactics such as avoidance. For this reason, we expected variability to have little influence among those with low mean levels of self-efficacy. If the findings from Study 1 extend to Study 2, the relationship between demands and strain would be weaker among those with high and more variable efficacy. Considering our broader model and this newer perspective that explains differences in the coping processes between stable high efficacy persons and variable high efficacy persons, we form the following hypothesis: Hypothesis 2: Role-related demands, efficacy mean level, and efficacy variability interactively predict psychological strain. Hypothesis 2a: Among individuals with a low efficacy mean level, demands are positively associated with psychological strain regardless the levels of efficacy variability.

Overview and Hypotheses We conducted Study 2 with two objectives in mind: (a) to verify our prediction that the level of variability in efficacy beliefs moderates the relationship between demands and psychological strain only among individuals with high efficacy mean levels, and (b) to test an alternative prediction for the moderating effect of efficacy variability on the demand-strain relationship among individuals with a high efficacy mean level. Thus, we retested the original H1a (H2a in Study 2) and revised H1b (H2b in Study 2) to determine whether the unexpected aspect of the interaction findings replicates in an independent and very different sample. As with our initial hypothesis, we predicted that individuals with low self-efficacy mean levels are likely to perceive high demands as a threat and they are less likely to persist in efforts to cope

Hypothesis 2b: Among individuals with a high efficacy mean level, demands are less strongly associated with psychological strain when efficacy beliefs are more variable.

Sample and Procedure The participants were full-time students enrolled in a public university in the United States. Participants were recruited at the beginning of the Fall semester. Informed consent was obtained from a total of 171 students who volunteered to participate in the study. Participants completed four online surveys, which were e-mailed to them on Wednesday morning every other week. They were asked to submit the survey by 6:00 p.m. the following day (Thursday). To protect their privacy, participants were instructed


367

a 2.3 2.2 Low JSE Mean Level Low JSE Variability Low JSE Mean Level High JSE Variability

2.0

High JSE Mean Level Low JSE Variability

1.9

High JSE Mean Level High JSE Variability

1.8 1.7 Low Work Load

b

High Work Load

2.4 2.3

Anxiety


Depression

2.1

2.2

Low JSE Mean Level Low JSE Variability

2.1

Low JSE Mean Level High JSE Variability

2.0


1.9


1.8 1.7 Lo w Work Load

H i g h W o r k Lo a d

Figure 1. (a) Three-way interaction of JSE variability by JSE mean level by work load predicting depression. (b) Three-way interaction of JSE variability by JSE mean level by work load predicting anxiety.

to create their own unique usernames that were unidentifiable to the researchers. These usernames were then used to match their responses across the four surveys. Of the 171 participants, 141 completed all four surveys, 25 completed three surveys, and the remaining five completed one or two surveys. Only those who provided complete data for all four surveys were included in the analyses. In our final sample, the average age was 21.8 (SD ⫽ 1.1) years and 51.1% of respondents were female. Regarding ethnicity, 70.9% of the analysis sample reported being Caucasian, 21.3% were Asian, and the remaining participants reported being African American, Hispanic, and “other.”

Measures School-related hassles. We developed a nine-item scale by adapting items from measures of daily life hassles (Kanner, Coyne, Schaefer, & Lazarus, 1981; Newcomb, Huba, & Bentler, 1981). These items assessed students’ perceived demands related to school and schoolwork (e.g., too much schoolwork to do, missing

a deadline for a school assignment; see the full scale in the Appendix). The response scale ranged from 1 (not at all) to 3 (often). The coefficient alpha reliabilities for this scale were .78 (Time 1), .78 (Time 2), .78 (Time 3), and .82 (Time 4). Academic self-efficacy (ASE). We measured ASE using a nine-item scale developed by Pintrich and De Groot (1990). Sample items were Compared with my classmates I expect to do well and I am sure I can do an excellent job on my school assignments. Participants rated these items on a five-point scale from 1 (Not at all) to 5 (very much). The coefficient alpha reliabilities for this scale were .95 for each of the four measurements. Similar to Study 1, ASE mean level and ASE variability were indexed by the grand mean and the variance of ASE scores over the four observation points. Psychological strain. Respondents were asked to report how often they had experienced symptoms of depression and poor sleep quality during the past few days. The scale for both measures ranged from 1 (extremely rare) to 5 (very often). Depression was


