testing alternative models of the relation between self

TESTING ALTERNATIVE MODELS OF THE RELATION BETWEEN SELF-ESTEEM AND DEPRESSION: A LONGITUDINAL APPROACH

A Cumulative Dissertation submitted to the Department of Psychology of the University of Basel in partial fulfillment of the requirements for the degree of Doctor of Philosophy by

JULIA FRIEDERIKE ELISABETH SOWISLO

Basel, Switzerland 2013

Approved by the Department of Psychology at the request of

Prof. Dr. Alexander Grob (Chair)

Prof. Dr. Ulrich Orth (Referee)

Prof. Dr. Jens Gaab (Co-Referee)

Basel, December 19, 2012

Prof. Dr. Alexander Grob (Dean)

Full of gratitude, I dedicate this thesis to my beloved father Dr. Wolfgang H. Sowislo

SELF-ESTEEM AND DEPRESSION

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ACKNOWLEDGEMENTS

First and foremost I would like to thank my supervisor, Professor Ulrich Orth, for his guidance and support throughout my PhD. It was a great opportunity to learn from him and I am sure that he substantially shaped the way I think and work scientifically.

I would also like to express special gratitude to Professor Jens Gaab for acting as coreferee and Professor Alexander Grob for serving on the dissertation committee. My heartfelt thanks go to Laurenz Meier and my very dear friend Thorana Grether for the pleasant scientific collaboration as well as for their helpful comments and insights when proofreading my dissertation. I am also grateful to my colleagues Farah Kuster and Ruth Yasemin Erol for all the tears of laughter we cried in Switzerland, in Turkey, in the United States, in Italy, and in Germany. I thank Sebastian Czyzykowski and his IT-team for the excellent technical support. My sincere thanks go to all the study participants for generously providing information on their self-esteem and depression, which I was lucky to use as anonymized data in my dissertation.

Moreover, I would like to express gratitude to my friends Mirella Walker and Janina Hoffmann for making the Department of Psychology a much sweeter and better place to be. Exceptional thanks go to Alexander Wiegmann without whom I would never have made it through my studies of Psychology at the University of Göttingen. It is still reassuring to know that no scientific project I will ever carry out will be half as good as one of his. I furthermore thank my friends Felicitas Sedlmair, Lisa Horvath, and Ania Mauruschat for many interesting discussions and hearty laughter, face-to-face as well as on the phone, as well as their loyalty and friendship. Also, I thank my friend Bartosz Adamcio for always being so beautiful both


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inside and outside and Henning Krampe for the same, as well as for serving as my secret psychological idol. My deep gratitude goes to my friend Katarina Trajkovic for the many wild adventures in Geneva and for exploring all those Swiss towns and spas with me.

I would like to express my deepest gratitude to my very dear parents Wolfgang Sowislo and Heidrun Heneka-Sowislo. I have only been so privileged because they have always supported me in everything I did, and loved me with all my deficiencies and failures. By the same token, I thank my younger sister Johanna Sowislo. Sitting at my desk in front of the computer, I felt proud and safe, knowing that at the same time she was doing much more important and responsible work at the clinics.

Last, but certainly not least, I would like to express my very special gratitude to two persons: I have been more than lucky to be friends with Gabriele Bartolomaeus. I thank her for her continuous inspiration, for her loving encouragement to keep on searching, for new and intelligent perspectives on many issues, for the funniest stories, the coziest places, and for being such a wonderful person in general. Finally, I am indebted to Julia Schwanke, whose love, appreciation, and patience accompanied me fondly and safely through the years of my dissertation. Merci viu mau!


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TABLE OF CONTENTS ACKNOWLEDGEMENTS .....................................................................................................IV TABLE OF CONTENTS .........................................................................................................VI ABSTRACT .......................................................................................................................... VIII Introduction ................................................................................................................................ 1 Theoretical Framework............................................................................................................... 3 Models of Self-Esteem and Depression.................................................................................. 3 Locating the Research Within the Field of Personality Psychology ...................................... 5 Debate 1: Uniqueness of self-esteem.................................................................................. 5 Debate 2: Self-esteem’s implicative meaning. ................................................................... 9 Research Questions and Contribution of the Research ........................................................ 12 Method ...................................................................................................................................... 13 Research Design ................................................................................................................... 13 Longitudinal design: Cross-lagged regression analysis. .................................................. 13 Meta-analysis. ................................................................................................................... 14 Samples and Statistical Procedures ...................................................................................... 15 Research questions 1-3: Article 1. .................................................................................... 15 Research questions 4-7: Article 2. .................................................................................... 16 Results ...................................................................................................................................... 17 Article 1 ................................................................................................................................ 17 Research question 1. ......................................................................................................... 17 Research question 2. ......................................................................................................... 17


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Research question 3. ......................................................................................................... 18 Article 2 ................................................................................................................................ 18 Research question 4. ......................................................................................................... 18 Research question 5. ......................................................................................................... 19 Research question 7. ......................................................................................................... 19 General Discussion ................................................................................................................... 19 Implications .......................................................................................................................... 20 General implications. ........................................................................................................ 20 Implications for self-esteem research and personality psychology. ................................. 23 Implications for clinical psychology and depression interventions.................................. 25 Limitations ............................................................................................................................ 26 Future Research .................................................................................................................... 28 Conclusion ............................................................................................................................ 30 REFERENCES ......................................................................................................................... 31 APPENDIX .............................................................................................................................. 48


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ABSTRACT Self-esteem and depression are strongly related, but the nature of this relation is still open to debate. The aim of this cumulative dissertation is to clarify the exact nature of the prospective relation between self-esteem characteristics (i.e., level, instability, and contingency) and depression by testing alternative, theoretically plausible models. In Article 1 (Sowislo & Orth, 2013) we investigated the prospective reciprocal relations of self-esteem level with depression and anxiety by means of a meta-analysis of longitudinal studies. We found consistent support for the vulnerability model of low self-esteem and depression (i.e., low self-esteem contributing to subsequent depression) but only weak support for the scar model of low selfesteem and depression (i.e., depression eroding self-esteem). Moderator analysis of the vulnerability effect of low self-esteem suggested its stability across different sample and study characteristics. Article 2 (Sowislo, Orth, & Meier, 2013) builds upon the results from Article 1 by testing whether other characteristics of self-esteem, namely self-esteem instability and contingency, have a vulnerability effect on depression over and above the vulnerability effect of self-esteem level. Two primary studies testing overarching models showed that only level of self-esteem predicted subsequent depression and that the characteristics of self-esteem did not interact in the prospective prediction of depression. Altogether, our findings suggest that low self-esteem, but not unstable and contingent self-esteem, acts as a stable and consistent vulnerability factor for depression. The dissertation contributes to resolving debates in personality psychology, refining theories of self-esteem and of depression and provides translational implications for clinical psychological interventions.

“Your self-esteem is a notch below Kafka.” (Woody Allen, 1979)


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Introduction Sex, sweets, alcohol, seeing a best friend, or receiving a paycheck: human beings tend to place greater value on boosts to their self-esteem than on all those pleasant rewards (Bushman, Moeller, & Crocker, 2011). The importance of self-esteem in everyday life is mirrored by an extensive study of the construct in psychological science: a literature search for the keyword “self-esteem” on PsycINFO in June 2013, when this dissertation framework was written, yielded more than 39,501 indexed articles (cf. Donnellan, Trzesniewski, & Robins, 2011). This research on self-esteem spans different fields of psychology, such as clinical psychology, organizational psychology, personality psychology, and social psychology (Watson, Suls, & Haig, 2002). Despite these extensive research efforts, there are still several unresolved questions in the area of self-esteem. For instance, there is ongoing disagreement about whether self-esteem should be conceptualized as a global evaluation of the self or as an evaluation in specific selfrelevant domains (e.g., Swann & Bosson, 2010), whether self-esteem has an impact on reallife outcomes (e.g., Baumeister, Campbell, Krueger, & Vohs, 2003), and whether these possible outcomes are exclusively positive or might also be negative in nature (e.g., Baumeister, Smart, & Boden, 1996; for further details on these issues see Sowislo & Orth, 2013). More specifically, in the domain of psychological adjustment, the relation between self-esteem and depression has not ultimately been established (e.g., Baumeister et al., 2003). Thus, the present research tests alternative models of self-esteem and depression in order to shed light on two relevant areas of debate: First, two different models of low self-esteem and depression dominate the relevant literature. While the vulnerability model (e.g., A. T. Beck, 1967) assumes that low self-esteem can contribute to depression, the scar model (e.g., Zeiss & Lewinsohn, 1988) assumes that the experience of a depressive episode can erode self-esteem. Although recently a growing body of studies has provided evidence in favor of the vulnerability model, this is not yet unequivocal: Some studies failed to replicate this evidence


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(e.g., Butler, Hokanson, & Flynn, 1994) and moreover some studies even found effects in favor of the scar model (e.g., Shahar & Davidson, 2003). Furthermore, little is known about the specific conditions that moderate possible effects of self-esteem and depression on each other (Sowislo & Orth, 2013). Second, theoretical considerations (e.g., J. E. Roberts & Monroe, 1994) and empirical findings (e.g., J. E. Roberts, Kassel, & Gotlib, 1995; Sargent, Crocker, & Luhtanen, 2006) suggest that when investigating the relation of self-esteem and depression, not only selfesteem level, but also self-esteem instability (i.e., the degree of variability in self-esteem across short periods; Kernis, 2006) and self-esteem contingency (i.e., the degree to which selfesteem fluctuates in response to self-relevant events; Crocker & Wolfe, 2001) should be considered. However, it has not yet been clarified whether these other characteristics of selfesteem, in addition to its level, explain incremental or even greater variance in subsequent depression. More precisely, previous relevant studies have not considered all three self-esteem characteristics simultaneously, have suffered from low power, and have yielded inconsistent results (Sowislo et al., 2013). To sum up, there are two global foci of this dissertation: In Article 1, we tested the vulnerability and scar models of low self-esteem and depression by means of a meta-analysis of longitudinal studies. Article 2 builds upon the results of Article 1: we tested whether selfesteem instability and self-esteem contingency predicted variance in depression, over and above the vulnerability effect of low self-esteem detected in Article 1 (see the Results section for further information). For that purpose we conducted two primary studies testing an overarching model of concurring predictors. In sum, both articles were based on longitudinal data, and cross-lagged regression designs were employed. These questions concerning the exact nature of the relation between self-esteem characteristics and depression can be broadly located at the interface of personality and clinical psychology. In this dissertation, the research questions are mostly approached from a


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personality psychology perspective. Therefore, in the Theoretical framework, after presenting conceptual models of the relation between self-esteem and depression, I will elaborate on a broader personality psychology framework in which this work is embedded. More precisely, I will highlight how the research relates to two selected debates in contemporary personality psychology. However, in the Discussion I will also take a clinical psychology focus in that implications for clinical research and interventions will be outlined. Theoretical Framework Models of Self-Esteem and Depression Self-esteem can be defined as “a person’s appraisal of his or her value” (Leary & Baumeister, 2000, p.2). An overview of theoretical and empirical issues in the field of selfesteem is given in Sowislo and Orth (2013). Depression can be characterized by affective (e.g., sad mood), motivational (e.g., lack of drive), cognitive (e.g., decreased ability to concentrate), and physical (e.g., weight loss) symptoms (American Psychiatric Association, 2013). 1 Between low self-esteem and depression, different kinds of relations may exist. These can be described by applying five models, which were initially proposed to explain the relation of personality and psychopathology (Clark, 2005; Clark, Watson, & Mineka, 1994; Klein, Kotov, & Bufferd, 2011; Krueger & Tackett, 2003; McWilliams, Cox, & Enns, 2001; Santor, Bagby, & Joffe, 1997; Tackett, 2006). 2 The common cause model states that one common underlying ethological factor (i.e., a common genetic background) causes both low self-esteem and depression. The continuum/spectrum model assumes that low self-esteem and depression lie on a continuum which may extend from low self-esteem to full-blown depression. The vulnerability model proposes that negative evaluations of the self constitute a causal risk factor of depression. The pathoplasty model suggests that low self-esteem 1

Throughout this dissertation, I use the term depression to denote a continuous variable rather than a clinical category (American Psychiatric Association, 2013) as taxometric analyses suggest that depression is best conceptualized as a continuous construct (e.g., Prisciandaro & Roberts, 2005; Ruscio & Ruscio, 2000). 2 Theoretically, these models are applicable as well to the relation of other characteristics of self-esteem (i.e., self-esteem instability or self-esteem contingency) and depression. Yet, for the sake of conciseness, I expose the models exemplary for the relation of self-esteem level and depression.


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influences the expression of depression (e.g., severity, course, or response to treatment). The scar model postulates that low self-esteem is a consequence of depression. It is important to note that these models are not mutually exclusive, as multiple processes might operate simultaneously (Clark, 2005; Sowislo & Orth, 2013). The models describing the relation of low self-esteem and depression can be divided into two groups (Klein et al., 2011). The first two models (common cause model and continuum/spectrum model) assume that self-esteem and depression are caused by similar factors, but do not have any causal influence on each other. However, as delineated in more detail in Sowislo and Orth (2013), conceptual arguments and empirical results suggest that it is unlikely that self-esteem and depression are simply indicators of one common construct. 3 For instance, studies revealed that feelings of worthlessness are not present in all individuals diagnosed with depression (e.g., Minor, Champion, & Gotlib, 2005). Consequently, these two models will not be the main focus of this dissertation; however relevant third variable models that correspond to the common cause model will be tested. The last three models assume that low self-esteem and depression have causal influence on each other. In this dissertation, I focus on the vulnerability model and the scar model of low self-esteem and depression, as they dominate the relevant literature and represent accepted theoretical views of the causal relationship between self-esteem and depression (Sowislo & Orth, 2013). Moreover, there are primary studies on self-esteem and depression that provide information on scarring and vulnerability effects, but very few primary studies that provide information on pathoplasty effects (e.g., information on diagnoses and treatment), which is important for the meta-analytical approach of the first part of the dissertation (see below). Besides the questions of which model describes the relation of self-esteem and

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Furthermore, a problem specific to the continuum/spectrum model is that it does not explain how a relatively stable characteristic (i.e., low self-esteem) transforms into a relatively episodic state (i.e., depression; Klein et al., 2011).


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depression best, it is important to know whether the models are specific for depression or are also applicable or generalizable to other affective symptoms. In line with previous studies testing the affective specificity of depression-relevant variables and processes (e.g., A. T. Beck, Steer, & Epstein, 1992; R. Beck & Perkins, 2001; R. Beck et al., 2001; Hankin, Abramson, Miller, & Haeffel, 2004; Joiner, 1995; Mor & Winquist, 2002), the dissertation addresses this question by focusing on anxiety (for a more detailed argumentation on the choice of anxiety see Sowislo & Orth, 2013). Locating the Research Within the Field of Personality Psychology As mentioned above, this dissertation approaches the relation of self-esteem and depression from a personality psychology perspective. Although there is still disagreement about a comprehensive definition of personality psychology, the field can be tentatively characterized by the following major concerns: (a) enduring communalities among individuals, (b) enduring individual differences between individuals, (c) personality structure and, (d) growth and development (Buss, 1983; Cervone & Pervin, 2008; Grob, 2009; McAdams & Pals, 2007). As distinguished from clinical psychology, this field is nowadays mainly concerned with non-pathological human characteristics and behavior (Grob, 2009). To locate the research of this dissertation in the field of personality psychology, I will focus on two selected, current debates in this area involving other individual difference variables and show how they interrelate with the study of self-esteem and depression. 4 Debate 1: Uniqueness of self-esteem. Personality traits can be defined as “relatively enduring patterns of thoughts, feelings, and behaviors that distinguish individuals from one another” (B. W. Roberts & Mroczek, 2008, p. 31). It has repeatedly been debated whether

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Due to their significance in contemporary personality theory and research, I decided to focus on debates involving the Big Five personality traits (John, Naumann, & Soto, 2008; McCrae & John, 1992):.Although there have been alternative approaches to organizing personality structure (e.g., Allport & Allport, 1921; Cattell, 1956; Eysenck, 1990; Musek, 2007; Zuckerman, Kuhlman, Joireman, Teta, & Kraft, 1993), there is widespread consensus that individual differences can be meaningfully organized into five trait dimensions, namely neuroticisms, extraversion, openness, agreeableness, and conscientiousness (Cervone & Pervin, 2008; McAdams & Pals, 2006).


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self-esteem is a discrete variable that can be meaningfully investigated in isolation or whether it is a subordinate dimension which should be studied as an indicator of broader personality traits such as neuroticism (e.g., Judge, Erez, Bono, & Thoresen, 2002; Watson et al., 2002). In line with this, different theories of personality structure incorporate self-esteem as a lower-order component of personality traits (e.g., Eysenck, 1990; McAdams & Pals, 2006). For instance, five-factor theory (e.g., McCrae & Costa, 2008), which offers a theoretical trait model for the Big Five (see Footnote 4), claims that there are central conceptual differences between personality traits and self-esteem. More precisely, this theory states that personality is mainly structured into two core components, namely basic tendencies and characteristic adaptations. Basic tendencies are abstract psychological potentials that have to be inferred from overt behavior. They are furthermore assumed to be endogenous biological dispositions and thus to show high stability across situations, the lifespan, and across cultures. Characteristic adaptations are in turn influenced by basic tendencies and are assumed to adjust the individual to changing environments by varying considerably across situations, the lifespan, and different cultures (McCrae & Costa, 2008). Within this structure, the Big Five personality traits are assigned to the basic tendencies, while self-esteem is assigned to characteristic adaptations. Empirical investigations of the relation between self-esteem and the Big Five have shown that self-esteem correlates most strongly with neuroticism and relatively more weakly with the other four personality traits (Erdle, Gosling, & Potter, 2009; Graziano, JensenCampbell, & Finch, 1997; Judge et al., 2002; Robins, Tracy, Trzesniewski, Potter, & Gosling, 2001; Watson et al., 2002). Consequently, and in line with five-factor theory, some authors have concluded that self-esteem is simply a “lower-order facet of a broader personality trait” (Donnellan et al., 2011, p.725). For instance, Watson et al. (2002) argued that self-esteem forms the positive end of the narrower trait depression, a lower-order facet of neuroticism. However, five issues seem to challenge this view, as they suggest that self-esteem and


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the Big Five cannot be easily dichotomized along the criteria of heritability, situational consistency, long-term stability, and universality, and more specifically, that self-esteem seems to be more than just an indicator of neuroticism. 1.

Heritability: Self-esteem tends to have a genetic component that cannot be retraced to the Big Five personality traits (Donnellan et al., 2011). Additionally, evidence suggests that change in the Big Five is not only due to biological maturation, but also to environmental influences (e.g., Bleidorn, 2012; B. W. Roberts, Wood, & Smith, 2005; Scollon & Diener, 2006; Specht, Egloff, & Schmukle, 2011).

2.

Situational consistency: A study by Diener and Larsen (1984) revealed that selfesteem shows a moderately strong cross-situational consistency (data aggregated across situations). In line with this, the intercorrelations between some domainspecific self-esteem scales or factors are moderate to high (e.g., r = .73 for Physical Appearance and Opposite-Sex Peer Relations; Fleming & Courtney, 1984; Gilman, Laughlin, & Huebner, 1999; Marsh, 1990). Although the latter is no direct proof of situational consistency, it is congruent with the view that self-esteem does not merely fluctuate, but also is, to some degree, consistent across situations.

3.

Long-term stability: The rank-order stability of self-esteem tends to be similar to that of the Big Five personality traits with regard to its temporal pattern (Donnellan, Kenny, Trzesniewski, Lucas, & Conger, 2012; Kuster & Orth, 2013) and magnitude (B. W. Roberts & DelVecchio, 2000; Trzesniewski, Donnellan, & Robins, 2003). Furthermore, studies showed that there are meaningful mean-level changes in personality across the life-span, even in middle and old age (e.g., B. W. Roberts, Walton, & Viechtbauer, 2006).

4.

Universality: There is evidence for the universality of the self-enhancement motive (Sedikides, 1993; Sedikides, Gaertner, & Toguchi, 2003; but see Heine, Lehman, Markus, & Kitayama, 1999) and for the structure and correlates (Schmitt & Allik,


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2005) of the most widely used measure of self-esteem (Blascovich & Tomaka, 1991), namely the Rosenberg Self-Esteem Scale (RSE; Rosenberg, 1965). Moreover, a recent study (Gurven, von Rueden, Massenkoff, Kaplan, & Vie, 2013) challenges the universality of the Big Five, as these personality dimensions could not be replicated in an isolated indigenous population. 5.

Unique Variance: The cross-sectional correlations between self-esteem and neuroticism reported in the above-mentioned studies on the Big Five and self-esteem range from -.39 to -.70. 5 Moreover, the cross-sectional correlations between selfesteem and depression (which is sometimes assumed to be a facet of neuroticism; see above) reported in previous studies range from -.23 to -.82 (for a review see Orth, Robins, & Roberts, 2008). Thus, the correlation between self-esteem and neuroticism/depression is not as strong as would be expected if self-esteem were merely an indicator of neuroticism or else the opposite pole of a continuum with depression. These conceptual arguments and empirical results point to the high similarity of self-

esteem and the Big Five on defining characteristics of basic tendencies and characteristic adaptations. They suggest two important conclusions: First, it might be appropriate to study self-esteem as a unique construct and not just as a subcomponent of the Big Five personality traits (as specified by five-factor theory). Second, self-esteem might be categorized as a personality trait (and as a basic tendency in the framework of five-factor theory), especially due to its high rank-order stability (Kuster & Orth, 2013). 6 However, future research should clarify the role of self-esteem within the five-factor theory and other models and theories of personality. Finally, it is important to keep in mind the moderate to strong correlations between self-esteem, neuroticism, and depression reported above (Donnellan et al., 2011),

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For reasons of comparability only monosource correlations between self-report measures were considered. When studies reported emotional stability, I reverse coded it into neuroticism. 6 Rank-order consistency can be considered a major criterion for a psychological trait (Donnellan et al., 2012).


which point to their potential to be relevant third-variables (see below; Research question 6). Debate 2: Self-esteem’s implicative meaning. When evaluating the importance of an interindividual difference variable, its implicative or practical meaning, namely its contribution to the understanding of “what people want, say, do, feel, or believe” (Ozer & Benet-Martínez, 2006, p. 402), constitutes an important aspect. Accordingly, another debate in personality psychology has been centered on the question of whether personality traits indeed show predictive validity for consequential real-life outcomes (e.g., Ozer & BenetMartínez, 2006; B. W. Roberts, Kuncel, Shiner, Caspi, & Goldberg, 2007). Historically, Mischel (1968) observed a correlational upper limit of .30 for the empirical relation of personality and behavior. From this finding it has subsequently been inferred that the most substantial part of variance is accounted for by the situation and not by personality, and that personality traits consequently have little practical importance (B. W. Roberts et al., 2007). However, as Roberts (2007) summarizes, there are several arguments against this inference. First, the standards for evaluating effect sizes in the field of personality might have been set too high. When converted into a correlation metric, the size of the effect of other psychological phenomena (e.g., Meyer et al., 2001) as well as of situational influences (e.g., Funder & Ozer, 1983) tends to be comparable to the effect of personality. Second, the practical importance of a predictor not only depends on the magnitude of its association with the outcome; small effects can be important when they incorporate a highly consequential outcome (e.g., mortality; B. W. Roberts et al., 2007) and/or are cumulated across a person’s life (e.g., Abelson, 1985). In light of these arguments, qualitative (Caspi, Roberts, & Shiner, 2005; Ozer & Benet-Martínez, 2006) and quantitative reviews (e.g., B. W. Roberts et al., 2007) have come to the conclusion that the Big Five personality traits have implicative meaning, as they predict important life outcomes, for instance in the health, relationship, and occupational domains. It has been suggested that lower-level, narrower predictor variables can account for

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even more variance in life outcomes than higher-level, broader personality traits (Paunonen, Haddock, Forsterling, & Keinonen, 2003). 7 In line with this, and mirroring the abovementioned debate on personality traits, it is at present debated whether level of self-esteem has a significant causal impact on important life outcomes (e.g., Baumeister et al., 2003; Kuster, Orth, & Meier, 2013; Swann, Chang-Schneider, & McClarty, 2007). For instance, Baumeister et al. (2003) cautioned against the assumption of the causal importance of high self-esteem, as most of the studies they reviewed showed only weak effects of self-esteem and/ or used inadequate methods to examine the causal impact of self-esteem (i.e., identifying correlates of self-esteem). However, as delineated above, moderate effect sizes cannot be equated with practical insignificance (Swann et al., 2007). Furthermore, recently, methodically more rigorous, namely longitudinal studies (for a discussion of advantages and limitations of longitudinal analysis see Method and Discussion section) suggest that high selfesteem might have significant positive effects on some important life outcomes, such as health (e.g., Trzesniewski et al., 2006), low externalizing problems (e.g., Donnellan, Trzesniewski, Robins, Moffitt, & Caspi, 2005), and job satisfaction (Orth, Robins, & Widaman, 2012). Extensive past study notwithstanding, further research is still needed to clarify the predictive ability of level of self-esteem with regard to other life outcomes and to additionally test the causality of the hypothesized effects in experimental designs (Sowislo & Orth, 2013). Additionally, there is another related debate about whether other characteristics of self-esteem, besides its level, have important real-life consequences. In line with this, some researches have advocated considering, besides the role of level of self-esteem, the role of instability and contingency of self-esteem in psychological adjustment and behavior (e.g., Crocker & Wolfe, 2001; Kernis, Cornell, Sun, Berry, & Harlow, 1993; J. E. Roberts & Monroe, 1994). As mentioned above, self-esteem instability has been defined as the extent to 7

But see above for a critique of this hierarchical model of personality and self-esteem.


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which self-esteem fluctuates across relatively short time intervals such as hours and days (Kernis, 2003, 2005; Meier, Orth, Denissen, & Kühnel, 2011). Related to the concept of selfesteem instability, self-esteem contingency has been defined as the extent to which selfesteem fluctuates in response to self-relevant events (Crocker & Park, 2004; Crocker & Wolfe, 2001; Meier et al., 2011). Empirically, self-esteem instability and contingency have both been linked to behavioral and emotional outcomes such as verbal defensiveness (e.g., Kernis, Lakey, & Heppner, 2008), supportiveness (e.g., Park & Crocker, 2005), and anger (e.g., Kernis, Grannemann, & Barclay, 1989). More specifically, for depression it has been suggested that fluctuations in self-esteem are an even more important vulnerability factor than level of self-esteem (e.g., Crocker, 2002), or that alternatively fluctuations in self-esteem moderate the vulnerability effect of level of self-esteem (e.g., Kernis et al., 1998). However, as mentioned in the Introduction, it has not yet been empirically clarified whether fluctuations in self-esteem (i.e., self-esteem instability and contingency) explain incremental or even greater variance in depression than level of self-esteem (for more details on this debate see Sowislo et al., 2013). Having discussed the predictor variables and turning now to the outcomes, it is noteworthy that as yet there is neither a list of important life outcomes nor even definite criteria to define them. Depression might be considered as but one consequential life outcome for the following reasons: First, for example, major depression is highly prevalent (Kessler et al., 2005) and highly recurrent (Kessler et al., 2003; Solomon et al., 2000). It “is associated with impaired functioning in the relationship (e.g., Davila, Karney, Hall, & Bradbury 2003; Wade & Pevalin, 2004), work (e.g., Adler et al., 2006; Kessler et al., 2006), and health domains (e.g., Raikkonen, Matthews, & Kuller, 2007; Wulsin & Singal, 2003) and with elevated rates of suicidal behavior (e.g., Berman, 2009; Harris & Barraclough, 1997)” (Sowislo & Orth, 2013, pp. 3-4). Third, the absence of depression is socially valued, namely most if not all individuals value not being depressed.


