Examining the Relation between Personality ... - Wiley Online Library

European Journal of Personality, Eur. J. Pers. 30: 467–483 (2016)

Published online in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/per.2052

Generality or Specificity? Examining the Relation between Personality Traits and Mental Health Outcomes Using a Bivariate Bi-factor Latent Change Model WENTING MU*, JING LUO, LAUREN NICKEL and BRENT W. ROBERTS Department of Psychology, University of Illinois at Urbana-Champaign, USA

Abstract: Most previous research has focused on the relationships between specific personality traits and specific facets of mental health. However, in reality most of the Big Five are associated at non-trivial levels with mental health. To account for this broad correlation, we proposed the ‘barometer hypothesis’, positing that behind both ratings of mental health and personality lies a barometer that indicates one’s general feelings of positivity or negativity. To the extent that both the general factors of personality and mental health reflect this same barometer, we would expect them to be correlated. We tested alternative models using data from a large longitudinal panel study that includes two cohorts of participants who were assessed every two years, resulting in parallel 4-year longitudinal studies. Similar results were obtained across both studies. Supporting the ‘barometer hypothesis’, findings revealed that the optimal model included general latent factors for both personality traits and mental health. Compared to the broad raw pairwise correlations, the bi-factor latent change models revealed that the relation among levels and changes in the specific factors were substantially reduced when controlling for the general factors. Still, some relations remained relatively unaffected by the inclusion of the general factor. We discuss implications of these findings. Copyright © 2016 European Association of Personality Psychology Key words: personality; mental health; generality; specificity; bi-factor; change

Scientists and laypeople have long assumed that personality is related to mental health, but what is the nature of that link? In this case, by mental health, we refer to the broad spectrum that ranges from languishing on the low end to flourishing on the high end (Keyes, 2002). Although many studies argue that specific personality traits are the strongest predictors of different aspects of general mental health (e.g. Chow & Roberts, 2014), in reality most of the Big Five personality traits are associated at non-trivial levels with mental health. For example, psychological well-being was found to be strongly related to low neuroticism, high extraversion, conscientiousness, openness, and agreeableness (Kokko, Tolvanen, & Pulkkinen, 2013). The same pattern, where almost all the Big Five traits share covariance with different aspects of mental health, holds true for other mental health indicators, such as happiness (e.g. Gutiérrez, Jiménez, Hernández, & Puente, 2005), depression (e.g. Chow & Roberts, 2014), anxiety (e.g. Caspi et al., 2014), loneliness (Saklofske & Yackulic, 1989), and hopelessness (Velting, 1999). One challenge is how we should interpret the broad correlation between personality traits and mental health indicators. Most research to date has sought to interpret such relationships on the level of specific traits or mental health indicators. That is, researchers focus and speculate on the relationship between one trait and one mental health outcome at a time. For example, neuroticism has long been considered an important vulnerability factor that increases the risk for depression under stressful *Correspondence to: Wenting Mu, Department of Psychology, University of Illinois, 603 E. Daniel Street, Champaign, IL 61820, USA. E-mail: [email protected]

Copyright © 2016 European Association of Personality Psychology

conditions (Duggan, Sham, Lee, Minne, & Murray, 1995). In a recent study, neuroticism and major depressive disorder were explained about equally well by neuroticism polygenic scores, suggesting a significant genetic association between neuroticism and psychiatric disorders (De Moor et al., 2015, JAMA). Yet, despite the tendency to emphasize specific traits and specific aspects of mental health, there appears to be a strong signal across all the Big Five regardless of which aspect of mental health one investigates. This invites a set of questions seldom considered in past research. For example, do the specific relations emphasized in the literature, such as the association of neuroticism and depression, hold when the general underlying shared variance in either personality or mental health is objectively modelled? The present set of studies was driven by the observation that there is a tremendous amount of general shared variance between personality traits and mental health. Specifically, we address three research questions: (i) Is there a single factor underlying both the shared variance in personality traits and mental health? (ii) Do the relations between specific traits and specific components of mental health hold when the shared variance among the domains of personality and mental health are objectively assessed? And, (iii) do these patterns replicate across longitudinal analyses of the associations between changes in personality traits and changes in mental health? General factor of mental health Mental health diagnoses are highly correlated with one another not only at the disorder level, but also at the spectrum level (Hasin & Kilcoyne, 2012). The rule of 50% has been used to describe the situation: half of individuals who meet diagnosis Received 14 July 2015 Revised 12 January 2016, Accepted 26 January 2016

