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Psychology and Psychotherapy: Theory, Research and Practice (2012)  C 2012 The British Psychological Society

The British Psychological Society www.wileyonlinelibrary.com

Relationship between attributional style, perceived control, self-esteem, and depressive mood in a nonclinical sample: A structural equation-modelling approach Julie Ledrich and Kamel Gana∗ University of Bordeaux, France Background. The aim of this study was to examine the intricate relationship between some personality traits (i.e., attributional style, perceived control over consequences, self-esteem), and depressive mood in a nonclinical sample (N = 334). Method. Structural equation modelling was used to estimate five competing models: two vulnerability models describing the effects of personality traits on depressive mood, one scar model describing the effects of depression on personality traits, a mixed model describing the effects of attributional style and perceived control over consequences on depressive mood, which in turn affects self-esteem, and a reciprocal model which is a non-recursive version of the mixed model that specifies bidirectional effects between depressive mood and self-esteem. Results. The best-fitting model was the mixed model. Moreover, we observed a significant negative effect of depression on self-esteem, but no effect in the opposite direction. Conclusions. These findings provide supporting arguments against the continuum model of the relationship between self-esteem and depression, and lend substantial support to the scar model, which claims that depressive mood damages and erodes self-esteem. In addition, the ‘depressogenic’ nature of the pessimistic attributional style, and the ‘antidepressant’ nature of perceived control over consequences plead in favour of the vulnerability model.

Practitioner Points • Pessimistic explanatory style and perceived control over consequences were found to be depressogenic, acting as vulnerability factors for depression. • Depression was found to erode and damage self-esteem (i.e., ‘scarring’ effect).

∗ Correspondence should be addressed to Dr Kamel Gana, Department of Psychology, University of Bordeaux, 33800 Bordeaux, France (e-mail: [email protected]). DOI:10.1111/j.2044-8341.2012.02067.x

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The relationship between personality and depression (or depressive mood)1 is not a new topic (see Hippocrates) but until now, it has remained a main focus of research in scientific psychology (Zuckerman, 2011). Empirical literature dealing with this relationship comprises two sources of difficulties at least. The first one is related to the wide variety of models proposed to explain the nature of the relationship between these constructs. According to Klein, Kotov, and Bufferd (2011; see also Kotov, Gamez, Schmidt, & Watson, 2010), seven major models have been proposed: the common cause model, the continuum model, the precursor model, the vulnerability, the pathoplasticity model, the concomitants model, the scar. As noted by Clark (2005) these models are not mutually exclusive and each may be partially correct or simply incomplete. The second source of difficulties is related to the large diversity of personality models, each with different levels of abstraction (i.e., from very specific to very broad). The aim of the present study was to draw on some of these models to investigate ‘causal’ relationship between some specific personality traits, that is, self-esteem, attributional style, perceived control over consequences, and depressive mood by using structural equation modelling (SEM) approach. To our knowledge, no attempt was made to delineate the structure of the relations among these constructs.

Relationship between self-esteem, attributional style, and depression Since the question raised by Lewinsohn, Steinmetz, Larson, & Franklin (1981): ‘Depression-related cognitions: Antecedent or consequence?’, the causal relationship between depression and self-esteem, and also attributional (explanatory) style, remains ambiguous (Orth, Robins, & Roberts, 2008; Wichers, Geschwind, van Os, & Peeters, 2010). Self-esteem refers to global self-views (i.e., thoughts, feelings, and evaluations about the self) (Swann, Chang-Schneider, & Larsen McClarty, 2007); it can be classified either as a trait or as a state. Global trait self-esteem is relatively stable over time, while global state self-esteem fluctuates according to life contingencies. Attributional style is a cognitive personality characteristic that reflects the habitual manner in which people explain negative and positive events that befall them (Abramson, Seligman, & Teasdale, 1978; Furnham, 2009; Metalsky, Abramson, Seligman, Semmel, & Peterson, 1982; Peterson et al., 1982; Seligman, Abramson, Semmel, & Von Baeyer, 1979). According to the common cause model, personality and depression are features of the same underlying factor. Exploratory and confirmatory factor analyses conducted by Hankin, Lakdawalla, Carter, Abela, & Adams (2007) in their two studies revealed and confirmed that self-esteem, neuroticism, and depression may be highly overlapping constructs. They are thought to be part of a common latent construct (i.e., a latent mood factor). Furthermore, in the behavioural genetic analyses conducted by Neiss, Stevenson, Legrand, Iacono, & Sedikides (2009), depression, self-esteem, and neuroticism emerged as facets of a common temperamental (heritable) core. The continuum model of the relationship between self-esteem and depression argues that both self-esteem and depression are derived from the broader construct of negative affectivity (Orth et al., 2008). Their causal status is ambiguous, however, because their measures overlap. Indeed, in each of their three studies, Watson, Suls, and Haig (2002) found that self-esteem was strongly and negatively correlated with negative affectivity. 1 Throughout