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Research Questions and Contribution of the Research Given the debates and arguments exposed above, I believe that it is useful and interesting to study the relation of self-esteem and depression. First, self-esteem seems to be a unique construct that merits research attention for its own sake (i.e., not only in terms of a sub-dimension of broader personality traits). Second, showing that self-esteem has consequences for important life outcomes is crucial for the demonstration of the implicative meaning and thus the importance of the construct. Third, depression, or more precisely not being depressed, represents an important life outcome. In order to shed more light on different aspects of the relation of self-esteem and depression, this dissertation mainly addresses the following seven questions: 1.

Prospective relation of low self-esteem and depression: Is low self-esteem a risk factor for future depression (vulnerability model), or does depression erode selfesteem (scar model)? (Article 1).

2.

Prospective relation of low self-esteem and anxiety: Are the vulnerability and scar model valid for anxiety too, or do they show affective specificity for depression? (Article 1)

3.

Moderators of the prospective effect of low self-esteem on depression: Do specific conditions (gender, age, time lag between assessments, sample type, and measures of the constructs) moderate the vulnerability effect of low self-esteem on subsequent depression? (Article 1) 8

4.

Prospective relation of different self-esteem characteristics and depression: Do other characteristics of self-esteem besides its level, namely self-esteem instability and contingency, explain incremental or even greater variance in depression? (Article 2)

5. 8

Interactive effects between the different self-esteem characteristics: Do the three

The following research questions are based on the vulnerability effect of low self-esteem on depression only, as the results of Article 1 supported the vulnerability model (see Results section). Furthermore, the number of studies on which the other cross-lagged effects were based was relatively low, which limited the statistical power of moderator analysis (Sowislo & Orth, 2013).


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characteristics of self-esteem interact in the prediction of subsequent depression? (Article 2) 6.

Third variables in the prospective relation of low self-esteem and depression: Do the Big Five personality traits account for the vulnerability effect of low self-esteem on subsequent depression? (Article 2)

7.

Divergent approaches to measuring self-esteem contingency: Do the results concerning the relation of different self-esteem characteristics and depression hold for a self-report measure and a new statistical index of self-esteem contingency? (Article 2) The present dissertation contributes to personality psychology theory. First, it aims to

inform the debate on the implicative meaning of self-esteem level by means of prospective, meta-analytical results on its relation to depression. Second, it helps in refining theories of self-esteem and depression by giving insight into the role of different characteristics of selfesteem in the etiology of depression. Furthermore, this dissertation provides translational implications for applied fields of clinical psychology. More precisely, it offers some information on the importance of self-esteem interventions and on the self-esteem characteristics and populations self-esteem interventions may target in order to prevent depression. Method Research Design Longitudinal design: Cross-lagged regression analysis. In both original articles, a specific longitudinal design, namely a cross-lagged regression analysis framework was employed. Longitudinal designs are more appropriate in investigating the causal relation between selfesteem and depression than cross-sectional designs, as the former account for one central principle of causality, namely the precedence of the cause over the consequence (Cohen, Cohen, West, & Aiken, 2003; Hume, 1978), or to put it differently, the necessity of time for


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causal processes to unfold (Kuster, Orth, & Meier, 2012). More specifically, as discussed in Sowislo and Orth (2013), cross-lagged regression analysis has another crucial advantage over cross-sectional correlation analysis: cross-lagged regression analysis, but not cross-lagged correlational analysis, statistically controls for the stability of the outcome. Therefore, crosssectional regression analysis allows ruling out that a relation between predictor and outcome is simply due to a high stability in the outcome. It is however important to note that although longitudinal designs can indicate whether the data are consistent with a causal model, they do not allow for a strong causal conclusion regarding the relation of self-esteem and depression (for further information see Discussion section; Finkel, 1995; Little, Preacher, Selig, & Card, 2007; Sowislo & Orth, 2013). With the goal of partially overcoming this limitation, we included relevant third-variable influences (i.e., neuroticism) on the relation between self-esteem and depression in our model (see Research question 6). Meta-analysis. In order to synthesize and analyze the vast amount of inconsistent findings on the relation between self-esteem level and depression, Article 1 used a meta-analytical approach. Meta-analysis is a technique of quantitative research synthesis which allows combining the effects of primary studies and evaluating the statistical significance of the summary effect (Hedges & Olkin, 1985). For this purpose each study’s findings are expressed in the form of effect sizes, which represents a standardized summary statistic of the data included in each study (Lipsey & Wilson, 2001). Meta-analysis has multiple advantages over primary studies and over qualitative research synthesis (i.e., literature reviews). For instance, in comparison to primary studies, meta-analysis estimates effects with greater power (Lipsey & Wilson, 2001) and greater precision (Schmidt, 1992), and provides the possibility of testing for moderators that are difficult to test for in primary studies (e.g., time lag; Sowislo & Orth, 2013). In comparison to literature reviews, meta-analysis is capable of handling information from a larger number of studies and is considered to be more systematic, as it uses an


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explicitly defined methodology. Moreover, meta-analysis is sensitive not only to statistical significance but also to different strengths of effects across studies and thus represents findings in a more differentiated manner (Lipsey & Wilson, 2001). Samples and Statistical Procedures Research questions 1-3: Article 1. In order to investigate Research questions 1 to 3, we conducted a meta-analysis of longitudinal studies. Literature search and subsequent application of inclusion criteria left 53 journal articles and seven unpublished dissertations for analysis, which included 80 relevant samples. Thus, our dataset for the meta-analysis comprised 77 samples providing information on the relation between self-esteem and depression and 18 samples providing information on the relation between self-esteem and anxiety. The average proportion of female participants was 64% and there was a wide variety of sample characteristics, such as mean age of participants (M = 27.7 years, SD =17.4, range: 8.2-79.3) and study characteristics, such as time lag between assessments (M = 1.23 years, SD =1.81, range: 1.00 week-13.00 years), sample type (i.e., convenience samples other than college student samples, college student samples, representative samples, and clinical samples), and measures of the constructs of interest. To answer Research question 1 we computed effect sizes (i.e., standardized regression coefficients) for the relation of self-esteem and depression whenever not directly reported in the primary studies. More specifically, the following effect sizes corresponding to the structural paths in a cross-lagged regression model were used for subsequent analyses: The cross-sectional correlation between the constructs (for Time 1 as an example), (b) the stability coefficients of the constructs (e.g., effect of self-esteem at Time 1 on self-esteem at Time 2, controlling for depression at Time 1), and (c) the cross-lagged effects between the constructs (e.g., the effect of self-esteem at Time 1 on depression at Time 2, controlling for depression at Time 1). For these, weighted mean effect sizes were computed and the respective effect size


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distributions were tested for homogeneity. To investigate Research question 2, the exact same procedure was used for the relation between self-esteem and anxiety. To answer Research question 3, we examined moderators (gender, age, time lag between assessments, sample type, and measures of the constructs) of the effect of self-esteem on depression using multiple regression analysis (for continuous and dichotomous variables) and analysis of variance (for polytomous variables). Research questions 4-7: Article 2. In order to investigate Research questions 4 to 7, we tested overarching cross-lagged regression models in two primary studies with different study characteristics. The two longitudinal datasets contain multiple diary assessments, which were necessary to calculate the measure of self-esteem instability and one of the two alternative measures of self-esteem contingency (see below). In Study 1, data came from 372 adults, who were assessed at 2 waves over 6 months, including 40 diary assessments of daily self-esteem and daily events assessments at Wave 1. The mean age of participants at Time 1 was 29.1 years (SD = 8.8, range = 18 to 61) and the proportion of female participants was 50%. In Study 2, data came from 235 young adults, who were assessed at 2 waves over 6 weeks, including about 6 diary assessments of daily self-esteem and daily events at each wave. The mean age of participants at Time 1 was 18.0 years (SD = 1.3, range = 16 to 23) and the proportion of female participants was 36%. In both studies, self-esteem level was measured with the Rosenberg Self-Esteem Scale (RSE; Rosenberg, 1965) and depression was measured with the Center of Epidemiologic Studies Depression Scale (CES-D; Radloff, 1977). Selfesteem instability was computed as the intraindividual standard deviation of daily self-esteem across daily assessments. 9 Self-esteem contingency was measured by self-report and by a new statistical index. More precisely, self-reported self-esteem contingency was assessed with selected subscales of the Contingencies of Self-Worth Scale (CSW; Crocker, Luhtanen, Cooper, & Bouvrette, 2003). The statistical index of self-esteem contingency was computed 9

More precisely, daily self-esteem was assessed with five-item versions of the RSE.


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based on the diary data, capturing daily event-related fluctuations in daily self-esteem (cf. Butler et al., 1994). 10 Finally, the Big Five personality traits were measured with the Big Five Inventory (BFI; John, Donahue, & Kentle, 1991). To answer Research question 4, we examined reciprocal main effects between all three characteristics of self-esteem and depression, using one overarching cross-lagged regression model. To investigate Research question 5, we examined the main effect together with the interactive effects of the self-esteem characteristics on depression. To examine Research question 6, we additionally controlled for the Big Five personality traits. Finally, to investigate Research question 7, we included the self-report measure and the statistical index of self-esteem contingency in all the above-mentioned analyses. Results Article 1 Research question 1. With regard to the prospective relation of low self-esteem and depression, the results of Article 1 provide consistent support for the vulnerability model and only weak support for the scar model. More precisely, the effect of self-esteem on subsequent depression (β = -.16) was significantly stronger than the effect of depression on subsequent self-esteem (β = -.08). This indicates that low self-esteem is a prospective risk factor for depression. Research question 2. Concerning the prospective relation of low self-esteem and anxiety, the results of Article1 demonstrate symmetric and reciprocal effects. More precisely, self-esteem predicted subsequent anxiety with β = -.10 and anxiety predicted subsequent self-esteem with β = -.07. Thus, the pattern of structural prospective relations with self-esteem tends to show affective specificity as it differs for depression and anxiety, respectively. This suggests that the vulnerability and scar effects are at least not entirely attributable to a common distress factor, but at least in part to anxiety and depression specific factors, respectively. 10

More precisely, daily events were operationalized based on the report of the occurrence of different positive and negative events, which were subsequently aggregated into an overall daily event measure.


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Research question 3. With reference to the moderators, Article 1 focused on the prospective effect of low self-esteem on depression, since it emerged as being the strongest cross-lagged effect in the previous analyses. Moreover, the other cross-lagged effects were estimated based on a relatively low number of studies, which limits the statistical power of moderator analysis. First, in a multiple regression analysis with the proportion of female participants, mean age of participants, and sample type (dichotomized) as predictors of the vulnerability effect, only sample size yielded a significant regression weight. It indicated that the effect of low self-esteem on depression was smaller in representative than in nonrepresentative samples. Subsequently, an analysis of variance using the original polytomous variable sample type was conducted in order to investigate its relation importance for the vulnerability effect. The results suggest that the vulnerability effect was present in all sample types, namely convenience samples other than college students, college student, representative, and clinical samples. Finally, two separate analyses of variance demonstrated that the effect size was relatively similar across the different measures of self-esteem and across the different measures of depression. In summary the moderator analyses suggest that the effect of low self-esteem on subsequent depression holds for samples with different gender and age compositions, for different time lags, for different measures of self-esteem and depression, and for representative, clinical, and convenience samples. Article 2 Research question 4. To investigate the prospective relation of different self-esteem characteristics and depression, in Article 2 cross-lagged regression models that simultaneously included self-esteem level, instability, contingency and depression were tested. In Study 1, self-esteem level was the only variable that had a cross-lagged effect on subsequent depression. Self-esteem instability and self-esteem contingency did not predict change in depression, controlling for the effect of self-esteem level and controlling for prior levels of depression. These results were replicated in Study 2. Thus, corresponding to the


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findings of Article 1, Article 2 provides support for the vulnerability model of low self-esteem and depression, suggesting that low, but not unstable and contingent self-esteem, is a risk factor for depression. These findings held for men and for women and when content overlap between the measures of self-esteem level and depression was controlled for. 11 Research question 5. Furthermore, Article 2 tested whether there were - in addition to the main effects- interactive effects on depression between the self-esteem characteristics. The results of both Study 1 and Study 2 showed that none of the interaction terms was significant. Research question 6. Concerning relevant third variables in the prospective relations of low self-esteem and depression, Article 2 (Study 1) demonstrated that the vulnerability effect of low self-esteem on depression is not attributable to a confounding by broad personality traits. In both studies, the effect of low self-esteem on depression held when neuroticism and the other Big Five personality traits were controlled for. Moreover, none of these potential third variables had a significant prospective effect on depression, after the effects of low selfesteem were controlled for. Research question 7. Finally as described above, Article 2 used divergent approaches to measure self-esteem contingency. In Studies 1 and 2, all of the above reported results held for both the established self-report measure and the more objective measure (statistical index) of self-esteem contingency, which strengthens the conclusion that contingency of self-esteem is not a risk factor for depression. General Discussion The aim of the present dissertation was to clarify the exact nature of the prospective relation between self-esteem characteristics (i.e., level, instability, and contingency) and depression by testing alternative models of self-esteem and depression. As mentioned in the Introduction (for more detailed reviews see Sowislo & Orth, 2013; Sowislo et al., 2013)

11

It is noteworthy that there was one exception to this pattern: In Study 2, when controlling for content overlap, depression was significantly predicted by self-esteem instability as well, over and above the effect of self-esteem level. However, the size of the instability effect was much smaller than the size of the level effect.


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previous studies have yielded inconsistent results with regard to the questions (a) whether the relation between low self-esteem and depression is best described by the vulnerability (i.e., low self-esteem contributes to depression) or/ and the scar model (i.e., depression erodes selfesteem) of low self-esteem, and (b) whether there are valid alternative vulnerability models of self-esteem (i.e., unstable or contingent self-esteem contributes to depression over and above the contribution of low self-esteem). To overcome possible causes for these inconsistencies we increased the overall statistical power (e.g., by means of a meta-analysis in Article 1 and by means of two primary studies in Article 2) and tested models that included all three characteristics of self-esteem simultaneously (Article 2). Article 1 revealed that the vulnerability effect of low self-esteem on subsequent depression is significantly stronger that the scar effect of depression on subsequent level of self-esteem. Moreover, Article 2 showed that only level, but not instability and contingency, of self-esteem acts as a vulnerability factor for future depression and that level, instability, and contingency of self-esteem do not in the prospective prediction of depression. Thus both articles provide converging support for the vulnerability model of low self-esteem and depression. Implications General implications. The results indicate that the vulnerability model is specific for low self-esteem and (at least with regard to its extent) depression. Moreover, the effect of low selfesteem on subsequent depression tends to be robust across different study, sample, and person characteristics (i.e., when testing moderation by third-variables) and when relevant third variables are controlled for (i.e., when testing for confounding by third-variables). Specificity. The present research suggests that the vulnerability effect is specific for the predictor (i.e., self-esteem level) and, with regard to its pattern, also for the outcome (i.e., depression). First, although in the literature alternative models of vulnerable self-esteem have been proposed (i.e, vulnerability as a function of self-esteem level, vulnerability as a function of self-esteem instability, and vulnerability as a function of self-esteem contingency; Sowislo


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et al., 2013), the results indicate that the vulnerability model is specific to low self-esteem and not applicable to unstable self-esteem and contingent self-esteem. Moreover, a study by Orth, Robins, Widaman, and Conger (in press) indicates that the vulnerability effect tends to be for the most part specific for level of global self-esteem (i.e., general evaluations of the self). When the authors tested for the prospective effects of several dimensions of domainspecific self-esteem (i.e., evaluations in specific self-relevant domains such as intellectual abilities, physical appearance, and social competence; Swann & Bosson, 2010) on depression, only one domain-specific self-evaluation (i.e., honesty-trustworthiness) predicted depression. Second, our results demonstrate the pattern of scarring and vulnerating by low self-esteem may be specific to depression. More precisely, the vulnerability effect of low self-esteem on anxiety is much smaller than on depression and of similar strength as the scar effect. The fact that depression and anxiety are differentially linked to low self-esteem suggests that low selfesteem influences (at least partly) depression-specific factors and not merely global distress. Robustness. First, the present research showed that the effect of low self-esteem on subsequent depression is not moderated by third-variables, namely study, sample, and other self-esteem characteristics. With regard to study and sample characteristics, the effect (a) does not differ across different gender and age composition of the sample, measures of selfesteem and depression, or time lags between assessments and (b) and was present in all sample types (i.e., representative, convenience, and clinical samples). 12 The fact that in Article 1, the vulnerability effect was robust across different measures of self-esteem and depression (which may differ in their degree of content overlap) furthermore suggests that it is not biased by potential content overlap (i.e., depression items that tap into the self-esteem construct). Article 2 and other studies (Kuster et al., 2012; Orth et al., 2008; Orth, Robins,

12

The results suggest that the structural relations between self-esteem and depression are not influenced by the tested potential moderator variables. However, the structural robustness does not imply an absence of mean level difference in self-esteem or depression across different values of the variables. For instance, men and women may of course differ in their average levels of self-esteem and depression (Hyde, Mezulis, & Abramson, 2008; Kling, Hyde, Showers, & Buswell, 1999; Major, Barr, Zubek, & Babey, 1999).


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Trzesniewski, Maes, & Schmitt, 2009) that directly controlled for content overlap by repeating the analysis after omitting relevant items for the depression measures cumulatively bolster this suggestion. However, concerning robustness across different cultural contexts the dissertation does not allow direct conclusions, as nearly all of the primary studies included were conducted in Western cultural contexts (e.g., in Article 1, it was only possible to include two studies conducted in Asia). Relatedly, for instance Kwan, Kuang, and Hui (2009) proposed that the extent to which self-esteem is a predictor for psychological adjustment depends also on the specific sources people of a certain culture derive their self-esteem from. Yet, two recent studies have shown that the association between low self-esteem and depression is present in more collectivistic cultures as well. Chen, Chiu, and Huang (2013) conducted a meta-analysis of Taiwanese samples and found a cross-sectional correlation of self-esteem and depression of -.48. Although this finding is comparable to ours (e.g., r = -.58 in Article 1), it must be noted that cross-sectional correlations are limited in interpretation with respect to the vulnerability effect (see above). The study of Orth et al. (in press) is methodologically more rigorous, as the relation of self-esteem and depression in Mexican-origin youth was examined using longitudinal data and cross-lagged regression analysis. Controlling for prior levels of depression, they found an effect of self-esteem on subsequent depression of -.14 to .15 (exact value depending on the self-esteem measure used), which is highly comparable to our finding (i.e., β = -.16 in Article 1). The results of these two studies might, in combination with our results, give a tentative hint for the robustness of the vulnerability effect of low selfesteem on depression across rather individualistic and rather collectivist cultures. With regard to other self-esteem characteristics, previous research for example suggested that the vulnerability effect might be stronger if a person’s self-esteem is not only low but also temporally stable (Kernis, Grannemann, & Mathis, 1991). However, the converging results of the two studies in Article 2 clearly show that the effect of low self-


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esteem on depression is robust across different levels of self-esteem contingency and selfesteem instability. This is in line with a recent study by Wouters et al. (2013) who found no prospective interaction between self-esteem level and self-reported self-esteem contingency in the prediction of depression in university freshmen. Second, the present research showed that the effect of low self-esteem on subsequent depression is not confounded by third-variables. Specifically, although proposed by several authors (Hankin, Lakdawalla, Carter, Abela, & Adams, 2007; S. B. Roberts & Kendler, 1999; Watson et al., 2002), the vulnerability effect is not due to the fact that neuroticism and the other Big Five personality traits influence low self-esteem and depression and thereby create a spurious link between these two constructs. The study by Orth et al. (in press) described above, further bolsters the conclusion that the vulnerability effect is not attributable to the effect of third-variables, as it rules out a confounding by social support, maternal depression, stressful events, and relational victimization. It is important to note that this dissertation and the study by Orth et al. (in press) provide evidence against the common cause model (Klein et al., 2011; see Theoretical framework), which corresponds to the third-variable models tested. This is of particular practical importance, as, if the common cause model was correct, interventions aimed at increasing self-esteem would have no effect on the risk of depression (Orth et al., in press). In summary, the specificity and the robustness of the vulnerability effect strengthens confidence in the vulnerability model of low self-esteem and depression (Garber & Hollon, 1991). Nevertheless, future research should continue (a) to test potential moderators in order to find out why some people with low self-esteem have a stronger risk of depression than others, and (b) to test other theoretically-relevant third-variables (i.e., biological factors such as a common genetic base) that might account for the vulnerability effect of low self-esteem on subsequent depression. Implications for self-esteem research and personality psychology.


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Effect sizes and implicative meaning. Overall, the dissertation proposes that low self-esteem serves as a specific and robust risk factor for depression. But how strong is this vulnerability effect? Using the conventions suggested by Cohen (1988), the effect is about small to medium size across both articles. In their review on the consequences of high self-esteem Baumeister et al. (2003) interpreted effect sizes in the neighborhood of .20 as weak and inconsequential. However, I think that several aspects must be kept in mind when evaluating the practical importance of the vulnerability effect itself and with reference to the implicative meaning of self-esteem: First, in this dissertation, cross-lagged effects (i.e., standardized regression weights) were used as effect size. As these are controlled for the stability of the outcome (i.e., depression) they are by implication smaller than the corresponding bivariate correlations. Second, the standards for effect sizes in the field of personality must be reevaluated in light of evidence that (a) predictors (e.g., self-esteem) have limited validity in predicting multiply determined outcomes (such as depression; Ahadi & Diener, 1989; Swann et al., 2007) and that (b) when converted in a correlational metric, the effect of other psychological phenomena as well as of situational influences tends to be of comparable size (see Theoretical framework). Third, even a statistically small effect of self-esteem on depression can have practical importance, as depression is a consequential outcome (e.g., through its association with mortality; Cuijpers & Smit, 2002; for a more detailed argumentation see Introduction). In the background of these arguments, self-esteem seems to have an important effect on subsequent depression (see arguments 1 and 2). Furthermore, especially in combination with other studies demonstrating consequences of self-esteem, the vulnerability effect points to the implicative meaning of self-esteem (see arguments 1-3). For instance, a recent study by Kuster et al. (2013) showed that high self-esteem prospectively predicts better work conditions, and better work outcomes. However, and in line with our results on self-esteem and depression, nearly all of the reverse effects (i.e., work conditions and outcomes predicting depression) were nonsignificant (for studies showing a similar pattern of effects between self-


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esteem and different life outcomes see e.g., Orth et al., 2012; Trzesniewski et al., 2006). As self-esteem seems to be a predictor, rather than an outcome of psychological adjustment (e.g., depression) and success (e.g., in the work domain), it might be premature to conclude that self-esteem is "not a major predictor or cause of almost anything" (Baumeister et al., 2003, p.37) and to abandon self-esteem as an important construct (Flynn, 2005). On the contrary, self-esteem “matters” (Swann et al., 2007), as it prospectively predicts important life outcomes (such as depression), even when potentially confounding variables (such as neuroticism) are appropriately controlled for. Self-esteem as a unique, trait-like variable. Our results contribute to the existing evidence showing that self-esteem is a unique, trait-like construct. First, as mentioned in the theoretical framework, some authors (e.g., Watson et al., 2002) suggest that self-esteem is merely an indicator of a more general neuroticism factor. However, our findings point to the uniqueness of the construct, as self-esteem has predictive power on depression even when neuroticism is controlled for. Second, in both articles, the stability of self-esteem (i.e., rank-order stability) was larger than the stability of depression and comparable to the stability of the Big Five personality traits (B. W. Roberts & DelVecchio, 2000). Thus, given the above-mentioned definition of personality as relatively stable patterns of thoughts, feelings, and behaviors (B. W. Roberts & Mroczek, 2008, p. 31), the dissertation proposes that self-esteem should be viewed as a personality trait (Kuster & Orth, 2013). In sum, our findings are in line with the assumption that self-esteem is a unique, traitlike construct with implicative meaning. Implications for clinical psychology and depression interventions. As this research shows that low self-esteem is meaningfully related to subsequent depression, I believe that it makes sense to take steps to improve self-esteem (Swann et al., 2007). However, there are for instance the following three prerequisites to the use of self-esteem programs in the prevention and therapy of depression. First, future studies have to support the hypothesized causality of


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the relations between self-esteem level and depression. Second, it is important that selfesteem interventions are actually capable of improving self-esteem. Third, self-esteem interventions have to be actually capable of alleviating depression and other psychosocial outcomes. The first prerequisite will be discussed below in the Limitations section. With regard to the second prerequisite, in their meta-analysis of interventions for self-esteem in children and adolescents, Haney and Durlak (1998) reported that these interventions significantly enhancement self-esteem. More recently, another meta-analysis by O’Mara, Marsh, Craven, and Debus (2006) also showed that programs designed to increase self-esteem in children and adolescence are effective. Finally, with regard to the third prerequisite, to the best of my knowledge, there have been only few primary studies that directly address the effectiveness of self-esteem interventions on alleviating depression (for an example see Kahn, Kehle, Jenson, & Clark, 1990). However, the meta-analysis of Haney and Durlak (1998) revealed that self-esteem interventions lead to positive changes in depression and other variables of psychological adjustment. 13 If these three prerequisites were fulfilled, our results would recommend that self-esteem interventions should primarily seek to increase a person’s level of self-esteem rather than focus on the instability and contingency of self-esteem. Moreover, they suggest that future research should investigate potential mediators of the vulnerability effect of low self-esteem on depression (for an example see Kuster et al., 2012): First, because most of the effective self-esteem programs are multifaceted schemes which additionally target components other than self-esteem (e.g., interpersonal relationships) research should specify how self-esteem interventions improve depression. Second, given the high stability of self-esteem, such mechanistic insights may provide a basis for interventions that aim to interrupt the link of self-esteem and depression at the stage of the mediating process. Limitations 13

Furthermore, enhancement of self-esteem seems to contribute to the effectiveness of a range of other types of interventions (such as cognitive-behavioral therapy; DuBois & Flay, 2004).