368

a

2.4

2.2

Low JSE Mean Level Low JSE Variability

2.1


2.0


1.9


1.8 1.7 Low Job Responsibility

b

High Job Responsibility

2.4 2.3 Low JSE Mean Level Low JSE Variability

2.2

Anxiety


Depression

2.3


2.1


2.0


1.9 1.8 Low Job Responsibility

High J ob Re sp ons ibilit y

Figure 2. (a) Three-way interaction of JSE variability by JSE mean level by job responsibility predicting depression. (b) Three-way interaction of JSE variability by JSE mean level by job responsibility predicting anxiety.

measured using the same instrument as in Study 1 (Zigmond & Snaith, 1983). The alpha reliabilities were .91 (Time 1), .92 (Time 2), .93 (Time 3), and .94 (Time 4). Four items were used to measure the quality of sleep (Jenkins, Stanton, Niemcryk, & Rose, 1988). This scale consisted of items such as I have trouble staying asleep, and I wake up several times during the night. The alpha reliabilities were .71, .90, .90, and .87 for Times 1, 2, 3 and 4, respectively. Control variables. As with Study 1, we did not control for the demographic variables such as gender and grade point average (GPA) in our model. There was little variation in age and education in this sample. The results also remained the same after controlling for these variables.

Results Tests of measurement invariance were conducted using the same procedures we described for Study 1. As presented in Table 4, the measures demonstrated configural and metric measurement invariance. Except for the measure of poor sleep quality, the other three measures also demonstrated intercept invariance. We also

conducted confirmatory factor analyses that supported the expected factor structure of the measurements. Table 5 presents the means, standard deviations, and correlations for the variables in Study 2. ASE variability was negatively correlated with ASE mean level, r ⫽ ⫺.14, p ⬍ .10. The sign and size of this correlation was similar to that for JSE variability and JSE mean level in Study 1. ASE mean level was negatively associated with depression, r ⫽ ⫺.42, p ⬍ .01 and poor sleep quality, r ⫽ ⫺.17, p ⬍ .05. ASE mean level also had a strong negative relationship with school-related hassles, r ⫽ ⫺.67, p ⬍ .01. It is noteworthy that aggregating the observations across four waves increased the correlation, compared to an average correlation of ⫺.50 between ASE and school-related hassles at each observation point. The ICC(1) values for depression and sleep quality were .68 and .61, respectively, and the ICC(2) values were .90 and .86. Thus there was substantial variability both within-persons and betweenpersons for the strain measures. Moreover, the ICC(1) and ICC(2) values for academic self-efficacy (ASE) were .74 and .92. We observed a smaller portion of within-person variation in ASE than


369

Table 4 Measurement Invariance Tests for Variables Assessed in Study 2 ␹2

df

RSMEA

CFI

⌬␹2 (df)b

and item intercepts

788.96 71.60 77.90 96.99

104 74 83 95

.217 .000 .000 .017

.883 1.000 1.000 .999

— 717.36ⴱⴱ (30) 6.30 (9) 19.09 (12)

and item intercepts

607.00 79.44 94.22 107.90

104 74 83 95

.186 .023 .031 .032

.515 .993 .980 .977

— 527.56ⴱⴱ (30) 14.78 (9) 13.68 (12)

and item intercepts

687.21 86.34 96.44 108.59

104 74 83 95

.200 .035 .034 .032

.856 .995 .993 .992

— 600.87ⴱⴱ (30) 10.10 (9) 12.15 (12)

and item intercepts

709.71 116.94 129.90 178.24

104 74 83 95

.204 .064 .070 .079

.873 .989 .982 .974

— 592.77ⴱⴱ (30) 12.96 (9) 48.34ⴱⴱ (12)


Modelsa ASE M0: Null model M1: Unconstrained model M2: Equal factor loadings M3: Equal factor loadings School-related hassle M0: Null model M1: Unconstrained model M2: Equal factor loadings M3: Equal factor loadings Depression M0: Null model M1: Unconstrained model M2: Equal factor loadings M3: Equal factor loadings Poor quality of sleep M0: Null model M1: Unconstrained model M2: Equal factor loadings M3: Equal factor loadings