this article, depression and depressive mood are used interchangeably as a dimensional, not as categorical construct (Flett, Vredenburg, & Krames, 1997; Hankin, Fraley, Lahey, & Waldman, 2005). Thus, we are interested in individual differences in depressive mood rather than a clinical category such as major depressive disorder.

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These findings clarify those obtained some years ago by Joiner and Rudd (1996), revealing that attributional style and depression are independent factors, while depression and selfesteem may be two facets of the same underlying dimension. The vulnerability model argues that self-esteem and attributional style, like some other personality traits such as neuroticism (Kendler, Gatz, Gardner, & Pedersen, 2006) constitute risk factors exerting a causal effect in the onset and maintenance of depressive states. Swann et al. (2007) plead in favour of the adaptive role of self-esteem. They affirm that self-esteem plays a vital role in guiding behaviours and organizing reality, which is why we are attached to preserving and protecting it. They defend the causal status of self-esteem, postulating that people with negative self-esteem think and behave in a manner that deteriorates their quality of life. Indeed, numerous studies have found support for this model (Lewinson, Hoberman, & Rosenbaum, 1988; Brown, Andrews, Bifulco, & Veiel, 1990). More recently, Trzesniewski et al. (2006) followed a group of adolescents for 11 years into adulthood (age 26). Even after controlling for numerous relevant predictors, they found that self-esteem was a significant predictor of major depressive disorder. In their recent studies based on longitudinal data, Orth et al. (2008) found that low self-esteem contributes to depression, but depression does not erode selfesteem. However, some studies have failed to prove that self-esteem predicts depressive mood (Butler, Hokanson, & Flynn, 1994), while others have shown that self-esteem lability (stability vs. unstability) may be more a reliable predictor of depression than global self-esteem (Auerbach, Abela, Ho, McWhinnie, & Czaikowska, 2010; Franck & De Raedt, 2007). According to the cognitive diathesis—stress theory, a pessimistic attributional style is characterized by the tendency to view negative events as caused by factors that are internal, stable, and global, so it constitutes a risk factor for the development of depression. To explain the psychological process underlying the effect of explanatory style, the authors suggest that a pessimistic style leads to helplessness and hopelessness, which in turn lead to depression (Abramson, Metalsky, & Alloy, 1989). Thus, the pessimistic attributional style has been qualified as the ‘depressogenic attributional style’ (Tennen, Herzberger, & Nelson, 1987). Since then, many prospective studies have demonstrated that this negative attributional style has a causal role in both the onset and maintenance of depression (Alloy et al., 2006; Lakdawalla & Hankin, 2008; Mongrain & Blackburn, 2005; Sanju´an & Magallares, 2009). The scar model, in contrast to the vulnerability model, argues that low self-esteem as well as pessimistic attributional style are a consequence rather than a cause of depression. Thus, depression erodes self-esteem and shapes one’s mode of thinking. This model has rarely been evaluated empirically. There are indeed few prospective studies investigating the effect of depression on self-esteem (Ormel, Oldehinkel, & Vollebergh, 2004; Orth et al., 2008; Shahar & Davidson, 2003) and attributional style. Ball, McGuffin, and Farmer (2008) conducted one of the rare studies dealing with the effect of depression on attributional style. The results of their study revealed that attributional style was mainly a result of current mood. Their results support those previously reported by Johnson and Miller (1990), whose longitudinal study showed that attributional style at Time 2 was predicted by depression at Time 1, and not the opposite. Concerning self-esteem, the scar model has been partially supported by the results obtained by Shahar and Davison (2003), and Coyne and colleagues (Coyne & Calarco, 1995; Coyne, Gallo, Klinkman, & Calarco, 1998), but by the results of either Orth et al. (2008) or Ormel et al. (2004).