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A first limitation common to both articles of this dissertation is that the study design does not allow for strong conclusions regarding the causality of the relations between selfesteem characteristics, depression, and anxiety, because of the correlational study designs. Consequently, the effects under investigation may be caused by third variables that were not controlled for (Finkel, 1995; Little et al., 2007). Therefore, future research should experimentally manipulate self-esteem (for examples of the experimental manipulation of implicit self-esteem see Baccus, Baldwin, & Packer, 2004; Dijksterhuis, 2004) in order to test for the causality of the vulnerability effect. However, this is practically (e.g., due to selfesteems high stability; Blascovich & Tomaka, 1991) and ethically (e.g., due to self-esteem’s relation with positive emotions and psychological adjustment) difficult and furthermore studies conducted in the laboratory tend to have limited ecological validity. To put it differently, especially in order to evaluate the implicative meaning of interindividual difference variables not only circumscribed laboratory contexts, but real-world situations are needed (Donnellan et al., 2011; Ozer & Benet-Martínez, 2006). Therefore, future research should continue testing relevant third-variables that might account for the effect of low selfesteem on subsequent depression (Sowislo & Orth, 2013). A second limitation pertains to the measurement of the constructs. With regard to the assessment of self-esteem level and depression, this dissertation used self-report measures exclusively; that is, participants were explicitly asked to reflect on their self-worth and depression, respectively. Although the self-esteem measure used in most of this work, namely the RSE (Rosenberg, 1965), possesses a high degree of reliability and validity (Donnellan et al., 2011; Sowislo & Orth, 2013), it is questionable whether (a) individuals are fully aware of their feelings of self-worth and are actually capable of comprehensively self-reporting on them; (b) whether these self-reports are biased by social desirability. Therefore, studies should additionally employ implicit measures of self-esteem level (Greenwald & Farnham, 2000) to test whether the vulnerability effect can be replicated. However, it is important to note that


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although implicit measures are a promising avenue for self-esteem measurement, they have to be used with caution, as there is not yet sufficiently strong support for their validity (Bosson, Swann, & Pennebaker, 2000; Krizan & Suls, 2008, 2009). A possible problem of self-report measures of depression is not so much that they might tap milder syndromes (see Footnote 1), but that they may measure something conceptually different from major depressive disorder. Therefore, conclusions about the antecedents of major depressive disorder should be based on clinical interviews. However, given that meta-analytic results suggest that the prospective effect of low self-esteem on depression holds in both clinical and nonclinical samples (Sowislo & Orth, 2013), the present findings might also be relevant for clinical levels of depression. With regard to the assessment of self-esteem contingency, one strength of this research is that we used a more objective measure, namely the statistical index of self-esteem contingency (cf. Butler et al., 1994). However, this approach is not completely objective because the index is again based on self-reported positive and negative events, which may be subjective to different biases. For instance, if an inaccuracy of event memory happens to correlate with fluctuations in self-esteem, the validity of the statistical index is heavily restricted. 14 In this case, future research would benefit from using an objective measure of events. Future Research The findings of this dissertation suggest several avenues for future research. For instance, future studies should test the longitudinal relation of self-esteem and depression not only within, but also between individuals. Both self-esteem and depression are constructs that have a strong interpersonal component (Baumeister & Leary, 1995; Joiner & Timmons, 2002). Especially close, intimate relationships, such as those involving romantic partners are a

14

As but one example, people with high self-esteem contingency might recall and report negative events better than people with low self-esteem contingency, as these events have a stronger impact on their self-esteem (Butler et al., 1994).


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particularly important source of self-esteem (Leary & Baumeister, 2000). When studying selfesteem and depression in couples, there are four possible interpersonal effects between the constructs (partner effects), namely a vulnerability effect of a person’s level of self-esteem on her or his partner’s depression, a scar effect of a person’s depression on her or his partner’s level of self-esteem, a cross-over effect of a person’s level of self-esteem on her or his partner’s level of self-esteem (e.g., Neff, Sonnentag, Niessen, & Unger, 2012), and a contagion effect (e.g., Katz, Beach, & Joiner, 1999) of a person’s depression on her or his partner’s depression. 15 Consequently, it would be interesting to find out whether the pattern from the within level, namely the domination of a vulnerability over a scar effect is replicated at the between level. From a methodological perspective it is important to rule out that the effects of interest are not artifacts of selection (i.e., because depressive individuals may prefer depressive partners; Joiner & Katz, 1999) and therefore longitudinal actor-partnerinterdependence models (Cook & Kenny, 2005; Kenny, Kashy, & Cook, 2006) should be employed. Ultimately, this line of research might help to shed light on the interpersonal antecedents and etiology of low self-esteem and depression, respectively. As another example, future studies should investigate the relation of self-esteem and depression from a lifespan developmental perspective. Both self-esteem and depression show systematic developmental changes over the life course that can be captured by quadratic curves. While the normative trajectory of self-esteem increases during adulthood and decreases in old age (e.g., Orth, Trzesniewski, & Robins, 2010; Robins & Trzesniewski, 2005), the normative trajectory of depressive symptoms tends to its mirror-inversion, at least to some extent. More precisely, it decreases during adulthood and increases again in older adulthood (Sutin et al., 2013). It is consequently possible that the development of self-esteem influences the development of depression and explains interindividual differences in

15

For reasons of conciseness, the theoretical appeal of these effects cannot be discussed within this dissertation. Interested readers are advised to consult for example the articles by Joiner & Katz (1999) and by Neff et al. (2012).


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depression trajectories (and vice versa). Thus, future research should use a latent growth curve approach (e.g., controlling for time-varying covariates, bivariate growth curves; Bollen & Curran, 2006; Preacher, Wichman, MacCallum, & Briggs, 2008) to analyze the interlocking trajectories of self-esteem and depression over the life span. Conclusion In sum, the results of this dissertation have important theoretical and practical implications. In theoretical terms it shows that self-esteem is not an empty construct that is largely redundant with other measures of psychological adjustment such as neuroticism and depression (Orth & Robins, in press). On the contrary, self-esteem seems to be a unique interindividual difference variable with implicative meaning, as it is a powerful predictor of a highly consequential life outcome, namely depression. Also with respect to its high, trait-like rank-order stability, personality psychology should consider the categorization of self-esteem as a personality trait and discuss its role in contemporary models of personality. In practical terms, the findings on the relation between self-esteem and depression have implications for understanding the etiology of depression, identifying at-risk individuals, and designing treatment (Klein et al., 2007). For example, especially low self-esteem individuals are at risk of future depression and might benefit from preventions. Furthermore, preventions and treatment of depressions should primarily seek to increase a person’s level of self-esteem rather than focus on instability and contingency of self-esteem. Being well aware of the fact that depression causes significant suffering and loss of quality of life in patients and relatives (Cuijpers, 2011), I hope that the findings of this dissertation contribute to designing interventions that might ultimately help to alleviate the burden of depression.


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APPENDIX Article 1 Sowislo, J. F. & Orth, U. (2013). Does low self-esteem predict depression and anxiety? A meta-analysis of longitudinal studies. Psychological Bulletin, 139, 213-240.

Article 2 Sowislo, J. F., Orth, U., & Meier, L. L. (2013). Comparing the effects of low, unstable, and contingent self-esteem on depression: Two longitudinal studies. Manuscript submitted for publication. 16

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Submission status of the manuscript as it was on 15th July, 2013

Psychological Bulletin 2013, Vol. 139, No. 1, 213–240

© 2012 American Psychological Association 0033-2909/12/$12.00 DOI: 10.1037/a0028931

Does Low Self-Esteem Predict Depression and Anxiety? A Meta-Analysis of Longitudinal Studies Julia Friederike Sowislo and Ulrich Orth University of Basel

Low self-esteem and depression are strongly related, but there is not yet consistent evidence on the nature of the relation. Whereas the vulnerability model states that low self-esteem contributes to depression, the scar model states that depression erodes self-esteem. Furthermore, it is unknown whether the models are specific for depression or whether they are also valid for anxiety. We evaluated the vulnerability and scar models of low self-esteem and depression, and low self-esteem and anxiety, by meta-analyzing the available longitudinal data (covering 77 studies on depression and 18 studies on anxiety). The mean age of the samples ranged from childhood to old age. In the analyses, we used a random-effects model and examined prospective effects between the variables, controlling for prior levels of the predicted variables. For depression, the findings supported the vulnerability model: The effect of self-esteem on depression (! " #.16) was significantly stronger than the effect of depression on self-esteem (! " #.08). In contrast, the effects between low self-esteem and anxiety were relatively balanced: Self-esteem predicted anxiety with ! " #.10, and anxiety predicted self-esteem with ! " #.08. Moderator analyses were conducted for the effect of low self-esteem on depression; these suggested that the effect is not significantly influenced by gender, age, measures of self-esteem and depression, or time lag between assessments. If future research supports the hypothesized causality of the vulnerability effect of low self-esteem on depression, interventions aimed at increasing self-esteem might be useful in reducing the risk of depression. Keywords: self-esteem, depression, anxiety, longitudinal studies

was to evaluate the vulnerability and scar models of low selfesteem and depression, by meta-analyzing the available longitudinal data. Moreover, we tested whether the vulnerability and scar models (if supported by the data) are specific for depression or whether they are also valid models for anxiety. Finally, we examined moderators that might explain variability in the relation between low self-esteem and depression.

There is an overwhelming amount of self-help literature that explains how people can boost and sustain their self-esteem in order to improve their psychological adjustment. But does selfesteem indeed contribute to psychological health or, to put it differently, does low self-esteem compromise a person’s psychological adjustment? Previous research suggests that self-esteem is linked to indicators of psychological adjustment such as happiness (H. Cheng & Furnham, 2004; Diener & Diener, 1995), high positive affect and low negative affect (Orth, Robins, & Widaman, 2012), and to the absence, or a low number, of psychological symptoms such as depression (Orth, Robins, Trzesniewski, Maes, & Schmitt, 2009; J. E. Roberts & Monroe, 1992) and bulimia (Vohs et al., 2001). However, with respect to many of these variables, the precise nature of their relation with self-esteem has not ultimately been established (Baumeister, Campbell, Krueger, & Vohs, 2003). In the present research, we focus on the relation of self-esteem with two important indicators of low psychological adjustment, specifically depression and anxiety.1 The central goal of this study

Self-Esteem: Concept, Measurement, Function, and Consequences Concept of Self-Esteem The concept of self-esteem has elicited a large body of theoretical accounts and empirical research (see, e.g., Baumeister, 1998; Kernis, 2006; Swann & Bosson, 2010). Historically, the first influential definition of self-esteem dates back to James (1890), who considered self-esteem to be the ratio of success and pretensions in important life domains. Whereas James focused to a stronger degree on the individual processes that form self-esteem,

This article was published Online First June 25, 2012. Julia Friederike Sowislo and Ulrich Orth, Department of Psychology, University of Basel, Basel, Switzerland. This research was supported by Swiss National Science Foundation Grant PP00P1-123370 to Ulrich Orth. Correspondence concerning this article should be addressed to Julia Friederike Sowislo, Department of Psychology, University of Basel, Missionsstrasse 62, 4055 Basel, Switzerland. E-mail: [email protected]

1

Throughout this article, we use the term depression to denote a continuous variable (i.e., individual differences in depressive affect) rather than a clinical category such as major depressive disorder (American Psychiatric Association, 2000). Taxometric analyses suggest that depression is best conceptualized as a continuous construct (Hankin, Fraley, Lahey, & Waldman, 2005; Lewinsohn, Solomon, Seeley, & Zeiss, 2000; Prisciandaro & Roberts, 2005; Ruscio & Ruscio, 2000). 213

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later symbolic interactionism approaches stressed the social influences on self-esteem (Cooley, 1902; Goffman, 1959; Mead, 1934). For instance, in his conception of the looking-glass self, Cooley (1902) hypothesized that self-views are based upon information gathered from explicit or implicit feedback from others. More recent definitions of self-esteem emphasize the fact that selfesteem should be distinguished from other components of the self-concept (such as self-knowledge and self-efficacy), insofar as self-esteem represents the affective, or evaluative, component of the self-concept; it signifies how people feel about themselves (Leary & Baumeister, 2000). This affective self-evaluation is subjective at its core and is not based on specific behaviors (Robins, Hendin, & Trzesniewski, 2001). According to Rosenberg (1989), high self-esteem “expresses the feeling that one is ‘good enough.’ The individual simply feels that he is a person of worth. . . . He does not necessarily consider himself superior to others” (p. 31). Although Baumeister and his colleagues share the view of selfesteem as self-appraisal with an affective component, they expand the definition of self-esteem to include feelings of superiority, arrogance, and pride (e.g., Baumeister, 1998; Baumeister, Smart, & Boden, 1996). In the literature, it is debated whether self-esteem is best conceptualized as a global evaluation of the self (i.e., global selfesteem) or as an evaluation in specific self-relevant domains such as intellectual abilities, physical appearance, and social competence (i.e., domain-specific self-esteem; Swann & Bosson, 2010). One finding that sheds more light on this debate is that both global and domain-specific self-evaluations show predictive ability for important outcomes, as long as these outcomes exhibit the same degree of specificity as the self-evaluation that is used as a predictor (specificity-matching principle; Swann, Chang-Schneider, & McClarty, 2007). More precisely, global self-esteem seems to have predictive ability for outcomes measured at a global level (such as several outcomes bundled together; for an example, see Trzesniewski et al., 2006), whereas domain-specific self-esteem seems to have predictive ability for outcomes measured at a specific level (e.g., academic self-esteem predicts academic outcomes; Marsh, Trautwein, Lu¨dtke, Koller, & Baumert, 2006). With regard to the relation between self-esteem and psychological adjustment, there are three reasons for focusing on global self-esteem rather than domain-specific self-esteem. First, most of the theories linking self-esteem to psychological adjustment address global self-esteem but not domain-specific self-esteem (e.g., Abramson, Seligman, & Teasdale, 1978; Blatt, D’Afflitti, & Quinlan, 1976; G. W. Brown & Harris, 1978). Second and relatedly, most studies in this field have used measures of global self-esteem (for reviews, see Orth, Robins, & Roberts, 2008; Zeigler-Hill, 2010). Third, according to the specificity-matching principle, it seems reasonable to examine global self-esteem in this context, because indicators of psychological adjustment such as depression and anxiety are relatively global constructs that combine a number of cognitive, affective, and somatic symptoms (Swann et al., 2007).

Measurement of Self-Esteem Measures of self-esteem reflect the distinction between global and domain-specific self-evaluations (for a review, see Blascovich & Tomaka, 1991). Frequently used measures of

global self-esteem, all of which are multi-item scales, include the Rosenberg Self-Esteem Scale (RSE; Rosenberg, 1965), the Janis–Field Feelings of Inadequacy Scale (Fleming & Courtney, 1984), the Texas Social Behavior Inventory (Helmreich & Stapp, 1974), and the Self-Liking/Self-Competence Scale (Tafarodi & Swann, 2001). Prominent measures of domain-specific self-esteem are, for example, the Self-Description Questionnaire (Marsh, 1990), the Self-Perception Profile for Children (Harter, 1985), and the Self-Perception Profile for Adolescents (Harter, 1988). Research suggests that these measures generally have good psychometric properties (Blascovich & Tomaka, 1991; Byrne, 1996; Fleming & Courtney, 1984; Gray-Little, Williams, & Hancock, 1997; Marsh, Ellis, Parada, Richards, & Heubeck, 2005; Marsh, Scalas, & Nagengast, 2010). For example, the widely used RSE (Rosenberg, 1965) shows good internal consistency and test–retest reliability (Blascovich & Tomaka, 1991; Robins, Trzesniewski, Tracy, Gosling, & Potter, 2002). Moreover, research supports the construct validity of this measure: First, factor analyses suggest that there is only one substantive factor that explains responses to the RSE (GrayLittle et al., 1997; Marsh et al., 2010; Schmitt & Allik, 2005). Second, the RSE shows good discriminant validity, for instance, regarding measures of life satisfaction, optimism, and academic outcomes (Blascovich & Tomaka, 1991; Lucas, Diener, & Suh, 1996; Robins et al., 2001). Third, the RSE shows convergent validity with other measures of self-esteem (Bosson, Swann, & Pennebaker, 2000). For example, in a multisample study by Zeigler-Hill (2010), correlations between the RSE and the above-mentioned measures of global selfesteem ranged from .63 to .90. All the measures discussed above are based on self-reports; that is, respondents are explicitly asked to reflect on their global or domain-specific self-worth. As self-esteem, by definition, is a subjective construct, it cannot be validly assessed with objective criteria (Baumeister, 1998). However, in the past few decades, researchers have also explored methods other than self-report to assess self-esteem, namely, implicit measures (Bosson et al., 2000; Krizan & Suls, 2009). According to a recent review by Buhrmester, Blanton, and Swann (2011), the most frequently used implicit measures of self-esteem are the Implicit Association Test (Greenwald & Farnham, 2000) and the Name–Letter Test (Greenwald & Farnham, 2000; Nuttin, 1985). However, research suggests that the currently available implicit measures of self-esteem suffer from low reliability and low convergent validity with each other (Bosson et al., 2000; Krizan & Suls, 2009) and with explicit measures of self-esteem (Bosson et al., 2000; Krizan & Suls, 2008). Moreover, implicit measures of self-esteem show weak predictive validity for theoretically relevant criteria such as personality (Krizan & Suls, 2009) and well-being (Buhrmester et al., 2011; Schimmack & Diener, 2003). Buhrmester et al. concluded from their review that the Implicit Association Test and the Name–Letter Test measure generalized implicit affect and implicit egotism, respectively, rather than self-esteem; thus, although implicit measures are a promising avenue for self-esteem measurement, there is not yet sufficiently strong support for their validity. For these reasons, in the present research we restricted our analyses to explicit measures of self-esteem.

LOW SELF-ESTEEM, DEPRESSION, AND ANXIETY

Function of Self-Esteem People tend to have a pervasive motive to increase their selfesteem and to maintain high self-esteem (Sedikides, 1993; Sedikides, Gaertner, & Toguchi, 2003; but see Heine, Lehman, Markus, & Kitayama, 1999). Correspondingly, many psychological theories assume that people are motivated to enhance and maintain their self-esteem without further delineating its functional value (cf. Pyszczynski, Greenberg, Solomon, Arndt, & Schimel, 2004). However, there are a few approaches that seek to explain why self-esteem is important for humans (for an outline of these approaches, see Crocker & Park, 2004; Leary & Baumeister, 2000). First, according to sociometer theory (Leary & Baumeister, 2000; Leary, Tambor, Terdal, & Downs, 1995), humans have a fundamental need for belongingness, because social inclusion has many adaptive benefits (e.g., the possibility of sharing knowledge within social groups; see also Baumeister & Leary, 1995). The theory states that self-esteem is a sociometer that serves as a subjective monitor of the extent to which a person is valued as a member of desirable groups and relationships. Thus, when people perceive their relational value as low, their self-esteem should be equally low, motivating behavior aimed at increasing or restoring social inclusion. Second, according to terror management theory (J. Greenberg, Pyszczynski, & Solomon, 1986; Pyszczynski et al., 2004), people have a central motive to identify with cultural values and groups, because this identification promises either literal immortality (e.g., being part of a religious group that believes in reincarnation) or symbolic immortality (e.g., being part of a cultural group whose existence will endure after one’s own death) and consequently reduces the deeply rooted fear of death. Thus, when people see themselves as living up to these cultural values, their self-esteem should be high, in turn serving as a buffer against the fear of death. Interestingly, the fact that both theories stress the interpersonal component of self-esteem is in line with early psychological accounts of self-views as mentioned above (e.g., Cooley, 1902; Goffman, 1959; Mead, 1934). Moreover, both theories imply an association between self-esteem and psychological adjustment. For terror management theory, this association is more evident, as self-esteem is assumed to buffer against anxiety. From the perspective of sociometer theory, self-esteem is related to psychological adjustment via beneficial aspects of social inclusion. For example, socially excluded individuals may suffer from loneliness and low social support, which increases the risk for depression (e.g., Joiner, 1997; Nolan, Flynn, & Garber, 2003; Stice, Ragan, & Randall, 2004).

Consequences of Self-Esteem A much debated question in the literature is whether self-esteem has an impact on real-life outcomes or whether self-esteem is merely an epiphenomenon of success and well-being in the relationship, work, and health domain (Baumeister et al., 2003; Harter, 1999; Swann et al., 2007). Although research suggests that selfesteem is correlated with many factors in important life domains (e.g., relationship satisfaction, Shackelford, 2001; socioeconomic status, Twenge & Campbell, 2002), this research does not demonstrate that self-esteem actually influences these correlates. The

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available longitudinal studies suggest that self-esteem might have significant positive effects on important life outcomes (e.g., Orth et al., 2012; Trzesniewski et al., 2006; but see Boden, Fergusson, & Horwood, 2008), but further research is needed to test the causality of the hypothesized effects of self-esteem. Moreover, research suggests a causal link between self-esteem and task persistence (Baumeister et al., 2003). More precisely, laboratory experiments have repeatedly shown that high self-esteem facilitates more adaptive persistence behavior: Individuals with high self-esteem persist longer in the face of failure (e.g., Perez, 1973; Shrauger & Sorman, 1977), but whenever persistence is maladaptive (e.g., when confronted with unsolvable tasks), they persist less than individuals with low self-esteem (e.g., Di Paula & Campbell, 2002; McFarlin, 1985). This adaptive self-regulatory behavior might contribute to the link between self-esteem and psychological adjustment (Baumeister et al., 2003). For instance, Shrauger and Sorman (1977) argued that persistence is often needed for the accomplishment of complex tasks and thereby helps to attain long-lasting satisfaction and external rewards. Furthermore, they suggested that task persistence may result in a sense of mastery and control (Shrauger & Sorman, 1977), which is inversely related to phenomena such as depression (Abramson et al., 1978). Importantly, some researchers have proposed that self-esteem may be associated not only with positive outcomes (i.e., the bright side of high self-esteem) but also with negative attributes (i.e., the dark side of high self-esteem). More precisely, Baumeister et al. (1996) suggested that some forms of high self-esteem— specifically, inflated and unstable high self-esteem—may cause interpersonal aggression and violence, because people with overly high self-esteem are more prone to experience ego threats and, consequently, are more strongly motivated to defend their selfesteem by devaluating and attacking people who question their inflated self-views (see also Crocker & Park, 2004; Kernis, Grannemann, & Barclay, 1989). However, other studies suggest that low, but not high, self-esteem predicts antisocial behavior and interpersonal violence, in particular when the confounding effect of narcissism is statistically controlled for (e.g., Donnellan, Trzesniewski, Robins, Moffitt, & Caspi, 2005; Paulhus, Robins, Trzesniewski, & Tracy, 2004). Overall, the available research suggests that high self-esteem may have positive consequences for the well-being and success of the individual and that low selfesteem may be a risk factor for negative outcomes.

Relation Between Low Self-Esteem and Depression Depression is not only an important indicator of low psychological adjustment but also a universal major health concern (Moussavi et al., 2007). According to the World Health Organization (2008), depressive disorders are among the leading contributors to the global burden of disease. For example, major depression affects a wide range of the population (e.g., a lifetime prevalence of 16.6% was estimated in the study of Kessler, Berglund, et al., 2005) and is highly recurrent (e.g., Kessler et al., 2003; Solomon et al., 2000). It is associated with impaired functioning in the relationship (e.g., Davila, Karney, Hall, & Bradbury, 2003; Wade & Pevalin, 2004), work (e.g., Adler et al., 2006; Kessler et al., 2006), and health domain (e.g., Räikkönen, Matthews, & Kuller, 2007; Wulsin & Singal, 2003) and with elevated rates of suicidal behavior (e.g., Berman, 2009; Harris & Barra-

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clough, 1997). As yet, the etiology of depression is not fully understood, but a biopsychosocial model is often assumed to best explain the emergence of depression (Gotlib & Hammen, 2009). Although it is generally undisputed that low self-esteem and depression are related, researchers disagree about the nature of the relation. Importantly, some researchers have argued that selfesteem and depression are essentially one construct and should be conceptualized as opposite poles of a single dimension (i.e., depression being the same as low self-esteem; Watson, Suls, & Haig, 2002). Watson et al. (2002) found strong negative correlations between self-esteem and depression and, on the basis of these results, cautioned against treating self-esteem and depression as distinct constructs (see also Judge, Erez, Bono, & Thoresen, 2002). However, theoretical considerations suggest that it is useful to distinguish between the two constructs. First, self-esteem plays an important role in several classic theories of depression that do not conceptualize low self-esteem as a synonym for depression but as a distinct construct (Abramson et al., 1978; Blatt et al., 1976; G. W. Brown & Harris, 1978); moreover, contemporary models of depression and reviews of the literature also emphasize the role of low self-esteem in the etiology of depressive disorders (Evraire & Dozois, 2011; Hammen, 2005; Joiner, 2000; Morley & Moran, 2011; O’Brien, Bartoletti, & Leitzel, 2006; J. E. Roberts, 2006). Second, although feelings of worthlessness are a symptom of depressive disorders, they are neither a sufficient nor a necessary criterion (American Psychiatric Association, 2000). Third, low self-esteem is not only a symptom of depression but also an associated feature of a wide range of other clinical conditions, such as learning disorders, stuttering, social phobia, and attentiondeficit/hyperactivity disorder (American Psychiatric Association, 2000). Likewise, empirical findings suggest that it is useful to distinguish between self-esteem and depression. First, the correlations reported in previous research range from the #.20s to the #.70s (for a review, see Orth et al., 2008). Thus, the correlation between self-esteem and depression varies widely across studies, and although some studies found strong correlations, the relation is not as strong as would be expected if self-esteem and depression were indicators of a common construct. Second, studies assessing the frequency of individual depressive symptoms have found that feelings of worthlessness are present only in a portion of individuals diagnosed with depression and that feelings of worthlessness do not belong to the most frequent depressive symptoms (Buchwald & Rudick-Davis, 1993; Minor, Champion, & Gotlib, 2005; Spalletta, Troisi, Saracco, Ciani, & Pasini, 1996). In line with the diagnostic criteria for depressive episodes, which do not require that feelings of worthlessness are present (American Psychiatric Association, 2000), these findings suggests that there can be depression without low self-esteem. Third, in two independent samples, Orth et al. (2008) found that a common factor model did not provide a good fit to the data, whereas a two-factor model did (but see Hankin, Lakdawalla, Carter, Abela, & Adams, 2007). Fourth, self-esteem and depression are differentially related to events that happen in people’s lives. For example, whereas there is a robust predictive effect of stressful life events on depression (Hammen, 2005; Kessler, 1997), the available evidence suggests that stressful life events do not predict changes in self-esteem (Orth, Robins, & Meier, 2009; Orth, Trzesniewski, & Robins, 2010). Moreover, whereas there is consistent evidence that depression contributes to

the occurrence of future stressful life events (i.e., stress generation effect; Cole, Nolen-Hoeksema, Girgus, & Paul, 2006; Hammen, 1991), the results of three independent studies suggest that selfesteem does not predict whether stressful life events will occur (Orth, Robins, & Meier, 2009). Finally, some studies have shown that self-esteem and depression are cross-sectionally (McPherson & Lakey, 1993) and prospectively (Orth, Robins, Trzesniewski, et al., 2009) related to each other, even after controlling for prior levels of each construct. It is unlikely that two indicators of a common factor would have replicable cross-lagged effects because their shared variance has been systematically removed in the models. Given these conceptual arguments and empirical results, we believe that it is useful to distinguish between self-esteem and depression. To further illustrate the difference between the constructs, it might be useful to highlight characteristics of a prototypical person with low self-esteem versus a prototypical person with depressive symptoms. According to Rosenberg and Owens (2001; see also Baumeister, 1993), individuals with low self-esteem can be described as follows. For example, they tend to be sensitive to criticism and to focus their attention on how others see them. Moreover, they tend to avoid people by whom they feel their self-esteem might be threatened and to conceal their inner thoughts and feelings from others. Also, as members of a group, these individuals have the tendency to stay at its fringes and not to contribute much to the group discussion. More generally, individuals with low self-esteem tend to avoid risk and try to protect their self-esteem instead of putting their abilities to the test. Furthermore, they may be marked by an attitude of uncertainty, particularly regarding the self and moral convictions. As a consequence, individuals with low self-esteem may lack spontaneity, be shy, and feel lonely and alienated from others. Current depression tends to be clinically heterogeneous and can present with different patterns of symptoms (Kendler, Gardner, & Prescott, 1999). For example, as described in the Diagnostic and Statistical Manual of Mental Disorders (American Psychiatric Association, 2000), individuals with current depression might feel empty and sad, or have the feeling of not being able to take it anymore. Moreover, they tend to lose the ability to derive pleasure from things that used to interest them, and they may feel a lack of drive and energy for work, family, and recreational activities. They tend to have problems concentrating, and others may notice that their movement and speech are slowed down. Individuals with depression might also experience alterations in sleep and appetite. Again, individuals with depression can, but do not have to, experience low selfesteem. Two dominant models on the relation between low self-esteem and depression exist in the literature. Within a diathesis-stress framework, the vulnerability model suggests that negative evaluations of the self (which are conceptually close to low self-esteem; A. T. Beck, Steer, Epstein, & Brown, 1990) constitute a causal risk factor of depression (e.g., A. T. Beck, 1967; Butler, Hokanson, & Flynn, 1994; Metalsky, Joiner, Hardin, & Abramson, 1993; J. E. Roberts & Monroe, 1992; Whisman & Kwon, 1993). For example, according to A. T. Beck’s (1967) cognitive theory of depression, negative beliefs about the self are not just a symptom of depression but a diathesis exerting causal influence in the onset and maintenance of depression. Conversely, the scar model states that low self-esteem is a consequence of depression, rather than a causal


factor, because episodes of depression may leave permanent scars in the self-concept of the individual (cf. Coyne, Gallo, Klinkman, & Calarco, 1998; Coyne & Whiffen, 1995; Rohde, Lewinsohn, & Seeley, 1990; Zeiss & Lewinsohn, 1988). It is important to note that the vulnerability model and the scar model are not mutually exclusive, because both processes (i.e., low self-esteem contributing to depression and depression eroding self-esteem) might operate simultaneously. The extant research has not yet provided unequivocal evidence in favor of the vulnerability or scar model. Although a growing body of longitudinal studies suggests that low self-esteem prospectively predicts depression (e.g., Kernis et al., 1998; Orth, Robins, & Meier, 2009; Orth et al., 2008; Orth, Robins, Trzesniewski, et al., 2009; J. E. Roberts & Monroe, 1992), some studies have failed to confirm this temporal pattern of results; moreover, the results of some studies have found prospective effects in support of the scar model (Burwell & Shirk, 2006; Shahar & Davidson, 2003; Shahar & Henrich, 2010). It is possible not only that these inconsistencies are due to within-study sampling error, but that systematic differences between studies (e.g., age of participants or measures used) account for variability in the findings. In the present research, we therefore test for moderating factors of vulnerability and scar effects, or, in other words, whether the vulnerability and scar effects replicate across sampling and method factors such as gender, age, sample type, time lag between assessments, and measures of self-esteem and depression.