Note. n ⫽ 141. a For measures that contain more than four items (i.e., ASE, School-related hassle, and depression), we randomly selected four items to serve as indicators of the latent constructs. b ⌬␹2 is calculated based on the differences in ␹2 values of Mn and Mn⫺1. ⴱⴱ p ⬍ .01.

we had observed for JSE in Study 1. This may be attributable to the shorter time interval between the observations in Study 2. Table 6 presents the HLM results testing the hypotheses. Chisquare tests indicated a significant amount of variance across individuals in the relationships between school-related hassles and the two health outcomes (p ⬍ .01). Thus, we tested the models in the same way as we did in Study 1. As shown in Table 6, a significant three-way interaction between school-related hassles, ASE mean level, and ASE variability was found for both health outcomes. The interaction term of ASE mean level ⫻ ASE variability explained 41% (in prediction of depression) and 43% (in prediction of poor sleep quality) of the variance in the stress-strain relationships. This three-way interaction explained an additional one percent of the overall variance in poor sleep quality, and two percent of the variance in depression. The patterns of these three-way interactions are shown in Figure 3. Figure 3a depicts the three-way interaction of ASE variability, ASE mean level, and school-hassles in predicting depression. Consistent with the patterns observed in Study 1 (see Figure 1a and Figure 2a), school-related hassles had a stronger positive relationship with depression among persons with stable high ASE than those with variable high ASE. Simple slope analyses indicated that higher school-related hassles had little relationship with depression among persons with high and variable ASE (simple-slope ⫽ .22, t ⫽ 1.18, ns). Conversely, persons with stable high ASE were adversely affected by higher school-related hassles (simpleslope ⫽ .80, t ⫽ 4.21, p ⬍ .01). A distinction between these patterns and those observed in Study 1 is that school-related hassles was not related to depression among persons with stable low ASE (simple-slope ⫽ .12, t ⫽ 0.60, ns). Consistent with our prediction, however, the differences in the demand-strain relation-

ship between persons with variable low ASE and those with stable low ASE were not statistically significant (t ⫽ 1.54, ns). Figure 3b describes this same three-way interaction predicting poor sleep quality. As with depression, school-related hassles was more strongly associated with poor sleep quality among persons with stable high ASE (simple-slope ⫽ .89, t ⫽ 4.20, p ⬍ .01), compared with those with variable high ASE (simpleslope ⫽ ⫺.23, t ⫽ ⫺0.99, ns). Whereas school hassles was positively related to poor sleep quality, this relationship did not differ significantly between individuals with stable low ASE and those with variable ASE (t ⫽ 1.00, ns). This replicates the pattern of the two interactions in predicting anxiety in Study 1 (see Figure 1b and Figure 2b). To summarize, H2a was supported in that school-related hassles was related to psychological strain in a similar way among persons with a low ASE mean level, regardless of the level of ASE variability. Supporting H2b, and consistent with the findings in Study 1, school-related hassles was less strongly related to strain among individuals with variable high ASE, compared to those with stable high ASE.

Study 2 Discussion Findings based on a sample of college students replicated the three-way interactions observed in Study 1 and supported the revised prediction (H2b) such that individuals with high and yet stable ASE were more vulnerable to psychological strain when confronting higher school-related hassles than those with variable high ASE. It is possible that individuals with high and variable ASE tend to adjust their academic competency beliefs based on the difficulties they have encountered in school. This may have com-


370

Table 5 Means, Standard Deviations, and Correlations Among Studied Variables in Study 2


Variable Between-individuals 1. School-related hassles (HS) 2. Depressions (DEP) 3. Poor sleep quality (PSQ) 4. Gender 5. GPA 6. ASE mean level 7. ASE variability Within-individualsa 1. HS1 2. HS2 3. HS3 4. HS4 5. ASE1 6. ASE2 7. ASE3 8. ASE4 9. DEP1 10. DEP2 11. DEP3 12. DEP4 13. PSQ1 14. PSQ2 15. PSQ3 16. PSQ4