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As underlined by Orth et al. (2008), so far, there is no clear evidence of the validity of either the vulnerability model or the scar model. Thus, the potential reciprocal relations between self-esteem (or attributional style) and depression have also been examined. In two recent prospective studies investigating reciprocal relations between self-esteem and depression from young adulthood to old age in two different datasets (American participants vs. German participants), Orth, Robins, Trzesniewski, Maes, and Schmitt (2009) found that in both data samples, low self-esteem predicted subsequent levels of depression after controlling for prior depressive symptoms. In contrast, depression did not predict subsequent levels of self-esteem when prior self-esteem was controlled. Thus, these findings failed to support the reciprocal effects hypothesis, and provide supporting evidence for the vulnerability model. Regarding attributional style, the results of a prospective study conducted by Lau and Eley (2010) showed that attributional style predicted depression, and attributional style also co-occurred and followed depression. Of course, all of these models need further empirical evidence (Kotov et al., 2010). Although it seems relevant, the idea that potential reciprocal effects between self-esteem (or pessimistic attributional style) and depression does not allow one determine and clarify their causal status. In addition, these models focus mainly on direct effects among the constructs, hence ignoring or underestimating the presence of potential meditating and/or moderated variables. Moreover, the majority of the studies discussed above evaluated the relationship between depression and either self-esteem or attributional style separately. Thus, it seems more relevant to examine their joint effects in depressive moods than their separate effects.

Perceived control over consequences: A neglected personality construct Perceived control seems to play an important role in psychological adjustment to negative events (Thompson, 2002; Weiner, 1985). The basic premise behind this statement is that events perceived as controllable are less likely to lead to psychological distress; conversely, events perceived as uncontrollable are more likely to lead to psychological distress. However, Skinner (1996) distinguished various ‘aspects’ of control (i.e., objective control, perceived control, experiences of control), ‘agents’ (i.e., self vs. others; interne vs. externe), ‘means’ (i.e., actions vs. cognitions), ‘ends’ (i.e., causes vs. consequences), and ‘time frames’ (i.e., past, present, future) of control. We focused in the present research on perceived control, and particularly on perceived control over the consequences of negative events. Perceived control is often used interchangeably with concepts of self-efficacy, mastery, and locus of control, despite their conceptual difference (Keeton, Perry-Jenkins, & Sayer, 2008; Skinner, 1995). Moreover, perceived control is considered to be an important cognitive component of the personality construct of hardiness (Eschleman, Bowling, & Alarcon, 2010). Wardle et al. (2004) found that depression and life satisfaction were related to perceived control among students from Central Eastern and Western Europe. And more recently, Auerbach, Tsai, and Abela (2010) found that adolescents with lower levels of perceived control reported higher levels of depressive symptoms. Recall here that the controllability dimension (cornerstone of the learned helplessness theory; Maier & Seligman, 1976) was dropped from the hopelessness theory of depression because it was shown to be correlated to the internality and stability dimensions, and can therefore be inferred from them (see Au, Watkins, Hattie, & Alexander, 2009; Brown & Siegel, 1988; Sanju´an & Magallares, 2009). However, controllability has been emphasized in other attribution theories (Weiner, 1985). Thus, despite its importance, the perception of control over consequences (i.e.,

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Figure 1. M1: Vulnerability model of the relationship between personality traits and depression (to keep the figure simple, the covariates and the indicators of the latent variables are not shown); D= disturbance; ∗ standardized coefficient significant at p < .05.

beliefs about one’s ability to control the consequences of a negative situation) has been neglected in hopelessness and psychological distress models and theories. There is some empirical evidence that controllability over consequences plays a more important role in psychological distress than controllability over causes (Frazier, Mortensen, & Steward, 2005; Jensen, Turner, & Romano, 2007). It is the reason why this study focuses more on control over consequences than over causes.