Relation Between Low Self-Esteem and Anxiety An important question is whether the vulnerability model and scar models (if supported by the meta-analytic results) are specific for depression or whether low self-esteem is related in similar ways to affective symptoms other than depressive symptoms. To address this question, we decided to focus on anxiety for several reasons. First, anxiety is an important affective variable (Endler & Kocovski, 2001), because it is the core symptom in the group of anxiety disorders (American Psychiatric Association, 2000) that cause a major burden of disease (P. E. Greenberg et al., 1999). Second, anxiety is associated with depression: Self-report measures of depression and anxiety are strongly correlated in clinical (Mendels, Weinstein, & Cochrane, 1972) and nonclinical samples (Dobson, 1985; Gotlib, 1984; Tanaka-Matsumi & Kameoka, 1986), and depressive and anxiety disorders show a high diagnostic comorbidity (T. A. Brown, Campbell, Lehman, Grisham, & Mancill, 2001; Kessler, Chiu, Demler, & Walters, 2005). Third, although depression and anxiety are related, the constructs are conceptually distinct and can be empirically distinguished (e.g., B. J. Cox, Swinson, Kuch, & Reichman, 1993; Endler, Denisoff, & Rutherford, 1998; McWilliams, Cox, & Enns, 2001; Watson & Clark, 1992). Fourth, many previous studies have examined whether risk factors and correlates of depressive disorders are specific for depression or whether they are also related to anxiety (A. T. Beck, Steer, & Epstein, 1992; R. Beck & Perkins, 2001; R. Beck et al., 2001; Hankin, Abramson, Miller, & Haeffel, 2004; Joiner, 1995; Mor & Winquist, 2002). The relation between self-esteem and anxiety has only rarely been studied (J. E. Roberts, 2006). Cross-sectional studies have reported negative, medium-sized to strong correlations between the constructs (Lee & Hankin, 2009; Riketta, 2004; Watson et al.,

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2002). However, we are not aware of any longitudinal study that has explicitly focused on the prospective relation between selfesteem and anxiety.2 Several theories postulate that self-esteem serves as a buffer against anxiety (see Crocker & Park, 2004). For example, terror management theory (J. Greenberg et al., 1986; Pyszczynski et al., 2004) suggests that self-esteem may predict a decrease in subsequent anxiety because high self-esteem buffers against anxiety elicited by awareness of human mortality. However, the opposite causal direction is also plausible; that is, experiences of intense anxiety might leave scars in the self-concept that persistently threaten and reduce self-esteem.

Theoretical Perspectives on the Relation of Low Self-Esteem With Depression and Anxiety There are two established theories that allow for hypotheses about how depression and anxiety might be differentially related to selfesteem. First, according to the tripartite model (e.g., Clark, Watson, & Mineka, 1994), depression should exhibit a stronger relation to selfesteem than does anxiety. The tripartite model states that depression and anxiety share the feature of high negative affectivity, that is, a stable disposition to experience nonspecific distress and unpleasant mood. However, the model also states that each construct includes a unique component, with low positive affectivity being specific to depression and with heightened autonomic arousal being specific to anxiety. Thus, whereas depression is linked to both positive affect and negative affect, anxiety is linked to negative affect only. Given that self-esteem is correlated with both positive and negative affect at about similar effect size (Aspinwall & Taylor, 1992; Joiner, 1995; Watson et al., 2002), the tripartite model suggests that low self-esteem is more relevant for depression than for anxiety. Second, the cognitive content hypothesis of A. T. Beck et al. (1992), which was derived from Beck’s cognitive theory of depression (A. T. Beck, 1967), posits that depression and anxiety can be distinguished by specific cognitive vulnerabilities. The cognitive content hypothesis states that depressive cognitions reflect negative evaluations of the self, the world, and the future, whereas anxious cognitions reflect the anticipation of a physical or psychological threat. Accordingly, low self-esteem should be a stronger diathesis for depression than for anxiety.

The Present Research The first goal of our study was to evaluate the vulnerability and scar models of low self-esteem and depression by means of metaanalysis. To increase the validity of conclusions, we analyzed effect size measures that were (a) based on longitudinal data and (b) controlled for prior levels of the predicted variable (i.e., controlled for autoregressive effects). Controlling for prior levels of the variables is of crucial importance, because it rules out the possibility that prospective effects are simply due to concurrent relations between the variables and the stability of the predicted variable (Finkel, 1995). Figure 1 provides a generic illustration of 2

Although no previous study has focused explicitly on the prospective relations between low self-esteem and anxiety, some longitudinal studies have included information on zero-order correlations between the constructs, which we used to compute the effect sizes examined in the present meta-analysis (see below).

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Self-Esteem Time 1

Self-Esteem Time 2

Depression Time 1

Depression Time 2

Figure 1. The figure illustrates the coefficients meta-analyzed in the present research, exemplary for the relation between self-esteem and depression (the coefficients for the relation between self-esteem and anxiety were specified accordingly). The relations between the variables at the two measurement occasions are specified as cross-lagged effects and stability effects. The cross-lagged effects indicate the prospective effect of one variable on the other (e.g., effect of self-esteem at Time 1 on depression at Time 2), after controlling for their stabilities across time (e.g., effect of depression at Time 1 on depression at Time 2). In addition to cross-lagged and stability effects, we examined the cross-sectional correlation between the constructs, for Time 1 as an example.

the effect size measures used, exemplary for the relation of selfesteem with depression. First, we examined the stability (i.e., autoregressive) coefficients for each construct (e.g., the effect of depression at Time 1 on depression at Time 2). Second, we examined the cross-lagged coefficients between the constructs, which are controlled for autoregressive effects (e.g., the effect of self-esteem at Time 1 on depression at Time 2, controlling for depression at Time 1). Third, for reasons of completeness, we also examined the concurrent correlation between self-esteem and depression, using the data from Time 1. Given the findings from primary studies discussed above, we hypothesized that self-esteem has a significant negative effect on subsequent depression (corresponding to the vulnerability model) and that the effect of depression on subsequent self-esteem is nonsignificant or, if significant, smaller than the self-esteem effect on depression. In this context, it is important to distinguish between two related approaches, specifically cross-lagged correlation analysis and cross-lagged regression analysis. Cross-lagged correlation analysis has been critiqued because cross-lagged correlations not only reflect the prospective influence of the predictor on the outcome but also depend on the stability of the outcome (Locascio, 1982; Rogosa, 1980). Thus, cross-lagged correlations are confounded by the stability of the variables and may result in misleading interpretations. It is possible that a large cross-lagged correlation simply reflects high stability of the outcome, when the constructs simultaneously show a strong concurrent correlation at Time 1. In contrast, cross-lagged regression analysis statistically controls for the stability of the variables. In other words, whereas cross-lagged correlations inform about whether the predictor at Time 1 is related to the outcome at Time 2, cross-lagged regressions inform about whether the predictor at Time 1 is related to change in the outcome between Time 1 and Time 2 (because the level of the outcome at Time 1 is controlled for; see Finkel, 1995). Therefore, in this research we used the cross-lagged regression approach, which avoids the possible confounding effect of the stability of the variables. The second goal of our study was to examine whether anxiety is related to low self-esteem much as depression is, or whether the

vulnerability and scar models (if supported by the results) are specific for depression. We therefore meta-analyzed the available longitudinal data on self-esteem and anxiety, examining the same effect size measures as for self-esteem and depression. On the basis of the theoretical perspectives discussed above, we expected weaker concurrent and cross-lagged relations of self-esteem with anxiety than with depression. However, we had no hypotheses on the relative strength of the cross-lagged effects between self-esteem and anxiety (i.e., whether the self-esteem effect on anxiety would be stronger than the anxiety effect on self-esteem). The third goal of our study was to test for moderators of the effect sizes. Because the number of studies was low for the relation between self-esteem and anxiety, we focused exclusively on the relation of self-esteem with depression (see the Results section for further information). Although previous studies tested whether the prospective relation between self-esteem and depression holds across gender (Orth et al., 2008; Orth, Robins, Trzesniewski, et al., 2009) and across different age groups from adolescence to old age (Orth et al., 2008; Orth, Robins, Trzesniewski, et al., 2009; Shahar & Henrich, 2010), the meta-analytic approach provides for a more powerful and valid test of the moderating effects of gender and age. We also tested whether the results hold across different types of samples, most importantly representative and clinical samples. Another important moderator might be the temporal design of the primary studies. Methodologists have advised that it is necessary to study different time lags between assessments to gain a “complete understanding of a variable’s effect” (Gollob & Reichardt, 1987, p. 82). More specifically, Collins and Graham (2002) highlighted the importance of studying the influence of time lags on the effect size when longitudinal studies are meta-analyzed. We therefore tested for the moderating impact of time lag: for example, whether a minimum time lag is required to observe any prospective effect between self-esteem and depression or whether effect sizes become smaller when assessed across long time intervals. Finally, we tested whether the effects replicate across different measures of self-esteem and depression or whether the effects are methodological artifacts of specific measures of the constructs. In summary, we tested whether gender, age, sample type, time lag between assessments, and measures of self-esteem and depression moderate the strength of the prospective effect of self-esteem on depression. The effect size coefficients examined in the present research were based on continuous measures of self-esteem, depression, and anxiety. The measures typically employed in this field include multiple indicators and have good psychometric properties, legitimating the statistical approach used in this meta-analysis. With regard to self-esteem, the most frequently used measures and their psychometric properties have already been discussed above. With regard to depression, frequently used measures are the Beck Depression Inventory (BDI; A. T. Beck, Ward, Mendelson, Mock, & Erbaugh, 1961) and the Center for Epidemiologic Studies Depression Scale (CES-D; Gotlib, Lewinsohn, & Seeley, 1995; Radloff, 1977), both of which can be used in nonclinical, subclinical, and clinical populations. The BDI is a self-report instrument comprising 21 items; research suggests that the BDI is a valid and reliable measure of depressive symptoms (A. T. Beck, Steer, & Carbin, 1988; Nezu, Nezu, Friedman, & Lee, 2009; Osman et al., 2004). Similarly, the CES-D—a 20-item self-report measure—is a wellvalidated and reliable measure of depressive symptoms (Eaton,


Smith, Ybarra, Muntaner, & Tien, 2004; Shaver & Brennan, 1991). With regard to anxiety, two frequently used measures are the Beck Anxiety Inventory (BAI; A. T. Beck, Epstein, Brown, & Steer, 1988) and the State–Trait Anxiety Inventory (STAI; Spielberger & Sydeman, 1994). Both measures are multi-item selfreport scales; the available research supports the reliability and validity of the BAI (Fydrich, Dowdall, & Chambless, 1992; Osman, Kopper, Barrios, Osman, & Wade, 1997) and STAI (Barnes, Harp, & Jung, 2002; Spielberger & Sydeman, 1994). This meta-analysis extends the primary studies on self-esteem and depression in several ways. First, prospective relations were estimated with greater power and based on a wide variety of study characteristics. Methodological concerns unique to each primary study were thus reduced, and more valid indications for the direction of the relation between self-esteem and depression were procured. Second, we tested whether the vulnerability and scar models are specific for the relation of self-esteem with depression, or whether similar relations exist with anxiety. As yet, no previous study has tested whether low self-esteem prospectively predicts anxiety or, vice versa, whether anxiety prospectively predicts low self-esteem. Third, the meta-analytic approach enabled us to test for moderators that are difficult to examine in primary studies, such as sample type and time lag between assessments. For example, in this study we tested whether the prospective effects between self-esteem and depression systematically differ when assessed across a few days, weeks, months, or several years.

Method Selection of Studies To search for relevant studies, we used three strategies. First, English-language journal articles, books, book chapters, and dissertations were searched in the databases PsycINFO and Medline for all years covered through July 2011.3 We used the following search terms: depress*, dysphori*, dysthym*, anxi*, fear, phobi*, self-esteem, self-worth, self-liking, self-respect, longitudinal, prospective, and antecedent. The asterisk (i.e., the truncation symbol) allowed for the inclusion of alternate word endings of the search term (e.g., depress* yielded articles containing depression, depressive, etc.). Second, we examined the information provided by relevant review articles (A. T. Beck, 1987; Dance & Kuiper, 1987; J. Greenberg et al., 1992; O’Brien et al., 2006; Pyszczynski et al., 2004; J. E. Roberts, 2006). Third, we examined the reference sections of all articles included in the meta-analysis. The search resulted in 251 potentially relevant journal articles and 44 dissertations. There were no relevant books or book chapters. We decided to include dissertations in our meta-analysis because dissertations are a category of unpublished studies that has important advantages for examining publication bias (Ferguson & Brannick, 2012; McLeod & Weisz, 2004). Dissertations are indexed in databases and, consequently, allow for an exhaustive search and avoidance of selection bias in sampling the relevant studies. In contrast, it is not possible to exhaustively search for other types of unpublished studies such as presentations at conferences and unpublished manuscripts. The empirical findings by Ferguson and Brannick (2012) suggest that the unpublished literature included in meta-analyses is frequently plagued by selection bias because several mechanisms prevent meta-analysts from ob-

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taining a random sample of, for example, unpublished manuscripts. It is important to note that dissertations, although indexed in databases, are generally not subject to publication bias because dissertations are frequently accepted by dissertation committees even if the research reported in the dissertation did not yield significant effects. Moreover, Ferguson and Brannick found that effect sizes that are based on nonindexed unpublished studies are, on average, closer to effect sizes based on published studies than effect sizes based on dissertations. Ferguson and Brannick therefore concluded that dissertations are better suited for examining publication bias than other types of unpublished studies. All studies were then assessed in full text by the first author of this meta-analysis. In addition, a random sample of 79 studies was rated by the second author to obtain estimates of interrater agreement. The interrater agreement on inclusion or exclusion in the meta-analysis was high ($ " .97), and all diverging assessments were discussed until consensus was reached.4 Studies were included in the meta-analysis if the following criteria were fulfilled: (a) self-esteem was assessed with an explicit measure of global self-esteem, (b) depression and/or anxiety was assessed with continuous measures of the constructs, (c) the study used a longitudinal study design, (d) at least one of the constructs (i.e., self-esteem or depression/anxiety) was assessed on at least two measurement occasions, and (e) enough information was given to compute effect sizes. We included samples of all age groups in the meta-analysis, covering the full life span from childhood to old age. If a sample was analyzed by more than one study, only one study was included in the meta-analysis to ensure independence of effect sizes. In these cases, we included the study that provided the most comprehensive coding information and excluded the other studies. Finally, studies were excluded if inconsistent information for the computation of effect sizes was given. This procedure left 53 journal articles and seven dissertations for analysis. The articles of Chen (1995); Colarossi and Eccles (2003); S. J. Cox et al. (2006); Le, Tv, and Taylor (2007); Orth et al. (2008); Ostrowsky (2007); Rueger (2011); Schroevers, Ranchor, and Sanderman (2003); and Steinberg, Karpinski, and Alloy (2007) provided two relevant samples each; the article of Orth, Robins, Trzesniewski, et al. (2009) provided 12 relevant samples; thus our data set comprised 80 samples. Of these, 77 samples provided information on the prospective relations between selfesteem and depression, and 18 samples provided information on the prospective relations between self-esteem and anxiety.

Coding of Studies We coded the following data: sample size, country of origin, mean age of participants, proportion of female participants, sample type (i.e., representative, clinical, college students, or convenience sample other than college students), time lag between assessments, 3

Although our search covered the entire time span indexed in these databases until July 2011, the earliest eligible study was published in 1984 (see Results section). 4 The qualifications of the coders were as follows: The first author had a master’s degree in psychology, and the second author had a PhD in psychology.

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measure used to assess self-esteem, measure used to assess depression and anxiety, and effect sizes. A few studies did not report the exact mean age or the exact time lag between assessments. Yet, when valid indicators were given in the studies, we used this information to estimate the variables. For example, if a study that examined a sample of undergraduate students did not report the mean age, we estimated it to be 20 years (as done by, e.g., Starr & Davila, 2008). To take another example, if a study reported that the first assessment was conducted in the third trimester of pregnancy, we estimated Time 1 as 2 months before delivery. In the meta-analytic data set, only few data were missing on moderator variables (i.e., 2.6%). We therefore used the complete case analysis method (i.e., listwise deletion) to deal with missing data in the moderator analyses (Pigott, 2009).5 In some cases, effect sizes were directly reported in the article (i.e., the standardized regression coefficients as shown in Figure 1). However, in most cases we computed effect sizes using the zero-order correlations between the variables (e.g., correlations between self-esteem assessed at Time 1, selfesteem assessed at Time 2, depression assessed at Time 1, and depression assessed at Time 2). For the computation, we used the following equation (Cohen, Cohen, West, & Aiken, 2003, p. 68), which is applicable when a criterion variable (Y) is influenced by two predictors (X1, X2): ! Y1.2 !

rY1 " rY2 r12 . 1 " r212

(1)

Here !Y1.2 is the standardized regression coefficient of X1 predicting Y, controlling for the effect of X2 (e.g., the effect of self-esteem at Time 1 on depression at Time 2, controlling for depression at Time 1); rY1 and rY2 are the zero-order correlations between each predictor (X1, X2; e.g., self-esteem at Time 1, depression at Time 1) and the criterion (Y; e.g., depression at Time 2); and r12 is the correlation between the two predictors (X1 and X2; e.g., the crosssectional correlation of self-esteem at Time 1 and depression at Time 1). For studies that provided more than one effect size for one of the coefficients examined (e.g., because more than one measure of self-esteem was used), we averaged the correlations and standardized regression coefficients, respectively, using Fisher’s Zr transformations. All articles were coded by the first author of this meta-analysis. In addition, a random sample of 33 studies was coded by the second author to obtain estimates of interrater agreement. The interrater agreement was high ($ # .95 for categorical variables and r # .99 for continuous variables). All diverging assessments were discussed until consensus was reached.

Meta-Analytic Procedure We made all computations with effect sizes using Fisher’s Zr transformations and using study weights with % " n # 3 (see Lipsey & Wilson, 2001). For the computations, we used SPSS and the SPSS macros written by Daniel Wilson (see Lipsey & Wilson, 2001, Appendix D). We conducted the following preliminary analyses. First, we searched for statistical outliers on effect size variables. Second, we determined whether there was evidence of publication bias, that is, whether studies with nonsignificant results had a lower probability

of being published. We hypothesized that publication bias would not be an issue in this research, because the majority of studies included in the meta-analysis did not focus specifically on the relations of low self-esteem with depression and anxiety but reported their intercorrelations together with intercorrelations among a larger set of constructs. Nevertheless, we tested for publication bias, using two methods. First, if publication bias exists, studies resulting in low effect sizes should have a low probability of being published if the sample size is small (because of a low probability of significant findings). In contrast, studies resulting in large effect sizes have a high probability of being published even if the sample size is small (because of a high probability of significant findings). The relationship of sample size and effect size can be examined visually with a funnel graph (cf. Sutton, 2009). If the funnel graph does not show a symmetrical shape, and if studies with small sample size show a bias toward larger effect sizes, there is evidence for publication bias. Second, we tested whether effect sizes based on dissertations differed significantly from effect sizes based on published studies. In the effect size analyses, we used a random-effects model, following the recommendations by Field and Gillett (2010) and Raudenbush (2009). We first computed weighted mean effect sizes and tested for homogeneity of effect size distributions. Then we examined moderators of the effect sizes using multiple regression analysis and analysis of variance. In multiple regression analysis, only continuous or dichotomous predictors can be used; therefore, we dichotomized the categorical variable sample type into a variable contrasting representative versus nonrepresentative samples. We decided to focus on this contrast because representative samples provide more valid results compared with nonrepresentative samples. Finally, using analysis of variance, we investigated the influence of the variable sample type in more detail using all the original categories and also examined the moderating effects of the self-esteem and depression measures used.

Results Description of Studies The 80 studies included in the meta-analysis were published between 1984 and 2010, with the median in 2004. Sample sizes varied between 44 and 6,813 (M " 447.5, SD " 1,050.2, Mdn " 214.5). The average proportion of female participants was 64% (range: 0%–100%). The average mean age of the participants at the time of the first assessment was 27.7 years (SD " 17.4; range: 8.2–79.3). The time lag between assessments varied between 1 week and 13 years (M " 1.23 years, SD " 1.81, Mdn " 0.75). Forty-nine studies used convenience samples other than college students, 19 used college student samples, nine used representative samples, and three used clinical samples. Sixty-two studies were conducted in the United States, six in Germany, three in the United 5 We tested whether the results of the multiple regression analysis used in the moderator analyses were altered when we used a different method to deal with missing data (i.e., the expectation–maximization algorithm; Dempster, Laird, & Rubin, 1977). The results were very similar, and all the significant effects remained significant and the nonsignificant effects remained nonsignificant.


Kingdom, two in Canada, two in Israel, and one each in Australia, China, Korea, the Netherlands, and Sweden. Tables 1 and 2 show the basic sample characteristics and effect sizes for each study, separated for depression and anxiety. A wide variety of measures were used in the studies. Selfesteem (k " 80) was assessed by the RSE (Rosenberg, 1965) in 61 studies, by the global self-worth subscale of the Self-Perception Profile for Children (Harter, 1985) or the Self-Perception Profile for Adolescents (Harter, 1988) in 11 studies, and by a range of other measures in eight studies. Depression (k " 77) was assessed by the CES-D (Radloff, 1977) or its child version (Weissman, Orvaschel, & Padian, 1980) in 30 studies, by the BDI (A. T. Beck et al., 1961) in 20 studies, by the Children’s Depression Inventory (Kovacs, 1985) in eight studies, and by other measures in 19 studies. Anxiety (k " 18) was assessed by the BAI (A. T. Beck, Epstein, et al., 1988) in five studies, by the STAI or its child version (Spielberger & Sydeman, 1994) in three studies, by the anxiety subscale of the Mood and Anxiety Symptom Questionnaire (Watson et al., 1995) in two studies, by the anxiety subscale of the Hospital Anxiety and Depression Scale (Zigmond & Snaith, 1983) in two studies, and by other measures in six studies.

Preliminary Analyses First, the data revealed that there were no statistical outliers on effect size variables. We therefore used the complete data set for the subsequent analyses. Second, the data showed evidence against any publication bias. For each effect size, the funnel graphs indicated that studies with small sample sizes were not biased toward larger effect sizes (see Figures 2 and 3). The distributions of effect sizes exhibited a symmetrical shape typical of nonbiased meta-analytic data sets. Moreover, we tested whether effect sizes based on dissertations differed significantly from effect sizes based on published studies. These tests were possible only for effect sizes related to depression, but not anxiety, because only one dissertation related to anxiety was included in the data set. The tests showed that there were no significant differences between dissertations and published studies (all ps & .05).

Effect Size Analyses We computed weighted mean effect sizes for the relation between self-esteem and depression and for the relation between self-esteem and anxiety. More specifically, we examined the crosssectional correlation between the constructs (for Time 1 as an example), the stability coefficients of the constructs, and the crosslagged effects between the constructs (cf. Figure 1). Tables 3 and 4 show the results for depression and anxiety, respectively. As reported in the tables, homogeneity statistics were significant for most effect sizes, except for the cross-lagged effects between self-esteem and anxiety. A significant homogeneity figure indicates that the variance of the corresponding effect size must be attributed not only to within-study sampling error but also to between-study sampling error. The results for depression supported the vulnerability model of low self-esteem (see Table 3). The mean cross-lagged effect of self-esteem on depression was #.16 (p ' .05) and was larger than the mean cross-lagged effect of depression on self-esteem, which

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was #.08 (p ' .05). No formal significance test for the difference between cross-lagged effects is available; however, the confidence intervals, which showed no overlap, clearly suggest that the two cross-lagged effects differ from each other.6 Moreover, the mean stability coefficient of self-esteem was larger than the mean stability coefficient of depression. Because the cross-lagged effect of self-esteem on depression was based on a much larger number of studies (k " 77) than the cross-lagged effect of depression on self-esteem (k " 42), we repeated the computation of the selfesteem effect on depression using the same set of studies that was used for the computation of the depression effect on self-esteem. However, the self-esteem effect on depression was virtually unaltered (with a weighted mean effect size at #.16). Similarly, the stability of depression was virtually unaltered when computed with the smaller set of studies (with a weighted mean effect size at .51). The results for anxiety suggested a symmetric reciprocal relation between self-esteem and anxiety (see Table 4). The crosslagged effects between the constructs were significant and of similar size (#.10 and #.08; both ps ' .05). As stated above, no formal significance test is available for the difference between the coefficients; however, the confidence intervals overlapped widely, which suggests that the coefficients do not significantly differ. Again, we tested whether the results for the self-esteem effect on anxiety differed when computed with the smaller set of studies (k " 10) used to compute the anxiety effect on self-esteem. However, the self-esteem effect on anxiety was virtually unaltered (with a weighted mean effect size at #.10). Similarly, the stability of anxiety was virtually unaltered when computed with the smaller set of studies (with a weighted mean effect size at .47).