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for one disorder meet diagnostic criteria for a second disorder at the same time (Caspi et al., 2014). In trying to account for such high correlations among mental health diagnoses, researchers have tried to extract higher-order factors to account for the comorbidity observed among different mental health diagnoses. For example, using longitudinal data across over 20 years, Caspi et al. (2014) extracted one general factor that helped explain the cooccurrence and comorbidity of mental disorders. The general factor was found to be associated with greater impairment, worse developmental histories, and compromised early-life brain function. In a similar vein, Lahey et al. (2012) found that a general factor was able to capture the common variances among eleven DSM-IV diagnoses of mental disorders. The general factor accounted for significant independent variances with functioning and future psychopathology above and beyond individual diagnoses. Lahey et al. (2012) reviewed theories on the meaning of the general factor. First, the general factor might be an artifact of implicit theories people hold about mental illness. That is, the high correlations among different diagnoses may be because of systematic biases in reporting symptoms. In other words, people who experience one symptom may report other symptoms that they believe are relevant but do not actually experience, hence inflating the symptom correlations. Second, high correlations among diagnoses might be a result of individual differences in a tendency to portray oneself in generally negative terms. That is, the systematic bias in negative selfpresentation may inflate the correlations between the specific diagnoses. Third, certain personality traits may predispose individuals to develop a variety of mental problems at the same time. For example, neuroticism is found to be high in individuals with fear, distress, and externalizing psychopathology. This raises the possibility that the biopsychological processes underlying neuroticism may account for the correlations among the fear, distress as well as externalizing psychopathology. Fourth, it is also possible that the general factor may suggest the existence of some common etiologic factors that are shared by all mental disorders. It should be noted that traditional interest in examining the general factor of mental health often arises from the motivation to address the comorbidity issue among clinical diagnoses, and therefore has mostly examined the shared variances among various mental disorders of Axis I in the multi-axial system outlined in the Diagnostic and Statistical Manual (DSM; APA, 2000). Given our interest in the relationship between personality and psychological functioning, our conceptualization of mental health takes a broader view that focuses on global psychological functioning rather than just psychopathology. Historically it was a convention of clinical psychology to gauge mental health broadly using Axis V in the earlier versions of the DSM system (e.g. DSM-IV; APA, 2000). It was designed as a global measure of functioning within diagnoses and across all diagnoses to reflect the overall impairment of daily functioning caused by the mental disorders (Kennedy & Aas, 2013). Similarly, those who study well-being have argued that both psychopathology and wellbeing should be combined in a dimension that ranges from languishing through flourishing (Keyes, 2002). This argument Copyright © 2016 European Association of Personality Psychology

is based on both conceptual grounds as well as research showing substantial associations between typical measures of psychopathology, such as depression, and typical measures of well-being, such as life satisfaction (Ryff & Keyes, 1995). Based on these perspectives we not only included the most commonly used indicators of psychopathology (i.e. depression, anxiety), but also included indicators of social impairment (i.e. loneliness and hopelessness) and well-being (life satisfaction and purpose in life). Indeed, more and more research has began to focus on the positive side of mental well-being, both theoretically and empirically (e.g. Keyes, 2006; Kokko, Korkalainen, Lyyra, & Feldt, 2013; Lamers, Westerhof, Kovács, & Bohlmeijer, 2012). In sum, the current study approached mental health from the perspective of general functioning and included depression, anxiety, loneliness, hopelessness, purpose in life, and subjective well-being as indices for the latent factor of mental health. General factor in personality A similar line of empirical work has developed in personality psychology because, just as in the case of mental health, putatively disparate, independent personality traits tend to also be intercorrelated. For example, Digman (1997) analysed 14 sets of correlations among the Big Five personality traits and found a mean correlation of 0.26. Rushton proposed a general factor of personality (GFP) accounting for the common variance across specific personality traits (Rushton & Irwing, 2011). Using second-order models, they extracted the GFP across a variety of personality assessment scales (for a review, see Rushton, Bons, & Hur, 2008). Such findings have been consistently shown across diverse samples and procedures, and using multiple methods. For example, one study found that GFP was independent of method variance using a multi-trait-multi-method analysis of self, teacher, and parent ratings (Rushton et al., 2009). One influential way to look at the GFP is considering it as a dimension of social effectiveness (Rushton & Irwing, 2011). GFP is believed to arise through evolutionary selection for adaptive traits that facilitate performance across different life domains. Individuals high on GFP are characterized as ‘altruistic, agreeable, relaxed, conscientious, sociable, and open, with high levels of well-being and self-esteem’. Musek (2007) found that high scores on the GFP were related to self-esteem and positive affect and low scores to negative affect. However, just like the general factor of mental health, the general personality factor may arise because of peoples’ tendencies to portray themselves positively or negatively regardless of the specific content of the items being rated. We describe this hypothesis as the ‘barometer hypothesis.’ That is to say that behind both ratings of mental health and personality lies a barometer that indicates ones general feelings of positivity or negativity that reflect not the specific variance associated with either aspects of mental health or specific traits, but the general, global evaluation of life. To the extent that both the general factor of mental health and the GFP reflect this same barometer, then we would expect them to be correlated, if not share a single factor in common. It should be noted that the barometer hypothesis does not negate the idea that the GFP reflects social functioning. That said, within the context of Eur. J. Pers. 30: 467–483 (2016) DOI: 10.1002/per