The current study The aim of the present study was to further explore the relationship between explanatory style, perceived control over consequences, self-esteem, and depression, using SEM. Thus, five competing models were specified and evaluated. Figure 1 depicts the first model (M1), which represents the hypothesis that pessimistic attributional style and perceived control over consequences are intercorrelated, exogenous variables that do not directly affect depressive mood but have a mediated impact via the subject’s selfesteem. The mediating effect of self-esteem between causal attributions and between control expectancies and depression was assumed by Abramson et al. (1978), and found by Pillow, West, and Reich (1991) and Lightsey, Burke, Ervin, Henderson, & Yee (2006). We also expected that a pessimistic attributional style would have a negative effect on self-esteem, while perceived control over consequences would have a positive effect. And we expected higher levels of self-esteem to predict lower levels of depressive mood. These hypotheses were based on the vulnerability model of depression. Figure 2 depicts another version of the vulnerability model (M1b) which represents the hypothesis that self-esteem has an indirect effect on depression via attributional style and perceived control over consequences. We also expected self-esteem to have a positive effect on perceived control, and a negative effect on attributional style. A higher level of perceived control should predict a lower level of depressive mood. And a more negative attributional style would predict greater depression. Figure 3 depicts a model (M2) that hypothesizes that depression exerts an influence on both perceived control over consequences and attributional style, which in turn, impact self-esteem. Because depression is characterized by cognitive biases regarding the world, the future, and the self (i.e., the cognitive triad; Beck, 1987), this state can even after remission, result in biases like more internal, stable, and global attributions, or in the perception of uncontrollability (Shahar & Davidson, 2003). We also expected that, contrary to selfesteem, depression would have a negative effect on perceived control and a positive

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Figure 2. M1b: Vulnerability model of the relationship between personality traits and depression. (To keep the figure simple, the covariates and the indicators of the latent variables.) ∗ Stndardized coefficient significant at p < .05).

Figure 3. M2: Scar model of the relationship between personality traits and depression. (To keep the figure simple, the covariates and the indicators of the latent variables are not shown.) ∗ Standardize coefficient significant at p < .05.

effect on attributional style. Greater levels of perceived control should predict higher selfesteem. And a more negative attributional style should predict lower self-esteem. These hypotheses were based on the scar model. The fourth model (M3) depicted in Figure 4 hypothesized that a depressive mood would mediate the effects of pessimistic attribution style and perceived control over consequences on self-esteem. These hypotheses follow from a mix of the vulnerability model (represented by the effects of attributional style and perceived control on depression) and the scar model (represented by the effect of depression on self-esteem). Also, we expected a pessimistic attributional style to have a positive effect on depression, while perceived control over consequences should have a negative effect. And a stronger depressive mood should predict lower self-esteem. The fifth model (M3-Reciprocity), a non-recursive version of M3, describes a reciprocal effect between depression and self-esteem.

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Figure 4. M3: Mixed model of the relationship between personality traits and depression. (To keep the figure simple, the covariates and the indicators of the latent variables are not shown.) ∗ Standardize coefficient significant at p < .05.

Method Participants and procedure The sample was made up of 334 adults (267 women and 67 men). They were 26.90 years of age on average (SD = 9.72), and 82% were undergraduate students. Most were single (74.6%), married (or living maritally) (23.4%), while the others were divorced, separated, or widowed (1.8%). Participants were informed of the voluntary and anonymous character of the study.

Measures Attributional style The Extended Attributional Style Questionnaire (EASQ, Metalsky, Halberstadt, & Abramson, 1987) consists of 12 negative hypothetical events and asks respondents to provide a cause for each one in an open-ended format. The causes given are then rated on a 7-point scale in terms of their internality, stability, and globality. Scores for each of these three attributional dimensions were calculated here by averaging participants’ responses for that dimension across all 12 items. Thus, high scores are indicative of a tendency to attribute negative events to more internal, stable, and global factors. In addition, we created a composite attributional score by averaging the participant’s responses on each of the three attributional dimensions. Thus, higher scores are indicative of a pessimistic explanatory style. Cronbach’s alphas were .47 for internality, .71 for stability, and .75 for globality. While the last two coefficients are satisfactory, the internality coefficient is weaker. This pattern of results is comparable to that found in past research (Kinderman & Bentall, 1996; Reivich, 1995).

Perceived control over the consequences of negative events We also included in the EASQ an item (in the same 7-point Likert format) to assess perceived controllability over consequences in each of the 12 situations. Because of the lack of an available measure that assesses the locus of control over consequences, this item was created for the purposes of this study (To what extent do you think you have control over the consequences of this situation?) (Cronbach alpha, ␣ = .73).