Moderator Analyses The preceding analyses revealed heterogeneity of the distributions of most effect sizes; therefore, we investigated whether moderator variables explain variation of effect sizes. In the moderator analyses, we focused on the effect of self-esteem on depression for several reasons. First, the effect emerged as the strongest cross-lagged effect in the effect size analyses and is of central importance for the vulnerability model. Second, the number of studies on which the other cross-lagged effects were based was relatively low (i.e., 42, 18, and 10 studies), which limited the statistical power of moderator analysis. We first examined the simple correlations between the effect size and the moderator variables (see Table 5). The results showed 6 No formal significance test for the difference between the cross-lagged regression effects is available because the coefficients (a) do not involve the same set of variables and (b) are based on a partially, but not fully, overlapping set of studies. In this situation, none of the tests discussed in, for example, Clogg, Petkova, and Haritou (1995); Cohen et al. (2003); and Raghunathan, Rosenthal, and Rubin (1996) is applicable. We therefore used the confidence intervals as an approximate means of comparing the cross-lagged effects. For comparison purposes (although formally not admissible), we also computed unpaired t tests, which bolstered our conclusions. For the cross-lagged effects between self-esteem and depression, the test was significant, with t(117) " 3.78, p ' .001, suggesting that the coefficients differed significantly. For the cross-lagged effects between self-esteem and anxiety, the test was nonsignificant, with t(26) " 1.18, p " .250, suggesting that the coefficients did not significantly differ.

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Table 1 Longitudinal Studies of the Relation Between Self-Esteem (SE) and Depression (D) Sample characteristics

Study Abela & Payne (2003) Bohon et al. (2008) Borelli & Prinstein (2006) Burwell & Shirk (2006) Butler et al. (1994), Part 2 Cambron et al. (2010), Study 3 Cast & Burke (2002) Chen (1995), female subsample Chen (1995), male subsample S. K. Cheng & Lam (1997) Cikara & Girus (2010) Colarossi & Eccles (2003), female group Colarossi & Eccles (2003), male group Conley et al. (2001) Fernandez et al. (1998) Flynn (2006) Fontaine & Jones (1997) Hobfoll & Leiberman (1987) Hobfoll & Walfisch (1984) Hubbs-Tait et al. (1994), group of mothers Jalajas (1994) Joiner (1995), group of targets Joiner et al. (1999) Joiner et al. (2000) Kakihara et al. (2010) Katz et al. (1998), female group Kernis et al. (1998) Kim et al. (2008) Klima & Repetti (2008) Kling et al. (2003) Le et al. (2007), female group Le et al. (2007), male group Lee & Hankin (2009) Lewinsohn et al. (1988) McCarty et al. (2007) Mindes et al. (2003) Ohannessian et al. (1994) Orth et al. (2008), Study 1 Orth et al. (2008), Study 2 Orth, Robins, Trzesniewski, et al. (2009), Study 1, age 18–29 Orth, Robins, Trzesniewski, et al. (2009), Study 1, age 30–39 Orth, Robins, Trzesniewski, et al. (2009), Study 1, age 40–49 Orth, Robins, Trzesniewski, et al. (2009), Study 1, age 50–59 Orth, Robins, Trzesniewski, et al. (2009), Study 1, age 60–69 Orth, Robins, Trzesniewski, et al. (2009), Study 1, age 70( Orth, Robins, Trzesniewski, et al. (2009), Study 2, age 18–29

N

Proportion of female participants

Effect sizes

Mean age (years)

Time lag (years)

Sample type

rSE,D

SE3Da

Convenience Convenience Convenience Convenience College students College students Convenience Convenience Convenience Convenience College students

#.50 #.34 #.59 #.45 #.36 #.61 #.31 #.59 #.50 #.64 #.65

#.20 #.09 #.06 #.30 #.11 #.21 #.04 #.14 #.10 #.26 #.26

D3SEa

SE3SEa

D3Da

#.06

.27

#.21

.48

#.16 #.20 #.09 #.11

.69 .57 .72 .63

#.19

.74

.58 .61 .67 .43 .36 .64 .46 .55 .62 .49 .52

314 496 478 110 73 230 574 374 374 286 67

.45 1.00 .51 .58 .77 .68 .50 1.00 .00 .27 .63

11.2 16.5 12.7 13.6 20.0 21.3

15.8 20.9

0.11 1.00 0.92 0.58 0.42 0.04 1.00 1.00 1.00 0.25 0.08

125

1.00

17.0

1.00

Convenience

#.52

#.21

.19

.81

.46

92 147 729 160 45 99 55

.00

17.0 8.2

1.00 0.06 2.00

#.52 #.45 #.45 #.69 #.64 #.47 #.23

#.19 #.19 #.14 #.15 #.39 #.15 #.10

.00

.60

0.21 0.25 0.25

Convenience Convenience Convenience College students Convenience Convenience Convenience

#.21

.48

.44 .59 .34 .57 .17 .39 .49

Convenience College students College students College students College students Convenience Convenience College students Clinical Convenience Convenience Representative Representative Convenience Convenience Clinical Convenience Convenience Representative College students

#.68 #.65 #.62 #.58 #.56 #.66 #.67 #.50 #.76 #.67 #.33 #.46 #.41 #.60 #.51 #.60 #.57 #.31 #.34 #.60

#.59 #.23 #.11 #.17 #.13 #.15 #.24 #.04 #.36 #.14 #.20 #.06 #.08 #.03 #.14 #.21 .05 #.13 #.09 #.20

#.04 #.14 #.02 #.02 #.03 #.13

.70 .72 .58 .61 .58 .54

#.33 #.10

.33 .71

#.07 #.19

.52 .61

#.04 .00

.51 .80

#.01 .55 .40 .39 .45 .50 .39 .72 .26 .51 .42 .60 .59 .68 .35 .46 .61 .29 .51 .35

.52 .73 1.00 1.00 1.00

19.5 31.0 28.0 38.2

44 205 100 177 143 1,022 134 98 60 226 285 6,813 6,504 350 562 331 67 235 2,403 359

1.00 .38 .61 .63 .59 .47 1.00 .88 1.00 .48 1.00 1.00 .00 .57

17.7 23.4 20.0 20.0 20.0 15.3 19.0 20.0 31.8 9.5 69.5 16.0 16.0 14.5

.47 1.00 .56 .50 .59

12.0 33.0 12.2 15.5 18.3

3.42 0.25 0.06 0.06 0.06 1.00 0.11 0.08 0.25 2.00 1.17 1.00 1.00 0.42 0.69 1.00 0.75 1.00 2.00 1.00

95

.58

22.0

3.00

Convenience

#.82

#.44

.18

.89

.32

673

.57

35.5

3.00

Convenience

#.70

#.22

.01

.80

.45

146

.45

41.9

3.00

Convenience

#.72

#.23

#.05

.84

.62

270

.70

56.2

3.00

Convenience

#.59

#.25

.02

.80

.37

299

.49

63.4

3.00

Convenience

#.71

#.23

.02

.90

.55

202

.58

79.3

3.00

Convenience

#.44

#.29

.11

.89

.29

371

.51

24.5

2.00

Representative

#.77

#.10

#.09

.64

.64

223


Table 1 (continued) Sample characteristics

Study Orth, Robins, Trzesniewski, et al. (2009), Study 2, age 30–39 Orth, Robins, Trzesniewski, et al. (2009), Study 2, age 40–49 Orth, Robins, Trzesniewski, et al. (2009), Study 2, age 50–59 Orth, Robins, Trzesniewski, et al. (2009), Study 2, age 60–69 Orth, Robins, Trzesniewski, et al. (2009), Study 2, age 70( Ostrowsky (2007), female subsample Ostrowsky (2007), male subsample Prinstein & La Greca (2002) Procopio et al. (2006) Puckett (2010) Ralph & Mineka (1998) Ritter et al. (2000) J. E. Roberts & Kassel (1997) J. E. Roberts & Monroe (1992) Robinson et al. (1995) Rosario et al. (2005) Rueger (2011), female subsample Rueger (2011), male subsample Schafer et al. (1998) Schroevers et al. (2003), control group Schroevers et al. (2003), study group Settles et al. (2009) Shahar & Davidson (2003) Southall & Roberts (2002) Steinberg et al. (2007), high-risk group Steinberg et al. (2007), low-risk group Terry et al. (1996) Thoms (2006) Vohs et al. (2001) Whisman & Kwon (1993) Yang (2006)

Effect sizes

Proportion of female participants

Mean age (years)

Time lag (years)

437

.49

34.4

476

.45

545

Sample type

rSE,D

SE3Da

D3SEa

SE3SEa

D3Da

2.00

Representative

#.84

#.23

#.06

.81

.62

44.5

2.00

Representative

#.75

#.17

.05

.92

.63

.34

54.9

2.00

Representative

#.78

#.16

#.01

.80

.68

434

.29

64.0

2.00

Representative

#.62

#.22

#.04

.82

.61

216

.34

74.0

2.00

Representative

#.72

#.02

.03

.96

.83

253

1.00

14.0

1.00

Convenience

#.46

.01

#.11

.44

.64

675 246 150 345 141 191 213 192 381 156 256 241 98

.00 .60 1.00 .58 .54 1.00 .63 .64 .58 .49 1.00 .00 .50

14.0 16.8 45.2 14.0 20.0 24.5 20.3 18.7 12.0 18.3 13.2 13.2 56.0

1.00 6.00 2.50 0.50 0.02 0.63 0.17 0.08 0.38 0.50 0.33 0.33 13.00

Convenience Convenience Convenience Convenience College students Convenience College students College students Convenience Convenience Convenience Convenience Convenience

#.43 #.54 #.63 #.75 #.52 #.49 #.58 #.51 #.71 #.62 #.67 #.64 #.38

#.14 #.15 #.28 #.07 #.09 #.07 #.09 #.21 #.22 #.52 #.03 #.19 #.38

#.13 #.05 #.36

.55 .29 .37

#.13

.65

.49 .20 .48 .63 .49 .46 .52 .65 .50 .02 .57 .41 .38

225

.70

57.0

1.00

Convenience

#.33

#.13

403 128 260 115

.73 1.00 .43 .50

58.0 24.2 42.2 16.5

1.00 2.00 0.33 0.04

Convenience Convenience Clinical Convenience

#.37 #.49 #.69 #.69

#.10 #.14 .01 #.21

98

.61

20.0

0.34

College students

#.41

#.01

.55

83 185 91 70 80 1,149

.61 1.00 .81 1.00 .66 .62

20.0 27.5 21.2 20.0 18.9 71.0

0.34 0.25 0.13 0.10 0.25 6.00

College students Convenience College students College students College students Convenience

#.52 #.37 #.55 #.45 #.77 #.36

.07 #.14 #.19 .00 .01 #.19

.66 .45 .41 .58 .69 .38

N

.51 .01 #.20

#.30

.50 .62

.52

.64 .33 .70 .47

Note. rSE,D " correlation between the constructs at Time 1. Standardized regression coefficient.

a

that proportion of female participants, mean age of participants, and sample type did not significantly correlate with the effect size. Only time lag showed a significant zero-order correlation with the effect size (r " #.25, p " .03). To control for multicollinearity of the predictors, we computed a multiple regression analysis with these variables as predictors of the effect size (see Table 5). The variance explained was relatively small, and only one predictor (i.e., sample type) yielded a significant regression weight, indicating that the self-esteem effect on depression was smaller in representative than in nonrepresentative samples.7 In a second multiple regression analysis, we also tested whether time lag showed a significant quadratic relation to effect size by additionally including the square of the variable (the variable time lag was

centered for the analysis). However, neither the linear nor the quadratic term was significant. Figure 4 further illustrates the relation between time lag and effect size, showing that no linear or 7

In analyses of the moderating effect of time lag, we excluded one statistical outlier. In the study by Schafer, Wickrama, and Keith (1998), the time lag between assessments was 13 years, which was 6.5 standard deviations longer than the average time lag. When this study was included in the multiple regression analysis, time lag significantly predicted the effect size (! " #.26, p " .02), indicating that with increasing time lag the effect size became more negative (i.e., the absolute value of the effect became larger). However, because this analysis was strongly influenced by the statistical outlier (Cohen et al., 2003), we decided to report the analyses without the outlier.

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SOWISLO AND ORTH

Table 2 Longitudinal Studies of the Relation Between Self-Esteem (SE) and Anxiety (ANX) Sample characteristics

Effect sizes

Proportion of female Mean age Time lag participants (years) (years)

Study

N

Borelli & Prinstein (2006) Cast & Burke (2002) S. J. Cox et al. (2006), IVF group S. J. Cox et al. (2006), control group Ewen (2002) Hobfoll & Walfisch (1984) Jalajas (1994) Joiner (1995) Joiner et al. (1999) Kim et al. (2008) Lee & Hankin (2009) McCarty et al. (2007) Ohannessian et al. (1994) Prinstein & La Greca (2002) Procopio et al. (2006) Ralph & Mineka (1998) Rosario et al. (2005) Vohs et al. (2001)

478 574

.51 .50

12.7

70

1.00

111 115

Sample type

rSE,ANX SE3ANXa ANX3SEa SE3SEa ANX3ANXa

0.92 1.00

Convenience Convenience

#.46 #.18

#.02 #.11

#.14

.60

.61 .48

33.6

0.19

Convenience

#.67

#.06

#.01

.80

.70

1.00 .81

29.3 31.3

0.19 0.67

Convenience College students

#.40 #.66

.02 #.24

#.22 #.15

.63 .65

.55 .42

55 205 100 177 60 350 331 235

1.00 .38 .61 .63 1.00 .57 .47 .56

38.2 23.4 20.0 20.0 31.8 14.5 12.0 12.2

0.25 0.25 0.06 0.06 0.25 0.42 1.00 1.00

Convenience College students College students College students Clinical Convenience Clinical Convenience

#.05 #.38 #.31 #.42 #.38 #.53 #.37 #.41

#.22 #.15 #.15 #.07 #.31 #.21 #.04 #.07

#.07 #.02 .11

.78 .58 .67

#.08

.53

246 150 141 156 70

.60 1.00 .54 .49 1.00

10.8 45.2 20.0 18.3 20.0

6.00 2.50 0.02 0.50 0.10

Convenience Convenience College students Convenience College students

#.31 #.33 #.30 #.35 #.45

#.05 #.17 .01 #.10 #.13

.00 #.14

.31 .50

.30 .46 .32 .41 .61 .45 .53 .25 .31 .48 .49 .50 .51

Note. rSE,ANX " correlation between the constructs at Time 1; IVF " in vitro fertilization. Standardized regression coefficient.

a

curvilinear relation is discernible in the data. Thus, given that in the multiple regression analysis only one significant predictor of the effect size was identified, the important conclusion in this context is that the vulnerability effect of low self-esteem on depression replicated across samples with different gender and age compositions and across different time lags between assessments. Because sample type was used only as a dichotomous variable in the preceding analysis, we computed an analysis of variance to investigate the importance of sample type in more detail (see Table 6). Although the effect size differed for representative and nonrepresentative samples in the analysis reported above, the results of the analysis of variance indicated that the self-esteem effect on depression was present in all sample types (ranging from #.12 to #.19; all ps ' .05). Finally, we examined whether the effect size differed across measures of self-esteem and depression, using analyses of variance. Table 7 shows that the effect size was very similar across self-esteem measures (ranging from #.15 to #.18) and that there was no significant heterogeneity between measures (Qbetween " 0.39, p " .821). Likewise, Table 8 shows that the effect size was relatively similar across depression measures (ranging from #.14 to #.20), and again that the heterogeneity between measures was nonsignificant (Qbetween " 2.30, p " .531). Together, the findings of the moderator analyses suggest that low self-esteem serves as a general, stable risk factor for depression: the effect holds for samples with different gender and age compositions, for different time lags, for different measures of self-esteem and depression, and for representative, clinical, and convenience samples.

Discussion We investigated the prospective reciprocal relations of selfesteem with depression and anxiety by meta-analyzing 77 longitudinal studies providing information on the relation between self-esteem and depression and 18 longitudinal studies providing information on the relation between self-esteem and anxiety. The studies included differed substantially with respect to sample characteristics such as sample size, country of origin, sample type, mean age of participants, and proportion of female participants. Moreover, the studies differed significantly with respect to methodological characteristics, such as the time lag between assessments, and used a wide variety of measures to assess self-esteem, depression, and anxiety. The heterogeneity of the studies strengthens the generalizability of the findings: First, the analyses yielded consistent support for the vulnerability model of low self-esteem and depression (i.e., low self-esteem contributes to depression) and only weak support for the scar model (i.e., depression erodes self-esteem). Second, the findings indicate that the relation between low self-esteem and anxiety is more symmetric, with small, but significant, prospective effects in both directions. Third, moderator analyses of the vulnerability effect of low self-esteem on depression suggested that this effect is not significantly influenced by gender and age composition of the sample, measures of selfesteem and depression, or the time lag between assessments. Moreover, although the vulnerability effect differed significantly between representative, clinical, and convenience samples, the effect was present in all types of samples examined in this research.

225


A

7000

Sample size

6000 5000 4000 3000 2000 1000 0 -1.00

-.80

-.60

-.40

-.20

.00

Correlation between self-esteem and depression at Time 1

C 7000

7000

6000

6000

5000

5000

Sample size

Sample size

B

4000 3000

4000 3000

2000

2000

1000

1000

0 -.60

-.40

-.20

.00

.20

0 -.60

.40

Effect of self-esteem on depression

-.20

.00

.20

.40

Effect of depression on self-esteem

E

D 7000

7000

6000

6000

5000

5000

Sample size

Sample size

-.40

4000 3000

4000 3000

2000

2000

1000

1000

0 -.20

.00

.20

.40

.60

.80

1.00

Stability effect of depression

0 -.20

.00

.20

.40

.60

.80

1.00

Stability effect of self-esteem

Figure 2. Funnel graphs for the effect sizes of the relation between self-esteem and depression. The graphs display the relation between the effect size and sample size of the studies. The dashed lines show the weighted mean effect sizes.

Implications of the Findings The present results suggest that the prospective relation between low self-esteem and depression is best described by the vulnerability model, whereas the prospective relation between low self-esteem and anxiety is best described as a symmetric

reciprocal relation. Consequently, it would be interesting to gain further insight into (a) the mechanisms that account for the vulnerability effect of low self-esteem on depression, (b) the mechanisms that account for the small but significant scar effect of depression on self-esteem, and (c) the mechanisms that

226

SOWISLO AND ORTH

A

600

Sample size

500 400 300 200 100 0 -1.00

-.80

-.60

-.40

-.20

.00

Correlation between self-esteem and anxiety at Time 1

C 600

600

500

500

Sample size

Sample size

B

400 300

400 300

200

200

100

100

0 -.40

-.20

.00

.20

0 -.40

.40

-.20

D

.20

.40

E 600

600

500

500

Sample size

Sample size

.00

Effect of anxiety on self-esteem

Effect of self-esteem on anxiety

400 300

400 300

200

200

100

100

0 .00

.20

.40

.60

.80

1.00

Stability effect of anxiety

0 .00

.20

.40

.60

.80

1.00

Stability effect of self-esteem

Figure 3. Funnel graphs for the effect sizes of the relation between self-esteem and anxiety. The graphs display the relation between the effect size and sample size of the studies. The dashed lines show the weighted mean effect size.

account for the affective specificity of the results. Knowledge about mediating processes is of crucial importance because it provides for possible starting points for interventions, for instance, interventions aimed at preventing or reducing depression.

The vulnerability effect of low self-esteem on depression might operate through both interpersonal and intrapersonal psychological pathways. One interpersonal pathway is that some individuals with low self-esteem might excessively seek reassurance from friends and relationship partners, which might lead to social disruptions

227


Table 3 Summary of Effect Sizes for Relation Between Self-Esteem (SE) and Depression (D) Variable

k

N

Weighted mean effect size

95% CI

Homogeneity (Q)

rSE,D SE3Da D3SEa SE3SEa D3Da

77 77 42 42 77

35,501 35,501 14,049 14,049 35,501

#.57! #.16! #.08! .69! .51!

[#.60, #.54] [#.18, #.14] [#.11, #.05] [.63, .74] [.48, .54]

1338.79! 226.63! 115.05! 1692.57! 758.05!

Note. Computations were made with a random-effects model. k " number of studies; N " total number of participants in the k samples; rSE,D " correlation between the constructs at Time 1; CI " confidence interval. a Standardized regression coefficient. ! p ' .05.

that in turn foster depressive symptoms (Joiner, Alfano, & Metalsky, 1992; Potthoff, Holahan, & Joiner, 1995). A second interpersonal pathway is that individuals with low self-esteem seek negative feedback from their relationship partners to verify their negative self-concept. Negative feedback seeking might lead to rejection by close others and might undermine social support, which in turn increases the risk of depression (Giesler, Josephs, & Swann, 1996; Joiner, Katz, & Lew, 1997; Swann, Wenzlaff, & Tafarodi, 1992). An intrapersonal pathway explaining how low self-esteem contributes to depression might operate through selffocused attention (Mor & Winquist, 2002). Individuals with low self-esteem are prone to ruminating about negative aspects of the self, which in turn increases depression (e.g., Nolen-Hoeksema, 2000; Spasojevic´ & Alloy, 2001). Overall, reassurance seeking, negative feedback seeking, and rumination are theoretically linked to low self-esteem and depression, and there is some evidence that self-esteem contributes to these processes (Evraire & Dozois, 2011; Joiner, Katz, & Lew, 1999; Kuster, Orth, & Meier, 2012), making it less likely that reassurance seeking, negative feedback seeking, and rumination are third variables that cause the relation between low self-esteem and depression. However, the hypothesized mediational pathways should be tested directly. As yet, only one study has identified a mediator of the vulnerability effect of low self-esteem on depression. Using longitudinal mediation analysis (Cole & Maxwell, 2003), Kuster et al. (2012) found that rumination partially mediated the prospective effect of low selfesteem on depression across several waves of data. Future research

should continue to test for possible mediators of the vulnerability effect, such as reassurance seeking or negative feedback seeking. Similarly, the small but significant scar effect of depression on self-esteem might unfold through interpersonal and intrapersonal psychological pathways. One interpersonal pathway is that depressive episodes may cause damage to important sources of selfesteem such as close relationships or social networks. A second interpersonal pathway is that depression might change how the individual is perceived by others; these representations may be relatively persistent and may cause the individual to be treated by others with low regard or in ways that minimize the individual’s self-esteem, even if the depression has already remitted (Joiner, 2000). A possible intrapersonal pathway is that the experience of depression might influence self-esteem by persistently altering the way in which individuals process self-relevant information; for example, the chronic negative mood associated with depression may lead the individual to selectively attend to, encode, and retrieve negative information about the self, resulting in the formation of more negative self-evaluations. In addition to the psychological pathways through which the vulnerability effect (and the small scar effect) might operate, biological factors might play a role. As yet, there is little knowledge about the possible biological mechanisms underlying self-esteem and underlying its association with psychological adjustment (cf. Pruessner et al., 2005; Putnam & McSweeney, 2008). Generally, self-esteem shows a genetic component, with heritability estimates ranging widely, from 29% to 73% (cf. Saphire-Bernstein, Way, Kim, Sherman, & Taylor,

Table 4 Summary of Effect Sizes for Relation Between Self-Esteem (SE) and Anxiety (ANX) Variable

k

N

Weighted mean effect size

95% CI

Homogeneity (Q)

rSE,ANX SE3ANXa ANX3SEa SE3SEa ANX3ANXa

18 18 10 10 18

3,597 3,597 2,052 2,052 3,597

#.40! #.10! #.08! .62! .47!

[#.46, #.33] [#.14, #.06] [#.13, #.02] [.53, .69] [.42, .52]

83.01! 20.84 13.83 80.00! 63.85!

Note. Computations were made with a random-effects model. k " number of studies; N " total number of participants in the k samples; rSE,ANX " correlation between the constructs at Time 1; CI " confidence interval. a Standardized regression coefficient. ! p ' .05.

228

SOWISLO AND ORTH

Table 5 Correlations and Standardized Regression Coefficients for Sample Characteristics Predicting the Self-Esteem Effect on Depression (k " 69) Predictor

r

!

Proportion of female participants Mean age Time lag Sample typea

.00 #.11 #.25! .09

.13 #.10 #.19 .25!

Note. Computations for the multiple regression analysis were made with a random-effects model. Homogeneity Qmodel " 10.25 (df " 4, p " .036); homogeneity Qresidual " 84.55 (df " 64, p " .044); R2 " .11. k " number of studies. a 1 " representative, 0 " nonrepresentative. ! p ' .05.

.00

Self-esteem effect on depression

2011). More specifically, biological variables that have been associated with low self-esteem and depression include reduced hippocampal volume (Pruessner et al., 2005), higher cortisol stress response (Pruessner, Hellhammer, & Kirschbaum, 1999), specific patterns of prefrontal electroencephalography alpha activity (De Raedt, Franck, Fannes, & Verstraeten, 2008; Putnam & McSweeney, 2008), variations in the oxytocin receptor gene (Saphire-Bernstein et al., 2011), and reduced cardiac vagal tone (Martens, Greenberg, & Allen, 2008). Future research should examine whether these factors contribute (e.g., as third variables, moderators, or mediators) to the explanation of the effect of low self-esteem on depression (for an example, see Scarpa & Luscher, 2002). We can only speculate as to why depression and anxiety are differentially linked to low self-esteem. Divergent mediating mechanisms might provide an explanation. For example, selffocused attention is differentially related to depression and anxiety. First, self-focused attention is more strongly related to depression than to anxiety (Mor & Winquist, 2002), and if self-focused attention is a mediator of the vulnerability effect, it might account for the stronger effect of low self-esteem on depression than on anxiety. Second, depression is more strongly related to a focus on private aspects of the self, whereas anxiety is more strongly related to public aspects of the self (Mor & Winquist, 2002). If the vulnerability effect of low self-esteem is mediated more strongly by private self-focus than by public self-focus, this might provide a further explanation for the diverging effects on depression and anxiety. Third, given that the evidence suggests that self-focused attention has a reciprocal relation with depression and anxiety (Mor & Winquist, 2002), self-focused attention might also account for the small, but significant, reverse effects (i.e., the scar effects of depression and anxiety on self-esteem). Another mechanism that might account for the divergent relations of self-esteem with depression and anxiety is that excessive reassurance seeking might lead to increases in depressive, but not in anxious, symptoms (Joiner & Schmidt, 1998). Consequently, if excessive reassurance seeking is a mediator of the vulnerability effect, it might at least partially explain why low self-esteem has stronger predictive effects on depression than on anxiety. Future research should therefore explore the mediating mechanisms of the relation between self-esteem and depression and anxiety from a perspective of specificity: Which common pathways mediate the

-.20

-.40

-.60 0

2

4

6

8

10

12

14

Time lag (years)

Figure 4. Scatterplot displaying the relation between the cross-lagged effect of self-esteem on depression (standardized regression weight) and the time lag between the two assessments. The dashed line shows the weighted mean effect size.

vulnerability effect of low self-esteem on both depression and anxiety? And which additional unique pathways explain that low self-esteem has a stronger effect on depression than on anxiety? The results suggest that the strength of the vulnerability effect of low self-esteem on depression is not moderated by gender and age. Thus, although the mean levels of self-esteem and depression vary as a function of gender (Hyde, Mezulis, & Abramson, 2008; Kling, Hyde, Showers, & Buswell, 1999) and age (Kessler, Foster, Webster, & House, 1992; Lewinsohn, Rohde, Seeley, & Fischer, 1991; Orth et al., 2010, 2012; Robins et al., 2002), the structural relations between self-esteem and depression are unaffected by gender and age. The present meta-analytic findings, which are based on study-level data, are consistent with the findings from primary studies that suggested that the vulnerability effect of low self-esteem holds across gender (Orth et al., 2008; Orth, Robins, Trzesniewski, et al., 2009) and replicates across age groups from young adulthood to old age (Orth, Robins, Trzesniewski, et al., 2009; but see the findings on adolescent age groups by Shahar & Henrich, 2010). From a theoretical perspective, the evidence that the effect of low self-esteem on subsequent depression operates independently from gender and age is in line with the vulnerability model, which states that low self-esteem is a global risk factor for depression. In particular, we note that the vulnerability effect was present not only in samples of adolescents and adults (which represent the majority of the samples examined in this research) but also in samples of children.8 Major depression in childhood is a concern (although the prevalence in childhood is lower than in adolescence; Costello, Erkanli, & Angold, 2006; Kessler, Avenevoli, & Merikangas, 2001), but as yet, few studies have explicitly focused on the longitudinal relations between low self-esteem and depression in children (Abela & Payne, 2003; Abela & Taylor, 2003; 8

The vulnerability effect of low self-esteem on depression, based on seven samples (N " 2,112) with a mean age below 13 years, was #.16 (p ' .05).