Mean

SD

1

2

3

4

5

6

1.75 1.94 2.02 1.51 3.53 3.78 .17

.28 .70 .81 .50 .27 .73 .18

.44 .26 .18 ⫺.24 ⫺.67 .20

.59 .11 ⫺.15 ⫺.42 .12

.07 ⫺.07 ⫺.17 .12

.04 ⫺.11 .05

.39 ⫺.14

⫺.14

1.79 .32 1.77 .34 1.74 .32 1.70 .36 3.79 .84 3.81 .83 3.80 .79 3.69 .81 2.00 .78 1.98 .78 1.87 .74 1.89 .83 1.81 .75 2.11 1.01 1.98 .97 2.09 .96

.58 .54 .48 ⫺.47 ⫺.49 ⫺.49 ⫺.44 .31 .27 .24 .23 .15 .18 .18 .09

.66 .60 ⫺.47 ⫺.54 ⫺.52 ⫺.51 .35 .37 .35 .32 .17 .26 .35 .22

.72 ⫺.44 ⫺.49 ⫺.56 ⫺.54 .34 .28 .30 .27 .11 .18 .16 .15

⫺.47 ⫺.49 ⫺.57 ⫺.59 .25 .28 .27 .32 ⫺.03 .09 .10 .18

.80 .69 .69 ⫺.34 ⫺.31 ⫺.19 ⫺.24 ⫺.10 ⫺.16 ⫺.11 ⫺.08

.76 .75 ⫺.37 ⫺.34 ⫺.25 ⫺.26 ⫺.08 ⫺.16 ⫺.08 ⫺.01

7

8

9

10

11

12

13

14

15

.80 ⫺.40 ⫺.31 ⫺.32 ⫺.30 ⫺.05 ⫺.16 ⫺.18 ⫺.02

⫺.41 ⫺.36 ⫺.27 ⫺.42 ⫺.05 ⫺.20 ⫺.12 ⫺.10

.74 .62 .63 .36 .52 .36 .40

.70 .70 .29 .59 .42 .46

.67 .26 .44 .48 .44

.26 .45 .39 .50

.54 .54 .53

.65 .68

.66

Note. n ⫽ 141; ⫾r ⫽ .17, p ⬍ .05, ⫾r ⫽ .21, p ⬍ .01; GPA ⫽ grade point average and ASE ⫽ academic self-efficacy. Gender was coded as 1 ⫽ male and 2 ⫽ female. a The numbers 1 to 4 denote the variable score at each observation point.

individuals with variable self-efficacy tend to lower their competency beliefs when demands are high. To do so, we selected subgroups of individuals, one with self-efficacy variability scores one standard deviation (SD) above the sample mean and the other one SD below the mean. After centering the individual reports of efficacy and demands to each person’s mean to remove the between-person effect, we examined the within-person correlation between self-efficacy and job demands in each subgroup. We found that the within-person correlation between JSE and demand was positive when JSE variability was low (r ⫽ .10, p ⬍ .10, for job responsibility; r ⫽ .11, p ⬍ .10, for work load). When JSE

promised their academic performance while preserving important personal resources and, in turn, better health. Conversely, individuals with stable high ASE may have persisted in efforts to maintain high performance levels despite more school-related hassles because they believe they could resolve any difficulties by investing more effort. They may ultimately have succeeded, and yet this comes at a cost to their well-being. This is a plausible explanation for their higher levels of reported psychological strain, as was also found for individuals with stable high JSE in Study 1. Two supplementary analyses supported our new theorizing. First, as suggested by a reviewer, we tested our reasoning that

Table 6 HLM Results From Testing the Three-Way Interaction of School-Related Hassles, ASE Mean Level, and ASE Variability in Study 2 Psychological strain Depression Variable Intercept (␥00) School-related hassles (␥10, HS) ASE mean level (␥01, ASEM) ASE variability (␥02, ASEV) HS ⫻ ASEM (␥11) HS ⫻ ASEV (␥12) ASEM ⫻ ASEV (␥03) HS ⫻ ASEM ⫻ ASEV (␥13)

Coef. 1.97 .39 ⫺.34 ⫺.14 .17 ⫺.41 ⫺.80 ⫺1.67

SE .06 .09 .08 .32 .14 .53 .45 .65

Poor Sleep Quality t ratio ⴱⴱ

34.09 4.32ⴱⴱ ⫺4.38ⴱⴱ ⫺.44 1.17 ⫺.77 ⫺1.78 ⫺2.56ⴱ

Coef.