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Self-esteem Self-esteem was assessed using the 10-item Rosenberg Self-Esteem Scale (RSE; Rosenberg, 1965), which is the most commonly used and well-validated measure of global self-esteem (present study Cronbach’s alpha = .87).

Depressive mood The BDI-II is a 21-item standardized measure of depressive mood (Beck, Steer, & Brown, 1996). For each item, participants select one of four statements that best describes how they felt during the past 2 weeks. Responses were summed to yield a total score (present study Cronbach alpha = .88).

Covariates Gender and age were introduced as control variables because of their known associations with depression and self-esteem (e.g., Huang 2010; Syzmanowicz and Furnham, 2011; Van de Velde, Bracke, and Levecque, 2010).

Statistical analyses SEM was applied to evaluate the hypothesized models. Recall here that SEM is a flexible tool for modelling and clarifying ‘causal’ relationships among a set of variables (Bollen, 1989). SEM is a confirmatory approach; it is a way of testing a model, which allows to (1) include measurement models, thereby allowing for a better estimation of error structures, for one of the benefits of using SEM is indeed that it enables a researcher to account for error measurement in variable, (2) test the congruence between the hypothesized model (or alternative competing models) and the sample data (i.e., covariances of observed data), and (3) examine and understood the direct, indirect, and overall effects of one variable on another in the tested model. Regarding the second capability, different indexes have been developed to assess the goodness of fit of structural models. However, the model-fit criteria commonly recommended are chi-square, the comparative fit index (CFI), the standardized root mean square residual (SRMR), the root mean square error of approximation (RMSEA), and its 90% confidence interval (90% CI). The values of CFI range from 0 (no fit) to 1 (perfect fit), with a value greater than .95 being taken as the recommended cutoff point for acceptance of the specified model (Hu & Bentler, 1999). An RMSEA value below .06 is needed to support the plausibility of the theoretical model (Hu & Bentler, 1999). Browne and Cudeck (1993) suggested that a value of about .08 or less indicates a reasonable error of approximation. An SRMR value of .08 or lower indicates a good model fit (Hu & Bentler, 1999). Regarding the indirect effects, the bootstrap procedure for checking significance is recommended. Indeed, bootstrap estimation is a widely used re-sampling method that provides empirical information about the variability and behaviour of parameter estimates (e.g., their stability and accuracy; MacKinnon, Lockwood, Hoffmann, West, & Sheets, 2002). To evaluate the specified models, the self-esteem, depression, and perceived control scales were each randomly aggregated into parcels (see Little, Cunningham, Shahar, & Widaman, 2002). We then followed the two-stage modelling procedure recommended by Anderson and Gerbing (1988). This procedure consists of establishing the validity of the measurement model before evaluating the structural model.

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Table 1. Means and standard deviations (and F-test for gender differences) of scores on measures used in this study Women n = 267

Men n = 67

Measure

M (SD)

M (SD)

F-test

p-value

EASQ-total score EASQ-Internality EASQ-Stability EASQ-Globality Control over consequences Self-esteem Depressive mood Age

12.57 (1.84) 4.47 (.72) 4.00 (.79) 4.09 (1.03) 3.67 (.89) 30.25 (5.27) 11.75 (8.31) 23.12 (9.10)

12.93 (1.43) 4.43 (.61) 4.26 (.64) 4.21 (.91) 3.86 (.37) 31.69 (4.84) 9.12 (7.16) 28.28 (12.71)

1.98 .159 6.03 .769 2.56 4.16 5.65 13.96

.160 .691 .015 .381 .110 .042 .018 .000

Note. EASQ, Extended Attributional Style Questionnaire.

All analyses were conducted with AMOS software (Arbuckle, 2009), using the maximum likelihood method of estimation.

Results Descriptive statistics and gender differences The means and standard deviations of the scales and subscales in this study appear in Table 1. Because the prevalence of depression is known to be significantly greater in women than in men, differences between the genders were examined using a one-way ANOVA. The results showed significant gender differences in the EASQ stability subscale scores, with men reporting higher ratings (M = 4.26, SD = .64) than women (M = 4.00, SD = .79), in the self-esteem scores, with men reporting higher ratings (M = 31.69, SD = 4.84) than women (M = 30.25, SD = 5.27), and in the depressive mood scores, with women reporting significantly higher ratings (M = 11.75, SD = 8.31) than men (M = 9.12, SD = 7.16). In our sample, the men (M = 28.28, SD = 12.71) were older than the women (M = 23.12, SD = 9.10).