229


Table 6 Analysis of Variance of Self-Esteem Effect on Depression by Sample Type (k " 77) Sample type

k

N

Weighted mean effect sizea

95% CI

Homogeneity (Q)

Representative Convenience College students Clinical

9 47 18 3

18,199 14,078 2,573 651

#.12! #.17! #.13! #.15!

[#.17, #.07] [#.20, #.14] [#.18, #.08] [#.26, #.03]

6.47 66.93! 10.92 5.70

Note. Computations were made with a random-effects model. Homogeneity Qbetween " 3.63 (df " 3, p " .304); homogeneity Qwithin " 89.99 (df " 73, p " .086). k " number of studies; N " total number of participants in the k samples; CI " confidence interval. a Standardized regression coefficient. ! p ' .05.

Borelli & Prinstein, 2006; Conley, Haines, Hilt, & Metalsky, 2001; McCarty, Vander Stoep, & McCauley, 2007; Robinson, Garber, & Hilsman, 1995). The finding that the vulnerability model holds for children is important because children’s self-esteem is subject to relatively strong developmental changes (Robins et al., 2002; Trzesniewski, Donnellan, & Robins, 2003). Moreover, typical depressive symptoms of children may differ from typical symptoms among adolescents and adults (specifically, childhood depression can be characterized more strongly by irritable than depressed mood; American Psychiatric Association, 2000). The finding that low self-esteem shows a similar relation to depression in children as in adults is in line with findings on other vulnerability factors for depression (Abela & Hankin, 2008). The moderator analyses also indicated that the vulnerability effect of low self-esteem holds in different sample types. One important finding is that the effect replicates in representative samples (our data set included nine representative samples with altogether more than 18,000 individuals), which significantly strengthens the generalizability of the findings. Another important finding is that the effect also replicates in clinical samples, supporting the hypothesis that low self-esteem is a risk factor not only for moderate but also for clinically relevant levels of depression and, possibly, for depressive disorders. Although this conclusion must be treated with caution because of the small number of data points (i.e., three clinical samples including about 650 individuals), additional aspects support the conclusion. First, longitudinal studies have demonstrated a relation between low self-esteem and clinically diagnosed depression (Ormel, Oldehinkel, & Vollebergh, 2004; Trzesniewski et al., 2006). Second, in the general

population the prevalence of clinical depression is high (Kessler, Berglund, et al., 2005), which consequently should be reflected in the representative samples included in this meta-analysis. Third, as mentioned in Footnote 1, the available evidence suggests that depression is best conceptualized as a continuous rather than a categorical construct: Representative samples, which cover the full range of depression levels from absence of any depressive symptom to severe levels of depression, should therefore provide for valid insights into the structural relations between self-esteem and depression. We also tested for the moderating influence of the time lag between assessments, in response to a previous call for studying its influence on cross-lagged effects in a meta-analytic framework (Collins & Graham, 2002). We found that the vulnerability effect of low self-esteem on depression did not significantly vary as a function of the time interval between assessments, which is somewhat unexpected in view of the findings of Cole and Maxwell (2003). Cole and Maxwell’s analysis suggests that the effect size should be zero when the time interval is zero (because any causal effect needs a minimum amount of time to unfold); that the effect size should increase when the time interval becomes larger, reaching a maximum at a specific time interval; and that subsequently the effect size should decrease again and approach zero (because after long time intervals the causal effect will have disappeared). Although our meta-analysis covered a large range of time lags (from several days to several years), no linear or curvilinear trend was detectable after controlling for other study characteristics. One reason might be that the number of studies was too small for this type of moderator analysis, restricting the statistical power. Nev-

Table 7 Analysis of Variance of Self-Esteem Effect on Depression by Self-Esteem Measure (k " 77) Self-esteem measure

k

N

Rosenberg Self-Esteem Scale Harter Self-Perception Profile Other

60 11 6

30,954 2,721 1,826

Weighted mean effect sizea !

#.15 #.16! #.18!

95% CI

Homogeneity (Q)

[#.18, #.13] [#.21, #.10] [#.25, #.10]

71.08 6.60 12.59!

Note. Computations were made with a random-effects model. Homogeneity Qbetween " 0.34 (df " 2, p " .843); homogeneity Qwithin " 90.27 (df " 74, p " .096). k " number of studies; N " total number of participants in the k samples; CI " confidence interval. a Standardized regression coefficient. ! p ' .05.

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Table 8 Analysis of Variance of Self-Esteem Effect on Depression by Depression Measure (k " 77) Depression measure

K

N

Weighted mean effect sizea

95% CI

Homogeneity (Q)

CES-D BDI CDI Other

30 20 8 19

24,872 4,390 2,487 3,752

#.14! #.16! #.13! #.18!

[#.18, #.11] [#.21, #.12] [#.19, #.07] [#.23, #.14]

39.86 18.28 5.03 28.87

Note. Computations were made with a random-effects model. Homogeneity Qbetween " 2.66 (df " 3, p " .447); homogeneity Qwithin " 92.04 (df " 73, p " .065). k " number of studies; N " total number of participants in the k samples; CI " confidence interval; CES-D " Center for Epidemiologic Studies Depression Scale; BDI " Beck Depression Inventory; CDI " Children’s Depression Inventory. a Standardized regression coefficient. ! p ' .05.

ertheless, our analysis indicates that the vulnerability effect is stable and detectable across a wide range of time intervals. This finding has two implications. First, it indicates that self-esteem has predictive power over a long period. Consequently, an important avenue for future research is to further investigate which mediating mechanisms account for the large temporal stability of this effect. Second, the finding indicates that the vulnerability effect of low self-esteem is already detectable after short time intervals. Consequently, future research should examine which mediating mechanisms account for the self-esteem effect across a few weeks or even a few days (and whether the mediating processes across short vs. long time intervals are identical). For example, low self-esteem might elicit rumination on one day, which in turn exacerbates depressive symptoms over the following days (Nolen-Hoeksema, 2000). Thus, in future research it would be intriguing to study these phenomena in a higher temporal resolution, for example, using diary data. An important task of future research is to further examine moderators of the vulnerability effect of low self-esteem and to explain why some people with low self-esteem develop depression while others do not. For example, previous research suggests that the vulnerability effect might be stronger if a person’s self-esteem is not only low but also temporally stable (Kernis, Grannemann, & Mathis, 1991). Another example is research by Michalak, Teismann, Heidenreich, Strohle, and Vocks (2011), suggesting that mindful acceptance buffers the detrimental effect of low selfesteem on depression. Moreover, situational factors could moderate the vulnerability effect. One hypothesis is that low self-esteem might have stronger effects on depression when the individual simultaneously suffers from stressful life circumstances (e.g., J. E. Roberts, 2006). However, in three independent studies, Orth, Robins, and Meier (2009) did not find evidence that the occurrence of stressful life events or daily hassles influenced the prospective effect of low self-esteem on depression. Nevertheless, it is possible that other characteristics of the situation moderate the strength of the vulnerability effect. For example, social support by relationship partners, family, and friends might protect individuals with low self-esteem from spiraling downward into depression. The results on the stability coefficients provide an additional argument in favor of the distinction between the concepts of self-esteem and depression. More precisely, the present results suggest that self-esteem is a more stable, trait-like construct than depression, corresponding to findings reported in the literature

(Lovibond, 1998; Trzesniewski et al., 2003).9 Given that the average time lag between assessments was more than 1 year, the stability coefficients for self-esteem are not much smaller than the stability coefficients of broad personality constructs such as the Big Five personality factors (Ferguson, 2010; B. W. Roberts & DelVecchio, 2000). This result is in line with the findings by Trzesniewski et al. (2003), who reported that the stability of self-esteem is moderately high across the life span (disattenuated correlations averaging in the .50s–.70s). Moreover, the present results are consistent with the notion that, typically, the more dispositional factor (i.e., self-esteem) influences the more fluctuating, state-like factor (i.e., depression) rather than vice versa. If low self-esteem and depression were two interchangeable indicators of the same construct, then they should have comparable stabilities, because their individual stabilities should each reflect the stability of the common factor. However, we note that the stability coefficient for depression was still relatively large, indicating a moderate degree of stability.

Limitations An important limitation of this research is that it does not allow for strong conclusions regarding the causality of the relations between self-esteem, depression, and anxiety, because all the studies included in the meta-analysis used correlational designs. Therefore, the effects under investigation were not experimentally induced but may be caused by third variables that were not controlled for (Finkel, 1995; Little, Preacher, Selig, & Card, 2007). For example, neuroticism is related to low self-esteem (Judge et al., 2002; Robins et al., 2001) and depression (Kendler, Neale, Kessler, Heath, & Eaves, 1993; Ormel, Oldehinkel, & Brilman, 2001), and therefore might be a third variable influencing both constructs. Another example might be common genetic factors of low self-esteem and depression (Neiss, Stevenson, Legrand, Iacono, & Sedikides, 2009; S. B. Roberts & Kendler, 1999). Future 9

No formal significance test for the difference between the stability coefficients of self-esteem and depression is available, for the reasons given in Footnote 6. We therefore used the confidence intervals as an approximate means of comparing the coefficients. Moreover, for comparison purposes, we also computed an unpaired t test, which was significant, with t(117) " 5.10, p ' .001, suggesting that the coefficients differed significantly.


research should test relevant third-variable models that might account for the relations between low self-esteem and depression, and low self-esteem and anxiety. Nevertheless, when experimental designs are not feasible for ethical or practical reasons, longitudinal analyses are useful because they can indicate whether the data are consistent with a causal model of the relation between the variables, by establishing the direction of the effects and ruling out some (but not all) alternative causal hypotheses. Another limitation is that nearly all studies included in the meta-analysis employed self-report measures of the constructs. Although the vast majority of the measures used are reliable and well validated, a problem of the exclusive reliance on self-report methodology is that correlations between measures may be artificially inflated by shared method variance. Note, however, that shared method variance cannot account for the prospective crosslagged effects because shared method variance has already been statistically removed by controlling for prior levels of the predicted construct. Nevertheless, future research would benefit from including measures based on informant reports (e.g., ratings by relationship partners) and diagnostic interviews to further control for possible self-report biases. Furthermore, the studies included in the meta-analysis were predominantly conducted in Western cultural contexts (i.e., only two studies were conducted in Asia). Therefore, future research should test whether the results hold in other cultural contexts, such as in Asian or African cultures (Arnett, 2008; Henrich, Heine, & Norenzayan, 2010). The function of self-esteem and the frequency or intensity of depressive and anxious symptoms may vary crossculturally. For example, individuals from Asian and Western cultures show different self-construal styles and different tendencies toward self-enhancement (Heine et al., 1999; Markus & Kitayama, 1991). As another example, research suggests that there are cultural differences in the reporting of depressive symptoms (Parker, Gladstone, & Chee, 2001; Ryder et al., 2008). These cross-cultural differences might have consequences for the relation of low selfesteem with depression and anxiety. Therefore, whether studies with samples from other cultural contexts would yield the same results as the present meta-analysis is currently unknown. An additional limitation is that our data did not allow us to investigate several, more nuanced characteristics of the relations between self-esteem, depression, and anxiety. First, it would be interesting to test for other models that could explain the relations between the constructs. For example, in addition to being a vulnerability factor, self-esteem might influence the course or treatment of depressive disorders (e.g., G. W. Brown, Bifulco, & Andrews, 1990; Ezquiaga et al., 2004), corresponding to the pathoplasty model (Clark, 2005; Klein, Kotov, & Bufferd, 2011; Santor, Bagby, & Joffe, 1997). The present meta-analysis did not allow examining the pathoplasty model because very few studies reported information on diagnoses and treatment of depressive disorders (information that would be needed to assess whether self-esteem predicts the course of depressive disorders). Another example is the common cause model (Klein et al., 2011), which states that low self-esteem and depression have a shared etiology accounting for the observed association and that corresponds to the third-variable models discussed above. The present meta-analysis did not allow testing the common cause model because very few of the primary studies examined third variables that could serve as a common cause (as mentioned above, it would, for example, be

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interesting to test whether neuroticism is a common cause of low self-esteem and depression). Second, because clinical anxiety is a heterogeneous category (Heimberg et al., 1989; Mor & Winquist, 2002), research should clarify how low self-esteem relates to different forms of anxiety (e.g., social anxiety, worry, panic, and phobias). Recent studies with child, adolescent, and adult samples have found that some forms of anxiety (e.g., generalized anxiety) load together with depression and dysthymia on one factor, rather than on another factor together with the remaining forms of anxiety (Krueger, 1999; Lahey et al., 2004). Accordingly, low selfesteem might be a stronger vulnerability factor for certain forms of anxiety such as generalized anxiety (i.e., having a prospective effect of similar size as for depression). Similarly, it might be interesting to further investigate how low self-esteem relates do different forms of depression (e.g., depressive episodes with atypical or melancholic features; American Psychiatric Association, 2000). Another limitation of the meta-analytic approach is that we could not control for potential content overlap between the constructs. Although self-esteem and anxiety measures typically do not overlap in their item content, depression measures frequently include one or two items that are conceptually related to selfesteem. However, the fact that the vulnerability effect of low self-esteem on depression replicated across different combinations of self-esteem and depression measures (which may differ in their degree of content overlap) suggests that the effect is not biased by potential content overlap. Moreover, the findings from four longitudinal studies (Orth et al., 2008; Orth, Robins, Trzesniewski, et al., 2009) that controlled for content overlap between self-esteem and depression scales suggest that the vulnerability effect of low self-esteem is not influenced by depression items that tap into the self-esteem construct. A related issue is that depression and anxiety measures frequently overlap in their item content, which in turn might affect the relative strength of their individual relations to self-esteem. To further address this issue, future research should employ designs in which self-esteem, depression, and anxiety are simultaneously examined and potential content overlap is controlled for. Moreover, this approach would afford the additional benefit of enabling tests of prospective effects between all three variables. Furthermore, it is possible that the relations between selfesteem, depression, and anxiety are influenced by narcissism, which is conceptually related to high self-esteem (Morf & Rhodewalt, 2001; Tracy, Cheng, Robins, & Trzesniewski, 2009). Although measures of self-esteem and narcissism are only moderately correlated (Ackerman et al., 2011; R. P. Brown & ZeiglerHill, 2004), it is possible that the prospective effects of low self-esteem on depression and anxiety are even stronger when narcissism is statistically controlled for. Finally, it is possible that self-esteem, depression, and anxiety have been subject to generational changes in the past decades, and consequently an important question is whether these possible secular trends in the mean levels of the constructs can be reconciled with the findings of our meta-analysis. First, we note that the evidence regarding generational changes in self-esteem, depression, and anxiety is inconsistent. For example, whereas some studies suggest that there are generational increases in self-esteem (Gentile, Twenge, & Campbell, 2010; Twenge & Campbell, 2001), the results of other studies—two of which used longitudinal data

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from national probability samples—suggest that the average level of self-esteem has not changed across the generations born in the 20th century (Erol & Orth, 2011; Orth et al., 2010, 2012). Similarly, the evidence regarding generational increases in constructs related to self-esteem, such as self-enhancement and narcissism, is inconsistent. For example, whereas some studies find supporting evidence (Twenge & Foster, 2008; Twenge, Konrath, Foster, Campbell, & Bushman, 2008), the results of other studies suggest that there are no generational changes (Trzesniewski & Donnellan, 2010; Trzesniewski, Donnellan, & Robins, 2008). Also, with regard to depression and anxiety, some studies report significant generational increases (e.g., Twenge, 2000), whereas other studies did not find supporting evidence (Booth, Sharma, & Leader, 2011; Orth et al., 2012; Simon & VonKorff, 1992). In sum, the available evidence on generational changes in the constructs examined in this research is inconsistent and a topic of current debate in the literature. Second, even if generational changes in the constructs were present, they do not necessarily contradict the findings of the present study. The reason is that mean levels of variables and the structural relations between these variables can vary independently from one another. For example, it is possible that the mean levels of self-esteem, depression, and anxiety change over time, whereas the structural relations between the constructs remain unaltered. In accordance with this reasoning, although previous research has documented significant age differences in the level of self-esteem (Meier, Orth, Denissen, & Ku¨hnel, 2011; Orth et al., 2010, 2012) and depression (Kessler et al., 1992; Mirowsky & Kim, 2007) across the life course, this meta-analysis and a previous study (Orth, Robins, Trzesniewski, et al., 2009) found that age did not significantly moderate the prospective relations between selfesteem and depression. We therefore believe that the validity of our meta-analytic findings is not called into question by possible generational changes in the constructs.

Conclusions The present research suggests that self-esteem shows diverging structural relations with depression and anxiety. As yet, drawing clinical recommendations from this affective specificity would be premature. Nevertheless, continuing this line of research might ultimately lead to the identification of mechanisms specific to depression and anxiety, which in turn might provide important information for the further development of disorder-specific treatment approaches. Moreover, the present research shows that the effect of low self-esteem on depression is robust and holds across different sample and design characteristics of studies. The robustness of the effect has important implications for research, suggesting that the conclusions of extant studies in this field are probably generalizable and that future studies can build on this effect and investigate it in more detail. Furthermore, when studying vulnerability factors for depression, researchers should control for low self-esteem in order not to overestimate the effects of other vulnerability factors. The robustness of the effect also strengthens the potential importance of self-esteem interventions. If future research supports the hypothesized causality of the relations between the constructs, interventions aimed at increasing self-esteem might be useful in reducing the risk of depression, regardless of the gender and age of

the individuals, and might not only reduce the short-term risk of depression but have a long-lasting, positive influence.

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Received May 27, 2011 Revision received April 16, 2012 Accepted April 23, 2012 !


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Comparing the Effects of Low, Unstable, and Contingent Self-Esteem on Depression: Two Longitudinal Studies

Julia Friederike Sowislo and Ulrich Orth University of Basel Laurenz L. Meier University of South Florida

Author Note Julia Friederike Sowislo and Ulrich Orth, Department of Psychology, University of Basel; Laurenz L. Meier, Department of Psychology, University of South Florida. This research was supported by Swiss National Science Foundation Grant PP00P1123370 to Ulrich Orth. Correspondence concerning this article should be addressed to Julia Friederike Sowislo, Department of Psychology, University of Basel, Missionsstrasse 62, 4055 Basel, Switzerland. Email: [email protected].


2 Abstract

A growing body of longitudinal studies suggests that low self-esteem is a risk factor for depression. However, it is unclear whether other characteristics of self-esteem, besides its level, explain incremental or even greater variance in subsequent depression. We examined the prospective effects of self-esteem level, instability (i.e., the degree of variability in self-esteem across short periods), and contingency (i.e., the degree to which self-esteem fluctuates in response to self-relevant events) on depression in one overarching model, using data from two longitudinal studies. In Study 1, 372 adults were assessed at 2 waves over 6 months, including 40 daily diary assessments at Wave 1. In Study 2, 235 young adults were assessed at 2 waves over 6 weeks, including about 6 daily diary assessments at each wave. Self-esteem contingency was measured by self-report and by a statistical index based on the diary data (capturing event-related fluctuations in self-esteem). In both studies, only level, but not instability and contingency, of self-esteem predicted subsequent depression. Also, level, instability, and contingency of selfesteem did not interact in the prediction of depression. Moreover, the effect of self-esteem level on depression held when controlling for neuroticism and for all other Big Five personality traits. The findings suggest that low self-esteem, but not unstable and contingent self-esteem, is a risk factor for depression. Keywords: self-esteem, depression, instability and contingency of self-esteem, Big Five personality traits, diary data


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Comparing the Effects of Low, Unstable, and Contingent Self-Esteem on Depression: Two Longitudinal Studies In his benchmark essay “Mourning and Melancholia,” published first in 1917, Freud proposed that “the melancholic displays … an extraordinary diminution in his self-regard” (p. 246). Since then, a growing body of theory and empirical work has suggested that low selfesteem is a risk factor for depression. In particular, longitudinal studies indicate that low selfesteem prospectively predicts depression (e.g., Kernis et al., 1998; Orth, Robins, & Meier, 2009; Orth, Robins, & Roberts, 2008; J. E. Roberts & Monroe, 1992).1 A meta-analysis of the available longitudinal studies suggests that the effect of low self-esteem on depression is robust and holds across sample and design characteristics of studies (Sowislo & Orth, in press). However, research suggests that there is “more to self-esteem than whether it is high or low” (Kernis, Cornell, Sun, Berry, & Harlow, 1993, p. 1090) and that other characteristics of self-esteem, besides its level, can have important consequences for emotion, cognition, and behavior. With regard to vulnerability to depression, researchers have proposed that fluctuations in self-esteem might be influential (e.g., Crocker & Wolfe, 2001; Kernis et al., 1993; J. E. Roberts & Monroe, 1994). To describe the extent and nature of fluctuations in self-esteem, two constructs have been introduced into the literature, specifically self-esteem instability (e.g., Kernis, 2005) and self-esteem contingency (e.g., Crocker & Wolfe, 2001). Although previous studies have investigated the relations of self-esteem instability and contingency with depression (e.g., Bos, Huijding, Muris, Vogel, & Biesheuvel, 2010; Butler, Hokanson, & Flynn, 1994; Kernis et al., 1998; Kim & Cicchetti, 2009; Meier, Semmer, & Hupfeld, 2009; J. E. Roberts, Shapiro, & Gamble, 1999; Sargent, Crocker, & Luhtanen, 2006), the results of these studies are highly inconsistent, as we will review in detail below. Moreover, we are not aware of any study


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that has pitted the effects of self-esteem level, instability, and contingency on depression against each other in the context of a single study. Thus, it is unclear whether instability and contingency of self-esteem explain incremental variance in subsequent depression or even greater variance than level of self-esteem. To address these issues, in the present research we use trait and diary data from two longitudinal studies and examine the reciprocal relations of self-esteem level, instability, and contingency with depression. Models of Vulnerable Self-Esteem Self-esteem has been defined as “a person’s appraisal of his or her value” (Leary & Baumeister, 2000, p. 2). Vulnerable self-esteem can be defined by “those characteristics of [selfesteem] that place individuals at risk for future depression” (J. E. Roberts & Monroe, 1994, p. 162). Although vulnerable self-esteem plays an important role in several classic theories and contemporary models of depression (Abramson, Seligman, & Teasdale, 1978; Blatt, D'Afflitti, & Quinlan, 1976; Brown & Harris, 1978), just which characteristics constitute vulnerable selfesteem is still open to debate. In the following we will discuss three alternative models of vulnerable self-esteem, namely vulnerability as a function of self-esteem level, vulnerability as a function of self-esteem instability, and vulnerability as a function of self-esteem contingency. Of course, these three models are not mutually exclusive because two or even all three processes (i.e., low, unstable, and contingent self-esteem contributing to depression) might operate simultaneously. Vulnerability as a Function of Self-Esteem Level The first model states that a low level of self-esteem is a causal risk factor for depression (Beck, 1967; Butler et al., 1994; Metalsky, Joiner, Hardin, & Abramson, 1993; J. E. Roberts & Monroe, 1992; Whisman & Kwon, 1993; Zeigler-Hill, 2011). For example, according to Beck’s


5

(1967) cognitive theory of depression, negative beliefs about the self are not just a symptom of depression but a diathesis exerting causal influence in the onset and maintenance of depression. The mechanisms that account for the vulnerability effect of low self-esteem on depression are not yet understood. For example, a possible interpersonal mechanism is that individuals with low self-esteem might excessively seek reassurance from friends and relationship partners, which might lead to interpersonal conflicts that in turn elicit depressive symptoms (Joiner, Alfano, & Metalsky, 1992; Potthoff, Holahan, & Joiner, 1995). Another possible interpersonal mechanism is that individuals with low self-esteem seek negative feedback from their relationship partners to verify their negative self-concept, leading to rejection by close others, undermining social support, and in turn increasing the risk of depression (Giesler, Josephs, & Swann, 1996; Joiner, Katz, & Lew, 1997; Swann, Wenzlaff, & Tafarodi, 1992). A possible intrapersonal mechanism might operate through rumination (Mor & Winquist, 2002). Individuals with low self-esteem are prone to ruminate about negative aspects of their self, which in turn increases depression (NolenHoeksema, 2000; Spasojevic & Alloy, 2001). However, as yet, only one study has tested for mediation of the vulnerability effect of low self-esteem (Kuster, Orth, & Meier, 2012). Using longitudinal mediation analysis (Cole & Maxwell, 2003), Kuster et al. (2012) found that rumination partially mediated the prospective effect of low self-esteem on depression across several waves of data. Vulnerability as a Function of Self-Esteem Instability The second model is based on the observation that people not only differ in their habitual level of self-esteem, but also in the extent to which their self-esteem fluctuates around this level. For some individuals, self-esteem fluctuates strongly, so that on one day they may feel selfconfident, whereas on the next day they may feel incompetent and useless. In contrast, the self-


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esteem of other individuals is relatively stable across several weeks or even months. The extent of temporal fluctuation in self-esteem is captured by measures of self-esteem instability (Kernis, 2003, 2005). Typically, self-esteem instability—which has also been labeled self-esteem variability (e.g., Hayes, Harris, & Carver, 2004) and self-esteem lability (e.g., J. E. Roberts & Kassel, 1997)—is measured by computing the intraindividual standard deviation across several repeated assessments which are conducted over a short period such as one week (Kernis & Goldman, 2006). A person’s self-esteem may be unstable for several reasons. First, individuals with unstable self-esteem may experience a greater number of relevant positive and negative events in daily life compared to individuals with more stable self-esteem; thus, situational factors may determine the degree to which self-esteem is unstable. Second, unstable self-esteem may be caused by a general dysregulation in a person’s psychological system, possibly due to dysregulation in related biological systems (J. E. Roberts & Kassel, 1997). The notion that unstable self-esteem is a risk factor for depression is a commonly accepted view of the causal relationship between self-esteem and depression. For example, Kernis et al. (1998) state that unstable self-esteem “appears to be a diathesis for depressive symptoms” (p. 665), and Roberts and Gotlib (1997) state that “recent theory and research have suggested that dysregulation and variability in self-esteem … contribute to vulnerability to depression” (p. 521). Similarly, Crocker (2002a) argues that “it is increases and decreases in state self-esteem, rather than its average or trait level, that motivates behavior and constitutes a risk factor for depression” (p. 144). A possible explanation can be derived from Beck’s (1967) cognitive theory of depression, which states that schemata are generally latent until activated. Individuals at risk for depression might lack resilience to primes that activate negative self-schemata. A particularly important