SE

t ratio

2.04 .38 ⫺.08 .10 ⫺.06 ⫺1.30 ⫺.38 ⫺2.50

.07 .11 .10 .37 .13 .66 .59 .69

28.92ⴱⴱ 3.45ⴱⴱ ⫺.81 .28 ⫺.49 ⫺1.96ⴱ ⫺.65 ⫺3.64ⴱⴱ

Note. n ⫽ 564 (Level 1), n ⫽ 141 (Level 2). ASE ⫽ academic self-efficacy. ⴱ p ⬍ .05. ⴱⴱ p ⬍ .01.


a

371

2.5 2.4 2.3

Low ASE Mean Level Low ASE Variability

Depression

2.2 2.1

Low ASE Mean Level High ASE Variability

2.0 1.9

High ASE Mean Level Low ASE Variability

1.8

High ASE Mean Level High ASE Variability

1.6 1.5 1.4 Low School-Related Hassles

b

High School-Related Hassles

2.5 2.4 2.3

Poor Quality of Sleep

Low ASE Mean Level Low ASE Variability

2.2

Low ASE Mean Level High ASE Variability

2.1 2.0

High ASE Mean Level Low ASE Variability

1.9 1.8

High ASE Mean Level High ASE Variability

1.7 1.6 1.5 Low School-Related Hassles

High School-Related Hassles

Figure 3. (a) Three-way interaction of ASE variability by ASE mean level by school-related hassles predicting depression. (b) Three-way interaction of ASE variability by ASE mean level by school-related hassles predicting poor sleep quality.

variability was high, however, that correlation became negative (r ⫽ ⫺.14, p ⬍ .05, for job responsibility; r ⫽ ⫺.11, p ⬍ .10, for work load). In Study 2, the within-person correlation between ASE and school hassles was ⫺.03 (ns) for those with low ASE variability, but it was ⫺.21 (p ⬍ .05) for those with high ASE variability. These results are thus consistent with the rationale behind our revised prediction. Second, we conducted a supplementary HLM analysis with participants’ self-reports of academic performance serving as the outcome variable. We measured academic performance across the four observation points using a 6-item scale from Zimmerman, Bandura, and Martinez-Pons (1992). Participants reported how well they were doing in various school-related activities (e.g., finishing class assignments on time). The reliabilities were .83, .84, .86, and .87, respectively across the four observations. ASE mean level, ASE variability, and their interaction term were entered at Level 2 to predict academic performance at Level 1. We found a significant two-way interaction between ASE mean level and ASE variability in predicting academic performance (␥ ⫽ ⫺.81, p ⬍ .01). As shown in Figure 4, ASE variability was negatively associated with aca-

demic performance among those with a high ASE mean level (simple-slope ⫽ ⫺.78, t ⫽ 2.63, p ⬍ .01), but this relationship was not significant among individuals with a low ASE mean level (simple-slope ⫽ .40, t ⫽ 1.58, ns). Although the three-way interaction involving stressors was not significant, our plotting 4.4 4.2

Academic performance


1.7

4.0 3.8

Low ASE level

3.6

High ASE level

3.4 3.2 3.0 Low Variability in ASE

High Variability in ASE

Figure 4. Two-way interaction between ASE variability and ASE mean level predicting academic performance.


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of the interaction suggested that individuals with stable high ASE maintained a similarly high level of performance when school hassles increased (simple-slope ⫽ ⫺.14, t ⫽ 0.57, ns). In contrast, those with a variable high efficacy reported lower academic performance when school hassles became higher (simple-slope ⫽ ⫺.34, t ⫽ 1.72, p ⬍ .10).