Correlational analysis A correlation analysis was performed using participant demographics (age and gender) and scores from each measure used in this study. The results (summarized in Table 2) show that self-esteem and depressive mood were moderately and negatively correlated (r = −. 62). Thus, the proportion of common variation in the two variables reached 38% (R2 = .384). A pessimistic explanatory style and a depressive mood were weakly and positively correlated (r = .301, p = .000). These findings do not support the continuum model. In addition, self-esteem was negatively and significantly related to the tendency to attribute negative events to more internal (r = −.242, p = .000), stable (r = −.138, p = .012), or global (r = −.182, p = .001) factors. Also, self-esteem was significantly and positively related to perceived control over consequences (r = .178, p = .001). Depressive mood was also significantly and positively correlated with the tendency to attribute negative events to more internal (r = .148, p = .007), stable (r = .193,

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Table 2. Intercorrelations between measures used in this study (n = 334) Measure 1. EASQ-Total score 2. EASQ-Internality 3. EASQ-Stability 4. EASQ-Globality 5. Control over consequences 6. Depression 7. Self-esteem 8. Gender 9. Age

1

2

3

4

5

1.00 .52∗∗ .73∗∗ .83∗∗ −.03

1.00 .08 .15∗∗ .08

1.00 .46∗∗ −.09

1.00 −.04

1.00

.30∗∗ −.26∗∗ −.08 .04

.15∗∗ −.24∗∗ .02 −.12∗

.19∗∗ −.14∗ −.13∗ .13∗

.28∗∗ −.18∗∗ −.05 .05

−.20 .18∗∗ −.09 .06

6

7

8

9

1.00 −.62∗∗ .13∗ −.04

1.00 −.11∗ .15∗

1.00 −.20∗∗

1.00

Note. EASQ, Extended Attributional Style Questionnaire; gender: male = 1, female = 2.∗ p ⬍ .05; ∗∗ p ⬍ .01. Table 3. Goodness-of-fit summary of the competing models Model Measurement M1-Vulnerability M1b-Vulnerability M2-Scar M3-Mix M3-Reciprocity

␹ 2 (df )

p

CFI

SRMR

RMSEA (90% CI)

107.49 (47) 180.18 (69) 222.91 (71) 230.71 (71) 163.18 (69) 163.18 (68)

.000 .000 .000 .000 .000 .000

.965 .938 .916 .912 .950 .950

.053 .072 .065 .067 .062 .063

.060 (.047, .078) .070 (.057, .082) .080 (.068, .092) .082 (.070, .094) .061 (.052, .077) .062 (.052, .078)

Note. See figures for a description of various models. df = degrees of freedom; CFI, comparative fit index; SRMR, standardized root mean squared residual; RMSEA, root mean square error of approximation; CI, confidence interval; M3-Reciprocity is a non-recursive model with reciprocal effects between selfesteem and depression.

p = .000), or global (r = .276, p = .000) factors. Also, depressive mood was negatively and significantly correlated with perceived control over consequences (r = −.198, p = .000). Age was significantly and positively correlated with self-esteem (r = .148, p = .007) but not with depressive mood (r = −.037, p = .506).

Measurement model A four-factor measurement model including self-esteem (three indicators), depression (four indicators), attributional style (three indicators), and perceived control of consequences (two indicators) was estimated. The goodness-of-fit results of this model indicated a poor fit (␹ 2 (48) = 156.06, p = .000, CFI = .938, and RMSEA = .082). It was clear that some modifications were needed to improve the model’s fit. Thus, this model was modified to include the error covariance between two parcels of depression (concerning measurement error in SEM, see Rubio and Gillespie, 1995). As shown in Table 3, the goodness-of-fit results of the modified model indicated substantial improvement in the fit. Indeed, we can see a very large and significant drop in the overall chi-square value. Thus, it was appropriate to test some more restrictive structural models.