7

prime is negative mood (Teasdale, 1988; Teasdale & Dent, 1987). Thus, episodes of negative mood may activate negative cognitions about the self, which in turn increase depressive symptoms. Another cognitive approach proposes that individuals at risk for depression have problems with consistently and efficiently processing self-relevant information (i.e., poor schema consolidation; Swallow & Kuiper, 1988). As a consequence, these individuals experience heightened uncertainty about themselves, which may contribute to depression (J. E. Roberts & Monroe, 1994; Swallow & Kuiper, 1988). Finally, a possible explanation is that short-term fluctuations in self-esteem might induce the feeling of helplessness (Crocker & Wolfe, 2001), which is a risk factor for depression (Abramson, Metalsky, & Alloy, 1989; Metalsky & Joiner, 1992). In addition, it is possible that the effect of self-esteem instability on depression depends on the level of self-esteem, or, to put it differently, that level and instability interact in their effect on depression. For example, Kernis et al. (1991) hypothesized that instability is associated with different psychological processes among individuals with low versus high self-esteem. Whereas stability might be psychologically adaptive for individuals with high self-esteem, stability might reflect rigidity and lack of adjustment to changing environments for individuals with low selfesteem (Gable & Nezlek, 1998; Paulhus & Martin, 1988). Vulnerability as a Function of Self-Esteem Contingency The third model proposes that the contingency of self-esteem is a vulnerability factor for depression. People differ in the extent to which their self-esteem is influenced by positive and negative events, such as starting a new relationship, getting a compliment from a friend, receiving a bad grade in an exam, or performing poorly at work (Kernis, 2005). Generally, selfesteem contingency has been defined as the degree to which self-esteem fluctuates in response to


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self-relevant events (Crocker & Park, 2004; Crocker & Wolfe, 2001). Whereas some people experience boosts and drops in their self-esteem even when they receive minor positive and negative social feedback, other people’s self-esteem fluctuates only when major self-relevant events occur. Even though instability and contingency of self-esteem are conceptually related, the two constructs should be distinguished. For example, it is possible that the self-esteem of an individual varies over time, not because his or her self-esteem is contingent, but because biological factors cause fluctuations in self-esteem (J. E. Roberts & Kassel, 1997). Moreover, even if the self-esteem of an individual is highly contingent, it is possible that he or she experiences complete stability in self-esteem for some time, because no relevant events happen in the person’s life at this time. There are several theories that suggest that contingent self-esteem is a risk factor for depression (for an overview see J. E. Roberts & Monroe, 1994). In particular, psychoanalytic approaches have suggested that individuals at risk for depression lack internal foundations of self-esteem and base their self-esteem on approbation and recognition from others (Rado, 1928). Importantly, as long as external sources of self-esteem are present, the level of self-esteem of these individuals is not necessarily reduced (J. E. Roberts & Monroe, 1992). Second, Jacobson (1975) suggested that individuals at risk for depression are marked by low narcissistic tolerance. Accordingly, these individuals have problems with tolerating threats to their overly positive selfimage and , when failing in a specific domain, overgeneralize the failure to the entire self. Generally, these approaches suggest a positive linear effect of self-esteem contingency on depression. In contrast, sociometer theory (Leary & Baumeister, 2000; Leary, Tambor, Terdal, & Downs, 1995) suggests that a certain degree of self-esteem contingency is beneficial for the


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individual (Leary, 2006). The theory states that self-esteem is a sociometer that serves as a subjective monitor of the extent to which a person is valued as a member of desirable groups and relationships (Leary & Baumeister, 2000). More precisely, this monitoring process requires selfesteem contingency: the sociometer reacts to cues that are relevant for the individual’s relational value with drops or boosts in self-esteem. In particular, drops in self-esteem motivate behavior aimed at increasing or restoring the threatened relational value. Leary (2004) suggests that a medium degree of contingency is optimal for the individual’s psychological and social adjustment, whereas both low and high degrees of contingency are signs of a miscalibrated system. Thus, both a hypersensitive sociometer (i.e., the person’s self-esteem reacts too strongly) and a hyposensitive sociometer (i.e., the person’s self-esteem reacts too little or not at all), interferes with adaptive regulation of social interactions, which in turn might harm social relationships and consequently could increase the risk for depression (Oosterwegel, Field, Hart, & Anderson, 2001). In other words, sociometer theory proposes a curvilinear, U-shaped relation between self-esteem contingency and depression. Evidence on the Relation between Vulnerable Self-Esteem and Depression In this section, we review studies that (a) are prospective (i.e., that tested effects of selfesteem characteristics measured on one occasion on depression measured on a subsequent occasion) and (b) controlled for prior levels of the predicted variable (i.e., controlled for autoregressive effects). Controlling for prior levels of the variables is of crucial importance, because it rules out the possibility that prospective effects are simply due to concurrent relations between the variables and the autoregressor of the predicted variable (Cole & Maxwell, 2003; Finkel, 1995). Effect of Self-Esteem Level on Depression


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Overall, the available longitudinal studies support the vulnerability effect of self-esteem level on depression (e.g., Kernis et al., 1998; Orth, Robins, & Meier, 2009; Orth et al., 2008; J. E. Roberts & Monroe, 1992). The meta-analysis by Sowislo and Orth (in press) suggested that self-esteem level has a significant effect on subsequent depression (the a erage e ect was β = .16, controlling for prior levels of depression). Furthermore, the available evidence suggests that the effect is robust, holding for men and women (Orth et al., 2008; Orth, Robins, Trzesniewski, Maes, & Schmitt, 2009; Sowislo & Orth, in press), for all age groups from childhood to old age (Orth, Robins, Trzesniewski, et al., 2009; Sowislo & Orth, in press), for different measures of self-esteem and depression (Sowislo & Orth, in press), for affective-cognitive and somatic symptoms of depression (Kuster et al., 2012; Orth, Robins, Trzesniewski, et al., 2009), and after controlling for content overlap between self-esteem and depression scales (Kuster et al., 2012; Orth et al., 2008; Orth, Robins, Trzesniewski, et al., 2009). Effect of Self-Esteem Instability on Depression Some studies have investigated whether self-esteem instability prospectively predicts depression, over and above the effect of self-esteem level. Table 1 provides a summary of the findings from these studies, which yielded highly inconsistent results. Four studies supported the hypothesis that unstable self-esteem predicts depression (Butler et al., 1994; Franck & De Raedt, 2007; Kernis et al., 1998, using the Beck Depression Inventory, BDI; J. E. Roberts et al., 1999), whereas four other studies did not find supporting evidence (Kernis et al., 1998, using the Center for Epidemiologic Studies Depression Scale, CES-D; Kim & Cicchetti, 2009; J. E. Roberts & Gotlib, 1997; Vickery, Evans, Sepehri, Jabeen, & Gayden, 2009). Moreover, two studies found evidence for a significant interaction effect between level and instability of self-esteem (Kernis et al., 1998, using the CES-D; J. E. Roberts et al., 1999). For instance, Kernis et al. (1998) found


11

that instability was linked to depression for individuals with high self-esteem, whereas stability—not instability—predicted depression for individuals with low self-esteem. Four other studies did not find significant evidence of interactions (Butler et al., 1994; Franck & De Raedt, 2007; Kernis et al., 1998, using the BDI; Vickery, Evans, et al., 2009). Thus, the results for self-esteem instability are highly inconsistent. A possible explanation is that many previous studies were based on relatively small samples (see Table 1), providing insufficient power to test whether level and instability of self-esteem predict depression, and even lower statistical power to test for interactive effects (J. Cohen, Cohen, West, & Aiken, 2003). In addition, some studies examined effects of self-esteem instability on depression, but did not control for prior levels of depression. Again, these studies yielded inconsistent results. Whereas some studies found that both level and instability predict depression (de Man, Gutiérrez, & Sterk, 2001; J. E. Roberts, Kassel, & Gotlib, 1995, Study 1), other studies found that only level of self-esteem predicted depression (Kernis et al., 1991; J. E. Roberts et al., 1995, Studies 2 and 3; Vickery, Sepehri, Evans, & Jabeen, 2009; Vickery, Sepehri, Evans, & Lee, 2008). Again, some studies found significant interactions between level and instability of selfesteem (de Man et al., 2001; Kernis et al., 1991; J. E. Roberts et al., 1995, Study 1; Vickery et al., 2008), whereas other studies failed to find a significant interaction (J. E. Roberts et al., 1995, Studies 2 and 3). Effect of Self-Esteem Contingency on Depression Only one study examined whether self-esteem contingency prospectively predicts depression, over and above the effect of self-esteem level. In this study, Butler et al. (1994) did not find a significant effect of contingent self-esteem; moreover, there was no significant interaction between contingency and level of self-esteem on depression. However, other studies


12

on self-esteem contingency, which did not control for the effect of self-esteem level, partially supported the hypothesis that contingent self-esteem predicts depression (Burwell & Shirk, 2006; Crocker, 2002b; Sargent et al., 2006). All of these studies tested for a linear effect of self-esteem contingency on depression; we are not aware of any study that tested for a curvilinear, U-shaped effect of self-esteem contingency on depression, corresponding to the assumptions of sociometer theory as outlined above. The Present Research Thus, although previous research has examined whether self-esteem instability and contingency are—in addition to a low level of self-esteem—vulnerability factors for depression, the available evidence on the effects of instability and contingency is inconclusive. First, as illustrated by Table 1, the results are highly inconsistent. Second, most of the relevant studies were underpowered. Third, no previous study simultaneously tested for effects of self-esteem level, instability, and contingency on depression. In the present research, we therefore examined the reciprocal effects between all three characteristics of self-esteem and depression, using data from two longitudinal studies. We also systematically tested for interactions between the three self-esteem characteristics. The present research advances previous studies in several ways. First, we simultaneously tested for effects of self-esteem level, instability, and contingency on depression, using one overarching model. Second, we used data from two independent studies with different design characteristics; by replicating the findings across studies we reduce methodological concerns unique to each study and strengthen confidence in the overall pattern of results. Third, in each study, we used two divergent approaches to measure contingent self-esteem, which allowed for a more thorough evaluation of the hypothesis that vulnerability is a function of self-esteem


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contingency. More precisely, as we will describe in detail below, we used both a subjective measure (i.e., self-report) and a more objective measure of contingency (i.e., a statistical index that captures the degree to which self-esteem fluctuates in response to self-relevant events, across a series of diary assessments). Fourth, in addition to testing for a linear effect of self-esteem contingency on depression, we tested for a curvilinear effect, corresponding to the assumptions of sociometer theory. Fifth, one of the studies (i.e., Study 1) included a measure of the Big Five personality traits, which allowed us to test, and possibly rule out, an important alternative explanatory account. Specifically, it is possible that broad personality factors such as neuroticism influence both self-esteem and depression, thereby creating a spurious link between the two constructs (Hankin, Lakdawalla, Carter, Abela, & Adams, 2007; S. B. Roberts & Kendler, 1999; Watson, Suls, & Haig, 2002). For example, neuroticism is related to low self-esteem (Judge, Erez, Bono, & Thoresen, 2002; Robins, Hendin, & Trzesniewski, 2001) and depression (Kendler, Neale, Kessler, Heath, & Eaves, 1993; Ormel, Oldehinkel, & Brilman, 2001). Consequently, even if longitudinal studies indicate that vulnerable self-esteem predicts depression, this effect might be confounded by effects of personality factors such as neuroticism, if these factors are not included in the model (Little, Preacher, Selig, & Card, 2007). In Study 1, we therefore tested whether the effect of vulnerable self-esteem holds when the effects of the Big Five personality traits are controlled for. By doing so, we gained important information on the robustness of our model, which—if the effect of vulnerable self-esteem holds—would strengthen confidence in the findings. Study 1 Method


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Data came from the study My Partner and I (MPI), a German-language study with a sample of couples living in Switzerland (see Erol & Orth, in press). Participants were assessed on trait measures of self-esteem and depression on two occasions separated by six months (denoted as Time 1 and Time 2). Moreover, following the trait assessments at Time 1, participants were assessed on 40 consecutive days using short diary questionnaires including measures of state self-esteem and daily events. Data were collected using Web-based questionnaires. Participants were recruited by contacting members of a university-based online panel, which includes individuals who are interested in occasionally participating in Web-based studies. Individuals were invited to participate (a) if they were currently in a relationship, (b) if their relationship partner was also willing to participate, and (c) if both partners were 18 years or older. Participants received information on the purpose and procedure of the study and were informed that their data would be treated as strictly confidential. After providing informed consent, each partner received individual links to the assessments, and participants were asked to complete the questionnaires without his or her partner being present. The daily diary questionnaires could be accessed between 4 p.m. and 2 a.m. on the corresponding day. The average number of daily reports was 33.9. After completion of the study, participants were provided with individualized feedback on selected study variables (i.e., how their scale scores compared with population norms) and received 80 Swiss francs in exchange for participation in the study. Participants. The sample consisted of 372 individuals (50% female). Mean age of participants at Time 1 was 29.1 years (SD = 8.8, range = 18 to 61). Ten percent had completed the obligatory 9 school years, 54 % had completed secondary education (approximately 12 years), 15% had a Bachelor’s degree, 19% had a Master’s degree, and 2% had a doctoral degree.


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Data on study variables were available for 371 individuals at Time 1 and 341 individuals at Time 2. To investigate the potential effect of attrition, we tested for differences in study variables between participants who completed the Time 2 assessment and participants who had dropped out before Time 2. Participants who dropped out reported slightly higher depression than those who did not (Ms = 0. 82 vs. 0.58; d = 0.55). Although differences in depression were of medium size, differences in self-esteem level, self-esteem instability, the self-report measure of selfesteem contingency, and the statistical index of self-esteem contingency were nonsignificant. Thus, nonrepresentativeness because of attrition was not a serious concern in the present study. Trait measures. Self-esteem level. Self-esteem level was assessed with the RSE, a 10-item self-report measure of self-esteem, which is frequently used and well-validated (cf. Blascovich & Tomaka, 1991; Robins et al., 2001). Responses were measured on a 5-point scale ranging from 1 (strongly disagree) to 5 (strongly agree). The alpha reliability was .91 at both Time 1 and Time 2. Self-esteem contingency (self-report). The most widespread approach to assessing the contingency of self-esteem is using a self-report measure that asks people directly to what degree their self-esteem is contingent on events in daily life (Cambron, Acitelli, & Steinberg, 2010; Crocker, Luhtanen, Cooper, & Bouvrette, 2003; Kernis & Goldman, 2006; Knee, Canevello, Bush, & Cook, 2008). We used the 5-item Others’ Approval subscale of the Contingencies of Self-Worth Scale (CSW; Crocker et al., 2003). The subscale measures the extent to which an individual’s self-esteem is contingent on approval from generalized others. Item examples are “My self-esteem depends on the opinions others hold of me” and “I don’t care what other people think of me” (reverse-scored). Responses were measured on a 5-point scale ranging from 1


16

(strongly disagree) to 5 (strongly agree). The alpha reliability was .83 at Time 1 and .84 at Time 2. Depression. Depression was assessed with the Center for Epidemiologic Studies Depression Scale (CES-D; Radloff, 1977; for the German version see Hautzinger & Bailer, 1993). The CES-D is a frequently used 20-item self-report measure for the assessment of depressive symptoms in nonclinical, subclinical, and clinical populations, and its validity has been repeatedly confirmed (Eaton, Smith, Ybarra, Muntaner, & Tien, 2004). Participants were instructed to assess the frequency of their reactions within the preceding seven days. Responses were measured on a 4-point scale (0 = rarely or none of the time, less than one day; 1 = some or a little of the time, one to two days; 2 = occasionally or a moderate amount of time, three to four days; 3 = most or all of the time, five to seven days). The alpha reliability was .89 at both Time 1 and Time 2. On the basis of the recommended cutoff value of 23 (Hautzinger & Bailer, 1993), 10% of participants at both Time 1 and at Time 2 exhibited a clinically relevant level of depressive symptoms. Big Five personality traits. The Big Five personality traits were assessed with the 44item Big Five Inventory (BFI; John, Donahue, & Kentle, 1991; John, Naumann, & Soto, 2008; for the German version see Lang, Lüdtke, & Asendorpf, 2001), a well-validated measure of the Big Five dimensions (John et al., 2008; Soto & John, 2009). Responses were measured using a 5point scale ranging from 1 (disagree strongly) to 5 (agree strongly). Extraversion was assessed with 8 items, agreeableness with 9 items, conscientiousness with 9 items, neuroticism with 8 items, and openness to experience with 10 items. The alpha reliabilities were .84 (Time 1) and .86 (Time 2) for extraversion, .72 and .74 for agreeableness, .81 and .79 for conscientiousness, .85 and .86 for neuroticism, and .83 and .82 for openness to experience.


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Daily measures. Daily self-esteem. Daily self-esteem was assessed with five items of the RSE , which were slightly adapted to measure daily self-esteem. The items were: “I am satisfied with myself,” “I am able to do things as well as most other people,” “I take a positive attitude towards myself,” “I certainly feel useless” (reverse coded), “I feel that I am a failure” (reverse coded). Participants were instructed to rate the items with regard to their feelings on the current day. Responses were measured on a 5-point scale ranging from 1 (strongly disagree) to 5 (strongly agree). The alpha reliability was .90, averaged across daily assessments. Daily events. In the daily assessments, participants reported the occurrence of positive and negative events in 10 domains: marriage/relationship; family; friends; neighbors; work; coworkers; recreational activities; traffic and shopping; finances; health and physical well-being. As suggested by Butler et al. (1994), we aggregated the items into an overall daily event measure by subtracting the number of negative events (possible values ranging from 0 to 10) from the number of positive events (possible values ranging from 0 to 10). Thus, possible values of the dai y e ent measure ranged rom

to

.2

Computing measures of self-esteem instability and self-esteem contingency. Self-esteem instability. For each participant, instability of self-esteem was computed as the intraindividual standard deviation of daily self-esteem across daily assessments. The intraindividual standard deviation is the most widely used measure of instability of self-esteem (Kernis & Goldman, 2006). Self-esteem contingency (statistical index). In addition to the subjective measure of selfesteem contingency (i.e., the Others’ Approval subscale of the CSW), we used a more objective measure of self-esteem contingency. Using the daily diary data, we computed for each


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participant a statistical index of self-esteem contingency, which captures the degree to which the participant’s daily self-esteem fluctuates in response to events occurring on the same day. This approach has been employed previously for measures of affect and self-esteem (Meier, Orth, Denissen, & Kühnel, 2011), although different labels such as lability (Butler et al., 1994) and reactivity (Bolger & Zuckerman, 1995; L. H. Cohen, Gunthert, Butler, O'Neill, & Tolpin, 2005; Mroczek & Almeida, 2004) have been used. Because of the multilevel structure of the data (daily assessments nested within persons), we used a multilevel random coefficient model allowing for simultaneous modeling of random error at different levels of analysis (Nezlek, 2001). For the analyses, we used the Mplus 6 program (Muthén & Muthén, 2010). The daily event measure was centered on the group mean and the intercept and slope were allowed to correlate. The Level 1 equation was of the form:

daily self-esteemij = β0j

β1j (daily events) + rij

(1)

and the Level 2 equations were of the form:

β0j =

00

0j

(2)

β1j =

10

1j.

(3)

The Level 1 slope of daily events predicting daily self-esteem, β1j, represents the individual contingency of self-esteem. The participants’ scores on the slope were saved and used in the subsequent analyses.


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Procedure for the statistical analyses. The analyses were conducted using Mplus 6. To deal with missing values, we employed full-information maximum likelihood to fit models directly to the raw data, which produces less biased and more reliable results compared with conventional methods of dealing with missing data, such as listwise or pairwise deletion (Allison, 2003; Schafer & Graham, 2002). Models including latent interactions were estimated by numerical integration using the default algorithm (i.e., rectangular integration) with 15 integration points. Model fit was assessed by the comparative fit index (CFI), the Tucker-Lewis index (TLI), and the root-mean-square error of approximation (RMSEA), based on the recommendations of Hu and Bentler (1999) and MacCallum and Austin (2000). Hu and Bentler (1999) suggested that that good fit is indicated by values greater than or equal to .95 for TLI and CFI and less than or equal to .06 for RMSEA. To test for differences in model fit, we used the test of small difference in fit recommended by MacCallum, Browne, and Cai (2006, Program C). For these tests, statistical power was high, with values above .99 (MacCallum et al., 2006, Program D). Results and Discussion Table 2 shows means and standard deviations of the measures used in Study 1. In the analyses, the multi-item measures (i.e., self-esteem level, self-reported contingency of selfesteem, depression, and the Big Five personality traits) were examined as latent variables; for these measures, we used item parcels as indicators because they produce more reliable latent variables than individual items (Little, Cunningham, Shahar, & Widaman, 2002; following the recommendations by Little et al. we randomly aggregated the items into three parcels). First, we tested models that did not include any interactions between self-esteem level, instability, and contingency. For the analyses, we used a cross-lagged regression model (Finkel,


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1995; Little et al., 2007). The model included eight construct variables: five constructs measured at Time 1 and three constructs at Time 2 (see Figure 1; self-esteem instability and the statistical index of self-esteem contingency were not measured at Time 2). All construct variables at Time 2 were predicted by all construct variables at Time 1. Thus, the model included stability paths (also called autoregressive paths; e.g., the path from self-esteem level at Time 1 to self-esteem level at Time 2) and cross-lagged paths (e.g., the path from self-esteem level at Time 1 to depression at Time 2). The cross-lagged paths indicate the effect of one variable on the other, after controlling for the stability of the variables over time (Finkel, 1995). For constructs that were assessed on both measurement occasions, the uniquenesses of individual indicators were correlated over time to control for bias due to parcel-specific variance (Cole & Maxwell, 2003). We tested for metric measurement invariance of the latent construct factors (Widaman, Ferrer, & Conger, 2010) by comparing the fit of two models: in Model 1 we freely estimated the factor loadings of the latent constructs, whereas in Model 2 we constrained the factor loadings to be equal across time. If the constrained model does not fit worse than the unconstrained model, then the constraints are empirically justified and ensure that the latent variables are measured similarly across time. The difference in fit between Models 1 and 2 was nonsignificant. Consequently, we favored the more parsimonious Model 2 and retained the metric invariance constraints in the subsequent analyses. The fit of Model 2 was good (Table 3). Then, we tested whether there were—in addition to the main effects examined in the previous analyses—interactive effects of the self-esteem characteristics on depression. Because interactions that involve latent variables significantly increase the computational demands (each interaction adds two dimensions of integration), the interactions were examined in separate models; thus, we examined six models, each of which included one interaction between two of


21

the four self-esteem variables (i.e., level, instability, self-reported contingency, and statistical index of contingency). The results showed that none of the interaction effects were significant.3 Thus, the results suggested that the self-esteem characteristics did not interact in predicting subsequent depression. We therefore examined the structural coefficients for Model 2, which did not include interactive effects (Figure 1). Only two significant cross-lagged effects emerged. First, self-esteem level had a negative effect on depression, corresponding to the available evidence on the vulnerability model for low self-esteem and depression (Sowislo & Orth, in press). Second, self-esteem contingency, as measured by the statistical index, predicted a decrease in level of self-esteem. All other cross-lagged effects were nonsignificant: thus, selfesteem instability and the two measures of self-esteem contingency did not predict change in depression, controlling for the effect of self-esteem level and controlling for prior levels of depression. The stability coefficients were .69 for level of self-esteem, .78 for self-reported contingency of self-esteem, and .34 for depression, comparable to the stabilities reported in the literature (Lovibond, 1998; Sowislo & Orth, in press; Trzesniewski, Donnellan, & Robins, 2003).4 We also tested for gender differences in the structural coefficients using a multiple group analysis. However, a model allowing for different coefficients for female and male participants did not significantly improve model fit relative to a model with constraints across gender. For both female and male participants, the estimates were similar to the estimates for the total sample. Next, we controlled for content overlap between the measures of self-esteem level and depression by repeating the analyses after omitting two items from the CES-D that are conceptually related to self-esteem (“I felt that I was just as good as other people” and “I thought my life had been a failure”). The correlation between this abbreviated 18-item CES-D and the


22

full 20-item CES-D was above .99 at every assessment. Model fit was good (see Model 3 in Table 3) and, not surprisingly given their strong convergence, the estimates for the structural model using the 18-item CES-D were similar to the estimates for the model using the full CES-D displayed in Figure 1. We also tested whether self-esteem contingency had a curvilinear, U-shaped effect on depression, corresponding to the assumptions of sociometer theory. The model included 10 construct variables, that is, the eight construct variables included in Model 2 and quadratic terms of the two contingency variables (i.e., the self-report and the statistical index).5 As in Model 2, level of self-esteem had a negative effect on depression (β = -.23, p < .05). However, neither the quadratic term of the self-report (β = -.08; p = .11) nor the quadratic term of the statistical index (β =.07; p = .28) had a significant effect on depression. Finally, we tested whether the vulnerability effect of low self-esteem on depression holds when the Big Five personality traits are controlled for. For this purpose, we tested a cross-lagged regression model, which included 7 constructs: self-esteem level, depression, extraversion, agreeableness, conscientiousness, neuroticism, and openness to experience, at both Time 1 and Time 2. As in the models reported above, level of self-esteem had a negative prospective effect on depression (β = -.23, p < .05). However, none of the Big Five personality traits significantly predicted subsequent depression, with standardized regression coefficients ranging from -.03 (p = .55) for openness to .07 (p = .44) for neuroticism. Given that, in particular, neuroticism has been discussed as a third variable that may account for the vulnerability effect of low selfesteem, we also tested a model that just included self-esteem level, depression, and neuroticism. Again, however, self-esteem level had a significant effect on depression (β = -.22, p < .05), whereas neuroticism did not signi icant y predict depression (β = .

, p = .37).