General Discussion Across two studies, we found that self-efficacy variability, self-efficacy mean level, and demands interactively predicted psychological strain (see Figures 1 to 3). Although we initially predicted that more variable efficacy beliefs reflect a less certain self-view and thus have negative implications for stress coping, our findings from Study 1 called this into question. Based on the unexpected finding that demands had a weaker relationship with strain among individuals with high efficacy mean levels provided the variability in their efficacy beliefs was also higher, we formulated an alternative hypothesis and tested it in Study 2. We discuss below the implications of intrapersonal variability in self-efficacy for research on stress coping and well-being. Previous studies of the simpler hypothesis of self-efficacy mean level as a moderator of the relationships between demands and psychological well-being have demonstrated a range of different findings (Sonnentag & Frese, 2003). Our findings show that demands are positively related to strain among those with stable high self-efficacy but not for those with variable high self-efficacy. Thus, considering the degree of temporal variability in efficacy beliefs provides an explanation for the equivocal findings concerning the moderating effect of selfefficacy level in the stressor-strain relationships as reported in the literature. We found that variability in role-specific efficacy may influence coping with demands in a way that is different from self-esteem variability. Whereas variability in self-esteem is found detrimental to health among individuals with a high mean level of self-esteem, efficacy variability buffers high efficacy individuals from experiencing more strain as demands increase. We speculate that variability in domain-specific self-concept such as role-specific efficacy reflects individuals’ tendency to consider role-specific events when assessing their competencies. Variation in efficacy may often reflect actual changes in the role demands and precipitate search for external resources. Seeking for external resources may aid in preventing severe depletion of resources (e.g., burnout) that might result from continued active engagement with the demands based on stable efficacy beliefs. In contrast, self-esteem is based on one’s general self-regard across roles and domains and should be less susceptible to temporal events in a specific life domain (Chen et al., 2001). Variability in self-esteem may reflect a tendency to overgeneralize the implications of one specific adverse event in ways that hurts one’s global self-concept (Butler et al., 1994). Because individuals can elevate or downplay the relevance of a particular domain to how they view themselves (Crocker & Wolfe, 2001), a variable selfesteem may also indicate an inability to leverage one’s strengths in other domains to maintain an overall positive self-image. Thus, whereas temporal variation in role-specific efficacy beliefs is in-

strumental for psychological health when demands are high, selfesteem variability may largely reflect a disposition to overly react to negative daily events in a way that reduces one’s overall self-regard and well-being. Our results further suggest that there is an underlying dynamism in the stress-coping process that is not adequately captured by other existing theories of stress-coping (see Ganster & Rosen, 2013, for a review). As shown from results of the supplementary analyses, school-related hassles was negatively related to academic performance among individuals with variable high ASE, whereas that relationship was not significant among those with stable high ASE. This is consistent with a process in which persons with stable high efficacy maintain a high level of performance when encountering high demands. We argue that individuals with stable high self-efficacy strive for a consistently high performance owing to their firm beliefs about their competencies to manage the demands. Their subsequent high levels of performance, in turn, confirm the high efficacy beliefs they firmly maintain (Bandura, 1982). Yet, such determination to meet the demands may come at the cost of lower psychological well-being when demands become greater, as such persistence likely depletes personal resources needed for better well-being. This finding to some degree is in line with the literature on workaholism, which has shown that individuals who are addicted to work and constantly go the extra mile to maintain high performance more readily experience health problems (see a review by Clark, Michel, Zhdanova, Pui, & Baltes, 2014). Future research might profitably examine the attributions individuals with different levels of efficacy and efficacy variability make in response to success and failure feedback as they strive to cope with changing demands. For example, employees with stable high self-efficacy may perceive higher demands as more controllable than do employees with variable high self-efficacy. Our findings also shed light into the diverse coping strategies individuals may use to cope with demands. Individuals with variable high self-efficacy may be more prone to seeking support and comfort from their colleagues, friends, or family members when they have difficulties or fail in coping with role-related demands. Because they enhance their self-view when demands ease, it would seem they also do not tend to dwell on their past failures or unpleasant experiences. Despite an overall high level of psychological symptoms, individuals with low efficacy mean levels were found to be less strongly affected by higher demands, as reflected in a relatively flat stressor-strain slope. These individuals may develop different coping strategies than those with high efficacy mean levels. For example, they may be less personally involved with their work or affirm a generally positive self-image by refocusing their attention on other personal strengths or social relationships (Brown, Collins, & Schmidt, 1988; Crocker & Wolfe, 2001). Future studies may help to inform these speculations. Consistent with prior research (Kernis & Goldman, 2003), we found that a higher mean level of self-efficacy was negatively associated with symptoms of depression. However, a high efficacy mean level did not appear to mitigate employees’ experiencing anxiety; there were in fact a marginally significant positive relationships between JSE mean level and anxiety, r ⫽ .088, p ⫽ .056 (Study 1). Anxiety is a more common psycho-