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In addition, the standardized parameter estimates revealed that the correlation between the self-esteem and depression latent variables was strong (r = −.738). Thus, the discriminant validity of the two latent variables was assessed by comparing their shared variance (squared correlation) against their average variance extracted (AVE) (Fornell & Larker, 1981). If the squared correlation between two latent variables (constructs) is less than either of their individual AVE’s, this means that each latent variable has more internal (extracted) variance than the variance shared between them. The results revealed that the AVEs for self-esteem (.717) and for depression (.585) were higher than their shared variance (.545). Thus, the discriminant validity of the two constructs was confirmed. The CI of the correlation between the two latent variables obtained through the bootstrap procedure (−.802, −.647, p = .002) did not include the value of 1, giving further evidence of their discriminant validity (Torkzadeh, Koufteros, & Pflughoeft, 2003).

Structural models The initial hypothetical models were re-specified, taking into account the modification introduced into the measurement model. Age and gender were introduced as covariates. As shown in Table 3, M3 (depicting a mix between vulnerability and scar models) fit the data better than either M1 or M2, based on CFI (.950), SRMR (.062), and RMSEA (.061). Parameter estimates from this model revealed that a pessimistic style had a significant positive effect on depression (␤ = .36, p = .000), while perceived control over consequences had a significant negative effect on depression (␤ = −.22, p = .005). Depression had a negative significant effect on self-esteem (␤ = −.74, p = .000). The proportion of explained variance (R2 ) for each endogenous variable in the model was 19% for depressive mood (R2 = .19, 95% CI = .073, .316, p = .004) and 54% for self-esteem (R2 = .54, 95% CI = .413, .640, p = .002). Although the reciprocal model (M3-Reciprocity) yielded comparable fit statistics to those obtained with the M3, as shown in Table 3, self-esteem was not a significant predictor of depression (␤ = −.10, p = .639).

Test for the significance of the indirect effects: Bootstrap procedure The indirect effect estimates with bias-corrected bootstrap CIs were obtained using an analytic bootstrap approach (a bootstrap sample of 1000 was used) for M3. Recall here that an indirect estimate is considered significant when zero does not fall inside the CI (Preacher & Hayes, 2008). The results revealed that the indirect effect of a pessimistic explanatory style on self-esteem was negative and statistically significant b = −.520 (95% CI = −.839, −.189), p = .003, while the indirect effect of perceived control over consequences was positive and statistically significant b = .06 (95% CI = .016, .103), p = .006.

Discussion This study attempted to investigate “causal” relationship between personality traits (i.e., self-esteem, attributional style, and perceived control over consequences) and depressive mood. This study resumes the controversy about the nature of this relationship, more specifically its sequential order of occurrence: Does personality predispose a person to

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depression (vulnerability model) or does depression damage personality (scar model)? Of course, these two models are not mutually exclusive. Although longitudinal data seems to be the most appropriate for unravelling the cause–effect relationship, not all authors agree that they can resolve the causality problem, especially when a study is purely observational (Twisk, 2003). Thus, we modestly addressed this question by using SEM. Let us now summarize our main results. First, the correlation between a pessimistic explanatory style and a depressive mood was low (accounting for 10% of the variance), and the correlation between self-esteem and depressive mood was moderate (accounting for 38% of the variance), contradicting the continuum model. This model states that the one reason why the relationship between depression and certain personality traits such as self-esteem or a pessimistic explanatory style, should be stronger is that the two constructs are very similar (Watson et al., 2002). Furthermore, a strong relationship between self-esteem and depressive mood would mean that depression is a largely stable feature. Furthermore, the discriminant validity between self-esteem and depression was established, meaning that each of them has more internal (extracted) variance than the variance shared between them. In addition, our findings revealed that age was significantly and positively correlated with self-esteem but not with depressive mood. Thus, we can conclude that they are related but different psychological constructs. Second, the SEM results revealed that neither the vulnerability model nor the scar model was a good approximation of reality. However, the model that mixed the vulnerability and scar hypotheses was the one capable of adequately reproducing the observed variance–covariance matrix. Thus, a pessimistic explanatory style has a direct and positive effect on depressive mood. Studies have shown that as attributions for negative situations become more internal, stable, and global, depression increases (Ball et al., 2008; Sweeney, Anderson, & Bailey, 1986). Perceived control over consequences has a direct and negative effect on depression here. The more individuals perceived themselves as having control over the consequences of negative situations, the less they developed a depressive mood. This finding was not surprising because control is a key human drive, and control strategies are calibrated to share the task of coping with life’s contingencies at each period of the life span (Heckhausen & Schulz, 1995). Indeed, Frazier, Berman, & Steward (2002) suggested that control over consequences (in contrast to control over causes) is adaptive, by way of its positive effects on the outcome of the situation or on emotional damage. It allows individuals to restore a sense of control, and thus, counteract the generalization of potential helplessness symptoms. Thus, these findings are in line with previous research defending the vulnerability model, which holds that personality acts as a diathesis for depression (Sanju´an & Magallares, 2009). In particular, the ‘depressogenic’ nature of the pessimistic attributional style was confirmed here (Tennen et al., 1987), while perceived control over consequences seems to protect against depressive affects (Frazier et al., 2002). However, an effect of depression on selfesteem contradicts the vulnerability model. Indeed, the more a depressive mood, the lower self-esteem; but the inverse was not true. Also, these findings provide an argument against the continuum model of the relationship between self-esteem and depression, and lend substantial support to the scar model, which claims that depressive mood damages and erodes self-esteem (Shahar & Davison, 2003). Ultimately, to explore the relationship between personality and depression, it seems advisable not to consider personality as a monolithic construct that goes in only one orientation. Indeed, while a pessimistic attributional style seems to be ‘depressogenic’, and perceived control over consequences seems to have an ‘antidepressant’ effect,