23

The results of Study 1 suggest that (a) only level of self-esteem, but not instability and contingency of self-esteem, predicts subsequent depression; (b) this result holds for both the selfreport measure and the statistical index of self-esteem contingency; (c) the pattern of results holds across gender; (d) the results are not influenced by content overlap between the measures of self-esteem level and depression; (e) the vulnerability effect of low self-esteem on depression is not influenced by interactions with instability and contingency of self-esteem; and (f) the vulnerability effect of low self-esteem on depression holds when neuroticism and all other Big Five personality traits are controlled for. To cross-validate the findings, we replicated the analyses using a second data set. Study 2 differed from Study 1 in three major characteristics. First, in Study 2 we examined a sample of young adults. The developmental period of young adulthood is particularly important to understand the etiology of depression because the prevalence of depression is high during this period (Blazer, Kessler, McGonagle, & Swartz, 1994) and because self-esteem and depression are likely to show changes due to the many transitions that occur in young adulthood (cf. Erol & Orth, 2011; Mirowsky & Kim, 2007; Orth, Trzesniewski, & Robins, 2010). Second, the data for Study 2 were collected in the work context, using a sample of trainees; moreover, in Study 2 a different type of daily event was assessed: specifically, work-related positive and negative events, which could be used for computing the statistical index of self-esteem contingency. Third, the study design included diary assessments at both Time 1 and Time 2, which enabled us to compute statistical indices of self-esteem instability and contingency on both measurement occasions. Consequently, Study 2 allowed testing for reciprocal relations between these characteristics of self-esteem and the other measures included in the model. Study 2


24

Method Data came from the Trainee Diary Study (TDS), a German-language study with a sample of trainees from a large Swiss company (Orth, Robins, & Meier, 2009).6 Participants were assessed on trait measures of self-esteem and depression on two occasions separated by 6 weeks (denoted as Time 1 and Time 2). Moreover, during the first 12 workdays after each of the trait assessments at Time 1 and Time 2, participants were assessed using short diary questionnaires including measures of state self-esteem and daily events. Data were collected using Web-based questionnaires that were accessible only to individuals who were invited to participate. Participants received information on the purpose and procedure of the study and were informed that their data would be treated as strictly confidential. Because most of the trainees had to attend school on some of the weekdays, six daily reports per assessment period (i.e., Time 1 and Time 2) were expected for each participant. However, for practical reasons, participants received emails providing access to the questionnaire on every weekday; therefore, the maximum number of daily reports per assessment period was 12. The average number of daily reports was 5.2 at Time 1 and 7.2 at Time 2. The daily diary assessments were conducted at 11.30 a.m. After completion of the study, participants were provided with individualized feedback on selected study variables (i.e., how their scale scores compared with the mean score of the sample) and participated in a raffle (in which they could win a portable media player and several audio compact disks) in exchange for participation in the study. Participants. The sample consisted of 253 trainees (36% female). Mean age of participants at Time 1 was 18.0 years (SD = 1.3, range = 16 to 23). Data were available for 222 individuals at Time 1 and for 185 individuals at Time 2. To investigate the potential effect of attrition, we tested for differences on study variables between participants who completed the


25

Time 2 assessment and participants who had dropped out before Time 2. No significant differences emerged. Trait measures. Self-esteem level. As in Study 1, self-esteem level was assessed with the RSE. Responses were measured on a 6-point scale ranging from 0 (strongly disagree) to 5 (strongly agree). The alpha reliability was .86 at Time 1 and .89 at Time 2. Self-esteem contingency (self-report). As in Study 1, we used the Others’ Approval subscale of the CSW. Responses were measured on a 6-point scale ranging from 0 (strongly disagree) to 5 (strongly agree). The alpha reliability was .81 at both Time 1 and Time 2. Depression. Depression was assessed with the German 15-item short form of the CES-D (Hautzinger & Bailer, 1993). Participants were instructed to assess the frequency of their reactions within the preceding seven days. Responses were measured on a 4-point scale (0 = rarely or none of the time, 1 = sometimes, 2 = frequently, 3 = most of the time). The alpha reliability was .92 at both Time 1 and Time 2. On the basis of the recommended cutoff value of 17 (Hautzinger & Bailer, 1993), 17% of participants at Time 1 and 20% of participants at Time 2 exhibited a clinically relevant level of depressive symptoms. Daily measures. Daily self-esteem. As in Study 1, daily self-esteem was assessed with five items from the RSE, which were adapted to measure daily self-esteem. The items were: “I take a positive attitude towards myself,” “I am satisfied with myself,” “I feel that I’m a person of worth,” “I certainly feel useful,” “I have respect for myself.” Participants were instructed to rate the items with regard to their feelings at the present moment. Responses were measured on a 6-point scale


26

ranging from 0 (strongly disagree) to 5 (strongly agree). The alpha reliability was .94 at Time 1 and .95 at Time 2, averaged across daily assessments. Daily events. In the daily assessments, participants reported the occurrence of events at the workplace, using a 12-item scale including six positive events and six negative events. Item examples are: “I completed an important task,” “I was able to help another person in an important matter,” “I made a mistake that will have consequences,” and “I was left alone in a difficult situation.” We aggregated the items into an overall daily event measure by subtracting the number of negative events (possible values ranging from 0 to 6) from the number of positive events (possible values ranging from 0 to 6). Thus, possible values of the overall daily event measure ranged rom

to

.

Computing measures of self-esteem instability and self-esteem contingency. The statistical indices of self-esteem instability and self-esteem contingency were computed using the same procedures as in Study 1. Procedure for the statistical analyses. The analyses were conducted using Mplus 6. We used the same procedures as in Study 1. For the tests of small difference in fit, statistical power was high with values above .99 (MacCallum et al., 2006, Program D). Results and Discussion Table 4 shows means and standard deviations of the measures used in Study 2. The models tested were identical to Study 1, except that the Study 2 models included the statistical indices of self-esteem instability and contingency at both Time 1 and Time 2. Again, we first tested models that did not include interactions between the measures of self-esteem level, instability, and contingency. In Model 1, we freely estimated the factor loadings of the latent constructs and in Model 2 we constrained the factor loadings to be equal across time, imposing


27

metric measurement invariance. Models 1 and 2 did not differ significantly in fit. Consequently, we favored the more parsimonious Model 2 and retained the metric invariance constraints in the subsequent analyses. Although the fit values for TLI and RMSEA were slightly worse than the normative values specified by Hu and Bentler (1999), we judged the fit of Model 2 to be satisfactory overall (Table 5). Then, we tested whether there were interactive effects between the self-esteem characteristics on depression. However, the results showed that none of the interaction effects were significant.7 Thus, the results suggested that the self-esteem characteristics did not interact in predicting subsequent depression, replicating the findings from Study 1. We therefore examined the structural coefficients for Model 2, which did not include interactive effects (Figure 2). Only three significant cross-lagged effects emerged. First, self-esteem level had a negative prospective effect on depression, corresponding to the vulnerability model of low self-esteem and depression (Sowislo & Orth, 2012). Second, self-esteem level predicted a decrease in self-reported contingency of self-esteem. Third, depression also predicted a decrease in self-reported contingency of self-esteem. All other cross-lagged effects were nonsignificant. Thus, self-esteem instability and the two measures of self-esteem contingency did not predict change in depression, controlling for the effect of self-esteem level and controlling for prior levels of depression, replicating the findings from Study 1.8 We also tested for gender differences in the structural coefficients, using a multiple group analysis.9 A model allowing for different coefficients for female and male participants, however, did not significantly improve model fit relative to a model with constraints across gender, replicating the findings from Study 1. For both female and male participants, the estimates were similar to the estimates for the total sample.


28

As in Study 1, we also tested for curvilinear, U-shaped effects of the two measures of self-esteem contingency on depression. However, neither the quadratic term of the self-report (β = .00; p = .94) nor the quadratic term of the statistical index (β =.03; p = .68) had a significant effect on depression.10 Finally, we controlled for content overlap between the measures of self-esteem level and depression by repeating the analyses after omitting one item from the CES-D that is conceptually related to self-esteem (“I thought my life had been a failure”). The correlation between this abbreviated 14-item CES-D and the full 15-item CES-D was above .99 at every assessment. Model fit was good (see Model 3 in Table 5) and the estimates for the structural model using the 14-item CES-D were similar to the estimates for the model using the full CES-D displayed in Figure 2. The only exception was that in this model depression was significantly predicted by self-esteem instability as well, over and above the effect of self-esteem level; however, the size of the instability effect (β = .15, p < .05) was much smaller than the size of the level effect (β = .38, p < .05). General Discussion In this research we examined the question of what constitutes vulnerable self-esteem or, more precisely, which characteristics of self-esteem put individuals at risk for depression. As reviewed in the introduction, previous research on the depressogenic effects of self-esteem level, instability, and contingency did not consider all three self-esteem characteristics simultaneously, suffered from low power, and yielded inconsistent results. The present research advances the field by investigating main and interactive effects of self-esteem level, instability, and contingency on depression in one overarching model, using data from two independent longitudinal studies. The results from both studies suggest that only level, but not instability and


29

contingency, of self-esteem predicts subsequent depression. Also, the three self-esteem characteristics did not interact in the prediction of depression. Moreover, the effect of selfesteem level on depression held when controlling for neuroticism and for all other Big Five personality traits. Thus, the findings suggest that low self-esteem, but not unstable and contingent self-esteem, is a risk factor for depression. Next, we discuss these findings in more detail. Implications of the Findings The results of both studies support the vulnerability model of low self-esteem and depression, which states that low self-esteem is a risk factor for depression (Orth et al., 2008; Sowislo & Orth, in press; Zeigler-Hill, 2011). In both studies, low self-esteem prospectively predicted depression (controlling for prior levels of depression and instability and contingency of self-esteem). Across both studies, the effect was of about medium size, according to the conventions suggested by J. Cohen (1988). In contrast, the results of both studies do not support alternative models of vulnerable self-esteem. First, vulnerability to depression was not a function of self-esteem instability (i.e., the degree of variability in self-esteem across short periods). Second, vulnerability to depression was not a function of self-esteem contingency (i.e., the degree to which self-esteem fluctuates in response to self-relevant events).11 Although we failed to find support for the latter two models, we believe that these results provide a significant contribution to the field, given the widespread belief in the validity of the models; many researchers have argued that it is important for scientific fields to publish null results when there is a clear rationale for the hypothesis and the research is well conducted and has sufficient statistical power (Cooper, DeNeve, & Charlton, 1997; Fraley & Marks, 2007; Greenwald, 1975; Nosek & Bar-Anan, 2012).


30

Moreover, we did not find any significant interactions between level, instability, and contingency of self-esteem. As reviewed in the introduction, previous tests of this hypothesis have yielded inconsistent results, which may have been due to small sample sizes. The samples used in the present research provided sufficient statistical power to detect medium (Study 2) and even small (Study 1) interaction effects (J. Cohen et al., 2003). In addition, the combined statistical power to detect an interaction effect in at least one of the two studies was even larger compared with the power of each individual study; nevertheless, in both studies, all interactions between self-esteem level, instability, and contingency were nonsignificant. Thus, the results suggest that the vulnerability effect of low self-esteem on depression holds across different degrees of self-esteem instability and contingency. We also tested for a curvilinear, U-shaped relation between self-esteem contingency and depression, as sociometer theory suggests that a medium degree of contingency is optimal for psychological adjustment (Leary, 2004). As discussed in the Introduction, the theory proposes that both a hypersensitive sociometer (i.e., a person’s self-esteem contingency is overly high) and a hyposensitive sociometer (i.e., a person’s self-esteem contingency is overly low) interferes with adaptive regulation of social interactions, which in turn could increase the risk for depression. However, in both studies, the results did not suggest that there is a curvilinear relation between self-esteem contingency and vulnerability to depression. A possible explanation for the nonsignificant result is that depression is a relatively distal outcome of a miscalibrated sociometer; in this case, curvilinear effects of self-esteem contingency on depression could emerge when examining longer prospective time intervals (e.g., several years). However, selfesteem contingency could have more immediate curvilinear effects on more proximal outcomes


31

of a miscalibrated sociometer, such as negative social reactions, decreased popularity among peers, and withdrawal by relationship partners. The present results provide further important information on the robustness of the vulnerability effect of low self-esteem on depression, given that the effect held when we controlled for the effects of the Big Five personality traits. With regard to the link between low self-esteem and depression, in particular, neuroticism might be a third variable causally influencing both self-esteem and depression and thereby creating a spurious relation between the constructs (Hankin et al., 2007; S. B. Roberts & Kendler, 1999; Watson et al., 2002). However, the present results clearly suggest that the vulnerability effect of low self-esteem is not due to a confounding influence of neuroticism or of any of the other Big Five personality traits. Moreover, as in previous studies (e.g., Orth et al., 2008; Orth, Robins, Trzesniewski, et al., 2009; Sowislo & Orth, in press), the vulnerability effect of low self-esteem on depression held for both men and women. Of course, men and women may differ in their average levels of self-esteem and depression (Hyde, Mezulis, & Abramson, 2008; Kling, Hyde, Showers, & Buswell, 1999; Major, Barr, Zubek, & Babey, 1999). However, the findings suggest that the structural relations between self-esteem and depression are not influenced by gender. Thus, the present research adds to the cumulative evidence with regard to the robustness of the vulnerability effect of low self-esteem. Although the present research suggests that instability and contingency of self-esteem are no vulnerability factors for depression, we note that research supports the validity of the constructs and their utility in other fields of research. For example, several studies have supported the discriminant validity of the constructs, suggesting that the correlations among level, instability, and contingency of self-esteem are of only small to medium size (e.g.,


32

Cambron et al., 2010; Crocker et al., 2003; Knee et al., 2008; Meier et al., 2011; Okada, 2010; Oosterwegel et al., 2001; J. E. Roberts & Kassel, 1997) and that the correlational pattern holds across the life span (Meier et al., 2011). Moreover, research suggests that instability of selfesteem is distinct from instability in positive and negative affect (e.g., Oosterwegel et al., 2001; J. E. Roberts & Gotlib, 1997; J. E. Roberts & Kassel, 1997) and distinct from neuroticism (e.g., J. E. Roberts & Gotlib, 1997; J. E. Roberts et al., 1995). Furthermore, instability and contingency of self-esteem show predictive validity—over and above the effect of level of self-esteem—with regard to anger (Kernis, Grannemann, & Barclay, 1989), aggressive behavior (e.g., Webster, Kirkpatrick, Nezlek, Smith, & Paddock, 2007), verbal defensiveness (Kernis, Lakey, & Heppner, 2008), and supportiveness (Park & Crocker, 2005). In this research, we used two divergent approaches to measure self-esteem contingency. In addition to a commonly used self-report measure of contingent self-esteem (i.e., the Others’ Approval subscale from the CSW; Crocker et al., 2003), we computed a statistical index of contingency which captures interindividual differences in the intraindividual effect of daily events on daily self-esteem. We decided to use this nonreactive and more objective approach because the self-assessment of contingent self-esteem is likely a complex cognitive task and people might not be fully aware of their contingencies. Although cognitive psychology suggests that people are able to accurately judge contingencies in many situations (cf. Allan, 1993), in some situations the ability to accurately perceive and report contingencies is significantly reduced. First, at least some forms of contingency are learned without conscious awareness and, consequently, information about contingencies may not be consciously accessible (Custers & Aarts, 2011). Second, contingency judgments are frequently distorted, for instance by density bias (Alloy & Abramson, 1979; Dickinson, Shanks, & Evendena, 1984), base rate neglect


33

(Tversky & Kahneman, 1982), or the pseudocontingency illusion (Fiedler, Freytag, & Meiser, 2009). With regard to self-esteem, research suggests that people have difficulty in judging the contingencies of their self-esteem. For example, Leary et al. (2003) showed that the event of social disapproval clearly affected the self-esteem of even those individuals who believed that evaluations by others do not influence their self-esteem. With regard to the present research, the convergence of the findings across the self-report measure and the objective measure strengthens the conclusion that contingent self-esteem is not a risk factor for depression. Limitations and Future Research A limitation of the present research is that the study designs do not allow for strong conclusions regarding the causal influence of self-esteem on depression. As in all passive observational designs, observed effects may be caused by third variables that were not controlled for (Finkel, 1995; Little et al., 2007). However, the longitudinal models employed in this research are useful because they can indicate whether the data are consistent with a causal model of the relation between the variables, by establishing the direction of the effects and ruling out some (but not all) alternative causal hypotheses. Moreover, as reported above, Study 1 enabled us to partially overcome this limitation by statistically controlling for possible third-variable effects of broad personality factors such as neuroticism (e.g., Hankin et al., 2007; Watson et al., 2002). The findings suggest that the vulnerability effect of low self-esteem on depression is not due to confounding effects of the Big Five personality traits. Thus, the present research strengthens the case for the vulnerability model of low self-esteem. Nevertheless, future research should test other theoretically-relevant third-variable models that might account for the vulnerability effect of low self-esteem.


34

Another limitation of the present research is that the results do not allow for firm conclusions with regard to clinical categories of depression such as major depressive disorder. First, the depression measure used in our research relies on self-report; however, conclusions about the antecedents of major depressive disorder should be based on clinical interviews. Second, we used nonclinical samples, which do not allow for valid conclusions about depressive episodes in clinical populations, even if a nontrivial proportion of the sample experienced relatively high levels of depression. However, given that meta-analytic results suggest that the prospective effect of low self-esteem on depression holds in both clinical and nonclinical samples (Sowislo & Orth, in press), we believe that the present findings are relevant also for clinically significant levels of depression. A strength of the present research is the convergence of findings across the two studies, despite different study characteristics, which increases confidence in the generalizability of the findings. The studies differed in type of sample (relationship partners in Study 1 vs. trainees in Study 2), age of participants (i.e., adults from 18 to 61 years in Study 1 vs. young adults in Study 2), prospective time interval (six months in Study 1 vs. six weeks in Study 2), and type of events used in the statistical index of self-esteem contingency (a broad set of events in Study 1 vs. workplace events in Study 2). The present results suggest that low self-esteem, but not unstable and contingent selfesteem, is a risk factor for depression, raising the important question of why low self-esteem predicts increases in depression over time. Knowledge about mediating processes that account for the vulnerability effect of low self-esteem is of crucial importance because it provides possible starting points for interventions aimed at preventing or reducing depression. As outlined in the introduction, the vulnerability effect of low self-esteem on depression might operate via


35

several interpersonal and intrapersonal pathways. As yet, only one study has tested for mediators of the vulnerability effect of low self-esteem and found that low self-esteem increased the tendency to ruminate about negative experiences, which in turn increased depression (Kuster et al., 2012). However, given that rumination only partially mediated the depressogenic effect of low self-esteem, it is likely that further mediating processes are involved. Future research should therefore continue to examine the mediating mechanisms that explain the vulnerability effect of low self-esteem on depression. Moreover, although previous research suggests that the vulnerability effect of low selfesteem on depression is not influenced by the presence vs. absence of stressful life circumstances (Orth, Robins, & Meier, 2009) and although the present research indicates that the effect is not moderated by instability and contingency of self-esteem, future research should continue to test for factors that moderate the strength of the vulnerability effect. For example, social support by relationship partners, family, and friends might protect individuals with low self-esteem from spiraling downward into depression. Gaining further insight into these moderating processes would help to explain why some people with low self-esteem develop depression while others do not. Conclusion In conclusion, the present results contribute to the refinement of theory about vulnerable self-esteem, suggesting that a low level of self-esteem, but not instability and contingency of self-esteem, is a vulnerability factor for depression. If future research confirms the causal link between self-esteem level and depression, these findings have implications for work in clinical and counseling settings. To assess vulnerable self-esteem, it may be unnecessary to examine the extent and causes of fluctuations in self-esteem over multiple assessments, but it may be crucial

SELF-ESTEEM AND DEPRESSION to assess the overall level of self-esteem. Moreover, when focusing on self-esteem in the prevention and treatment of depression, interventions should primarily seek to increase a person’s level of self-esteem rather than focus on instability and contingency of self-esteem. Nevertheless, future investigations of vulnerable self-esteem are needed to gain a thorough understanding of the processes by which low self-esteem contributes to vulnerability to depression.

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55 Footnotes

1

Throughout this article, we use the term depression to denote a continuous variable (i.e.,

individual differences in depressive symptoms) rather than a clinical category such as major depressive disorder (American Psychiatric Association, 2000). Taxometric analyses suggest that depression is best conceptualized as a continuous construct (Hankin, Fraley, Lahey, & Waldman, 2005; Lewinsohn, Solomon, Seeley, & Zeiss, 2000; Prisciandaro & Roberts, 2005; Ruscio & Ruscio, 2000). 2

We did not compute the alpha reliability for the daily event measures used in Studies 1

and 2. Coefficient alpha is not an appropriate measure of reliability for these scales because they are emergent, not latent, constructs, defined by an aggregation of relatively independent indicators (Bollen & Lennox, 1991; Streiner, 2003). 3

Because of the exploratory character of the six interaction tests, we adjusted the

significance level to p < .008, following the Bonferroni method (i.e., dividing .05 by 6). 4

In addition to the Others’ Approval subscale of the CSW, the MPI includes two other

self-report measures of contingent self-esteem, specifically self-esteem contingency in the domains of job performance (adapted from the “Academic Competence” subscale of the CSW) and marriage/close relationship (adapted from the “Family Support” subscale of the CSW). Both measures included 5 items and the alpha reliabilities ranged from .71 to .79 across the two waves. When we replicated the analyses using these measures, the results were virtually identical to the results for the Others’ Approval subscale and did not lead to different conclusions: only level, but not instability and contingency of self-esteem, predicted subsequent depression. Thus, the results suggest that domain-specific contingencies of self-esteem are no vulnerability factor for depression, controlling for level of self-esteem.

SELF-ESTEEM AND DEPRESSION 5

56

The variables self-reported contingency and statistical index of self-esteem contingency

were centered for the analyses. 6

Orth, Robins, and Meier (2009, Study 2) used the data of the TDS to examine the

relation between level of self-esteem and depression; however, that study did not examine whether instability and contingency of self-esteem predict depression or whether instability and contingency interact with level of self-esteem in the prediction of depression. 7

As in Study 1, because of the exploratory character of the six interaction tests, we

adjusted the significance level to p < .008, following the Bonferroni method (i.e., dividing .05 by 6). 8

As in Study 1, in addition to the Others’ Approval subscale of the CSW, the TDS

includes two other self-report measures of contingent self-esteem, specifically self-esteem contingency in the domains of physical attractiveness (i.e., the “Appearance” subscale of the CSW) and job performance (adapted from the “Academic Competence” subscale of the CSW). Both measures included 5 items and the alpha reliabilities ranged from .71 to .79 across the two waves. When we replicated the analyses using these measures, the results were virtually identical to the results for the Others’ Approval subscale and did not lead to different conclusions: only level, but not instability and contingency of self-esteem, predicted subsequent depression. Thus, the results suggest that domain-specific contingencies of self-esteem are no vulnerability factor for depression, controlling for level of self-esteem. 9

Due to the restricted sample size (i.e., n = 80 in the female group), the results of the

multiple group analysis testing for gender differences in the structural coefficients must be treated with caution. Although there are no valid rules of thumb regarding sample size in structural equation modeling (MacCallum & Austin, 2000; MacCallum, Widaman, Zhang, &


57

Hong, 1999), estimating complex models with less than 100 cases is generally problematic (Kline, 1998). 10

As in Study 1, the variables self-reported contingency and statistical index of self-

esteem contingency were centered for the analyses. 11

As reported above, there was one significant effect for instability of self-esteem. In

Study 2, in the model in which we controlled for content overlap between self-esteem level and depression, instability predicted an increase in depression. However, given that this effect emerged in only one of the studies and given that the effect size was small, a conservative interpretation of the pattern of results across both studies is that self-esteem instability is not an important vulnerability factor for depression.

SELF-ESTEEM AND DEPRESSION Table 1

58

Summary of Previous Studies Simultaneously Testing the Effects of Level and Instability of Self-Esteem on Subsequent Depression,

Roberts et al. (1999)

Roberts & Gotlib (1997)e,g

Kim & Cicchetti (2009)d,e

Kernis et al. (1998, CES-D)c

Kernis et al. (1998, BDI)c

Franck & De Raedt (2007)b

Butler et al. (1994, Study 2)a

Study

120

26

122

215

98

98

52

73

N

57

65

100

36

86

86

78

77

% female

not available

11 weeksh

6 weeks

1.5 yearsf

4 weeks

4 weeks

6 months

5 months

time interval

Prospective

−

−

Ns

−

Ns

Ns

Ns

Ns

of SE level

Main effect

ns

+

ns

ns

ns

+

+

+

SE instability

Main effect of

ns

+

not tested

not tested

+

ns

ns

ns

level and instability

Interaction effect of SE

Controlling for Prior Depression

Vickery et al. (2009)e,i,j

Note. SE = self-esteem; BDI = Beck Depression Inventory; CES-D = Center for Epidemiologic Studies-Depression Scale; “+” denotes

Effects were controlled for a measure of life stress and additional interactive effects. b Effects were controlled for history of

a significant positive effect; “−” denotes a significant negative effect; “ns” denotes a nonsignificant effect. a

depression (i.e., never depressed vs. formerly depressed). c Effects were controlled for a measure of daily hassles and additional


59

interactive effects. d Effects were controlled for maltreatment status and additional interactive effects. e Self-esteem level was

operationalized as average across daily assessments. f Information on the predictors were based on aggregations of repeated

measurements over four occasions (with 1-year intervals). Depression on the forth occasion served as the outcome variable. We thus

used the mean prospective time interval. g Effects were controlled for anxiety at Times 1 and 2, severity of lifetime depression,

neuroticism, competence, life stress, and additional interactive effects. h The exact prospective time interval was not given. However,

we estimated the time interval based on the following information: Predictors were based on mean scores of an intake assessment,

which was conducted between one and three weeks prior to the first treatment session and an assessment at the first treatment session.

The treatment took eight weeks (see Lewinsohn, Antonuccio, Steinmetz, & Teri, 1984). The outcome was based on mean scores of the

final treatment session and the follow-up, which was conducted between one and three weeks after the final treatment session. i

Vickery, Evans, Sepehri, Jabeen, & Gayden (2009). j Effects were controlled for hospitalization-based hassles.


60

Table 2 Means and Standard Deviations of Measures in Study 1 Time 1 Variable

Time 2

M

SD

M

SD

SE level

4.07

0.75

4.17

0.75

SE instability

0.37

0.22

--

--

SE contingency (self-report)

2.71

0.88

2.73

0.90

SE contingency (statistical index)

0.07

0.05

--

--

Depression

0.60

0.43

0.56

0.43

Extraversion

3.46

0.72

3.50

0.69

Agreeableness

3.59

0.54

3.62

0.53

Conscientiousness

3.59

0.66

3.67

0.60

Neuroticism

2.77

0.75

2.72

0.74

Openness to experience

3.74

0.66

3.73

0.65

Note. Dash indicates that data were not available. SE = self-esteem.


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Table 3 Fit of the Models Tested in Study 1 Model

2

df

TLI

CFI

RMSEA [90% CI]

1. Full CES-D, free loadings

276.5*

135

.96

.97

.053 [.044, .062]

2. Full CES-D, metric invariance

284.7*

141

.96

.97

.052 [.044, .061]

3. CES-D controlled for content overlap

310.0*

141

.96

.97

.057 [.048, .065]

Note. TLI = Tucker-Lewis index; CFI = comparative fit index; RMSEA = root-mean-square error of approximation; CI = confidence interval; CES-D = Center for Epidemiologic Studies Depression Scale. * p < .05.


62

Table 4 Means and Standard Deviations of Measures in Study 2 Time 1 Variable

Time 2

M

SD

M

SD

SE level

3.78

0.86

3.74

0.90

SE instability

0.51

0.35

0.44

0.30

SE contingency (self-report)

2.77

1.10

2.75

1.07

SE contingency (statistical index)

0.14

0.04

0.11

0.07

Depression

0.68

0.56

0.72

0.59

Note. SE = self-esteem.


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Table 5 Fit of the Models Tested in Study 2 Model

2

df

TLI

CFI

RMSEA [90% CI]

1. Full CES-D, free loadings

300.2*

159

.94

.96

.061 [.051, .072]

2. Full CES-D, metric invariance

314.1*

165

.94

.95

.062 [.052, .072]

3. CES-D controlled for content overlap

304.4*

165

.94

.96

.060 [.049, .070]

Note. TLI = Tucker-Lewis index; CFI = comparative fit index; RMSEA = root-mean-square error of approximation; CI = confidence interval; CES-D = Center for Epidemiologic Studies Depression Scale. * p < .05.


Figure 1. Standardized structural coefficients for the “no interaction” model with longitudinal constraints (Model 2) estimated in Study 1. To keep the figure simple, only significant coefficients are shown (p < .05). Moreover, the figure omits indicators of latent variables and correlations of residual variances at Time 2. SE = self-esteem.

64


65

Figure 2. Standardized structural coefficients for the “no interaction” model with longitudinal constraints (Model 2) estimated in Study 2. To keep the figure simple, only significant coefficients are shown (p < .05; except for the Time 1 correlation between self-esteem instability and the self-report measure of self-esteem contingency, which was nonsignificant). Moreover, the figure omits indicators of latent variables and correlations of residual variances at Time 2. SE = self-esteem.

Fakultät für Psychologie

Julia F. Sowislo, Dipl.-Psych.

Missionsstrasse 62 CH-4055 Basel

Tel . +41 61 267 03 85 [email protected]

Basel, 15.07.2013

Selbstständigkeitserklärung Ich erkläre, dass ich die Dissertation „Testing alternative models of the relation between selfesteem and depression: A longitudinal approach” nur mit der darin angegebenen Hilfe verfasst und bei keiner anderen Universität und keiner anderen Fakultät der Universität Basel eingereicht habe.

Julia Friederike Sowislo