logical response that is not necessarily associated with the experience of despair concerning self-views as is depression. It is frequently elicited by threat appraisals associated with situational demands. Because individuals with high efficacy tend to pursue tasks and roles that are more challenging, one might expect they will more often experience anxiety that accompanies higher demands than persons with low efficacy mean level. Supporting this reasoning, a supplementary analysis found that the positive relationship between JSE mean level and anxiety in the Study 1 data disappeared when controlling for job demands.


Limitations and Future Research Directions Previous studies have demonstrated the significant role of short-term fluctuation in self-evaluations, specifically selfesteem, in psychological functioning and well-being. Our study extends this literature by examining domain-specific aspects of self-concept (i.e., job and academic self-efficacy) over relatively long-term periods. Both short-term and long-term approaches to studying self-concept variability have distinct advantages. Whereas the longitudinal design of our working adult sample, which spanned three years, was useful to assess changes in job demands and efficacy beliefs, a period of as long as three years may not be needed to bracket such changes in future research. However, scholars who may seek to replicate our findings should note that we observed less within-person variance in Study 2, which spanned an 8-week interval. Therefore, future studies that seek to assess within-person change in demands and efficacy in a work setting will need to bracket a fairly significant interval to capture meaningful changes in the demands. We do not expect that short-term fluctuation in demands, such as those that have characterized most research on self-esteem variability, will produce the processes that we have postulated and described. Given the complexity of three-way interaction findings, it is useful to attempt to replicate them, especially when certain aspects of the interaction pattern were unexpected. We obtained a separate sample to test the same interaction and obtained essentially the same results. In Study 2 we examined efficacy and demands in a different domain by examining American college students’ academic self-efficacy and school-related hassles, whereas the initial study concerned manufacturing workers in China and their job self-efficacy. Although it is tempting to infer that the interaction is robust considering how similar the patterns are between the two studies, one must be cautious in this respect because differences between the contexts and measures might conceivably have interacted in such a way as to produce similar findings. We therefore encourage attempts to replicate our findings in different organizational and cultural settings. Although our results across the two studies can be explained with reference to a trade-off between striving for high performance and maintaining good health, future research may use other indices of performance striving, because not all striving is reflected in role performance outcomes. For example, the amount of time on rolerelated activities could be one such index. Because persons with stable high efficacy tend not to adjust their efficacy beliefs in relation to demands, they might also be less inclined to change their work styles to respond more effectively to the challenges, such as by seeking help from coworkers. Future studies may seek

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to assess the cognitive appraisals and coping strategies to verify our proposed mechanisms.

Conclusions Our findings suggest that variability in self-efficacy beliefs has important implications for coping with demands. Research may now need to consider self-efficacy variables more broadly in terms of both a typical efficacy level and the degree of variability in that level. Examining the interaction between efficacy level and its variability may be necessary to understand how self-efficacy contributes to well-being as individuals confront stressors. Considering its theoretical linkages to psychological processes that are seen as central to self-regulation, examining self-concept variability in conjunction with both the corresponding self-concept level and stress exposures may further our understanding of why some people cope with adversity better than others.

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School-Related Hassles Items Instructions: The following is a list of unpleasant/annoying things that might have happened in your daily life. Please indicate how frequently these events have happened to you during the last few days. 1 ⫽ Not at all 2 ⫽ Occasionally 3 ⫽ Often 1. Too much schoolwork to do. 2. Missed a deadline for a school assignment. 3. Had difficulty keeping up with the new class.

4. 5. 6. 7. 8. 9.

Accidently lost my work. Got a poor grade on a school assignment. Got a poor grade on a test. Performed poorly in class. Wasted a lot of time. Did not achieve my study plans. Received March 8, 2014 Revision received November 10, 2014 Accepted November 16, 2014 䡲

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