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self-esteem seems to be subjected to the negative effects of depressive mood. As noted by Orth et al. (2008), scarring effects can follow both intraindividual and interindividual pathways. As an intraindividual pathway, Orth et al. (2008) mentioned the fact that depression alters self-relevant information process. Indeed, it was shown that depression is associated with negatively biased (selective) thinking about the self, resulting in the formation of more negative self-relevant thoughts and thereby creating a “vicious circle” (Wichers, Geschwind, Os, & Peeters, 2010). As interpersonal pathways, Orth et al. (2008) mentioned the fact that depressive mood may undermine important sources of self-esteem such as close relationships or social networks. Indeed, according to the sociometer theory of self-esteem (Leary, 2005), social inclusion and sense of belonging determine self-esteem. Thus, self-esteem depends on one’s interpersonal (i.e., relational) worth. For example, Srivastava and Beer (2005) found in their longitudinal study that, as predicted by sociometer theory, being liked by others enhances selfesteem. However, and as noted by Steger and Kashdan (2009), depressed people’s social information-processing biases (Levens & Gotlib, 2010; Williams et al., 2000) make them less likely to perceive cues of belonging and inclusion in social interactions. Thus, depression generates a social dysfunction (Joiner, 2002). Steger and Kashdan (2009) found, for example, that depressed people reported a higher number of negative social interactions and a lower sense of belonging in social interactions. This finding is in line with those showing that people with high levels of depressive symptoms not only report fewer intimate relationships, but also receive more negative rejecting attitudes and fewer positive caring responses from others (Gotlib, 1992). In addition, the metaanalysis conducted by Joiner and Katz (1999) revealed that depressive symptoms are contagious. It seems that depressed people induce negative affect in others, which in turn leads to social rejection. These results are in line with Coyne’s interactional theory of depression (Coyne, 1976), which postulates that the interpersonal behaviours of depressed people elicit rejection from others. Similarly, Hammen’s (1991, 2003) stress generation model argues that depressed individuals generate interpersonal stress that translates into increased risk of depression. Thus, the scarring effects of depression on self-esteem seem very plausible. Although our findings provide some insight into the intricate covariations frequently observed between certain personality traits and depression, a few methodological limitations of this study must be mentioned. First, SEM does not allow one to make any confident causal inferences about relationships among variables. Second, readers should keep in mind that a model that fits the data well cannot be taken as the truth, but only as a simple approximation of reality. Finally, one should be aware of the methodological limitations of data collected on self-report questionnaires (e.g., monomethod bias). Thus, indirect measures within the implicit cognition paradigm (e.g., implicit self-esteem, implicit depressive cognitions) might open up promising avenues for research into the causal relationship between personality and depression (Franck, De Raedt, De Houwer, 2008; Steinberg, Karpinski, & Alloy, 2007).

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