Change or specificity? linking personality to mental health, we believe the barometer hypothesis makes fewer assumptions about the meaning of either the GFP or the general factor of mental health, and is therefore a more parsimonious starting point and hypothesis to test. Modelling common and unique variances simultaneously One particularly useful method to examine the structure of higher-order, multifaceted constructs is bi-factor models. The bi-factor model allows simultaneous testing of the association of an outcome with the general factor and the unique contributions of specific factors underlying the general factor. Bi-factor models have been used in recent research to study the structure of psychopathology, individual differences, as well as the association between personality traits and other important outcomes. It has been shown to have potential advantages over second-order models when researchers are interested in predicting external criteria (Chen, West, & Sousa, 2006). Bi-factor models have been increasingly applied to mental health-related constructs. One study examined the intercorrelations among 11 DSM-IV disorders (Lahey et al., 2012). They found a model with a general factor capturing covariance among eleven DSM-IV disorders outperformed a model specifying three correlated factors (i.e. externalizing, distress, and fear). Bi-factor models have also been applied to more specific constructs. For example, anxiety sensitivity was found to be best represented by a model comprised of a general dimension and three independent subfactors (Allan, Albanese, Short, Raines, & Schmidt, 2015). While the general factor was found to be most associated with emotional distress, specific dimensions of anxiety sensitivity provided utility in predicting different components of emotional distress. For other examples, the bi-factor models have also been shown to fit well for SCL-90-R Brief Symptom Inventory (Urbán et al., 2014), burnout, internet addiction (Watters, Keefer, Kloosterman, Summerfeldt, & Parker, 2013), and psychopathy (Patrick, Hicks, Nichol, & Krueger, 2007). Personality psychologists are also interested in studying the specific personality variances independent of the common traits (see McCrae, 2015 for a review). Bi-factor models have been employed to this end. In one study, when looking at the structure of extraversion, the bi-factor model fit better than the second-order model (Chen, Hayes, Carver, Laurenceau, & Zhang, 2012). Moreover, facets of extraversion correlated with criteria in opposite directions after partialling out the general factor of extraversion. Additionally, the general factor of extraversion was related to other variables—positive affect, negative affect, optimism, pessimism, satisfaction with life, self-esteem, and depressive symptoms. For other examples, bi-factor models were also found to fit better than second-order models when looking at the Dispositional Hope Scale (Gomez et al., 2015), the Rosenberg Self-Esteem Scale (Hyland, Boduscek, Dhingra, Shevlin, & Egan, 2014; McKay, Boduszek, & Harvey, 2014), and affect (Ebesutani et al., 2011; Leue & Beauducel, 2011). In addition to answering structural questions, bi-factor models are also used in research that is designed to examine other types of issues. For example, when analysing the self and informant agreement on personality traits, bi-factor models have also been employed to make Copyright © 2016 European Association of Personality Psychology

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traits statistically independent from one another (Mõttus, McCrae, Allik, & Realo, 2014). In the current study, we used the bi-factor model to explicitly model the shared variance among the Big Five as well as the shared variance among facets of mental health. This allowed us to clearly specify the shared variance among these factors in order to better understand the relation between personality traits and mental health. One possibility is that the prototypical correlation between a personality trait, such as neuroticism, and a specific mental health outcome such as well-being or depression, is actually attributable to the ‘barometer’ factor underlying all of personality, all of mental health, or both. By imposing the bi-factor model on the data of personality and mental health, we get a more rigorous test of whether the specific facets actually do predict specific mental health outcomes above and beyond the general positivity or negativity underlying the typical personality and mental health ratings.

Modelling change Prototypically, personality traits have been considered precursors to mental health outcomes. Though traditionally conceptualized as static predictors of outcomes, research suggests that personality traits change across the lifespan (e.g. Roberts, Walton, & Viechtbauer, 2006). Specifically, conscientiousness, agreeableness, and emotionally stability increase as people age, and that these trends continue into older adulthood (Roberts et al., 2006). The fact that personality traits can and do change invites the question of how changes in personality traits are associated with changes in mental health. Latent growth and change models have been used to study both change in personality traits over time, as well as how change in personality is related to other important outcomes, such as psychopathology. For example, one study found a positive correlation between change in neuroticism and change in depression (Chow & Roberts, 2014). Changes in neuroticism and impulsivity also appear to be related to changes in problematic alcohol use; specifically, a decrease in neuroticism and impulsivity is associated with a decrease in alcohol use (Littlefield, Sher, & Wood, 2009). Similar relationships may also exist between personality traits and symptoms of personality disorders. In a study of avoidant personality disorder, individuals who demonstrated a decrease in avoidant personality disorder symptoms also demonstrated an increase in dominance and affiliation and a decrease in neuroticism (Wright, Pincus, & Lenzenweger, 2012). Similar models have been used when looking at the relationship between personality and health. In a study of older men, the men who were high in neuroticism at the beginning of the study and increased in neuroticism over the course of the study experienced a higher risk of mortality than men who began the study low in neuroticism or men who decreased in neuroticism over time (Mroczek & Spiro, 2007). Changes in conscientiousness also appear to have an impact on health, such that changes in conscientiousness are positively related to both changes in preventative health-related behaviours and changes in self-rated physical health (Takahashi, Edmonds, Jackson, & Roberts, 2013). Eur. J. Pers. 30: 467–483 (2016) DOI: 10.1002/per

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Like much of the cross-sectional research linking personality traits to psychopathology, these longitudinal studies focused on relations between specific traits and specific forms of psychopathology. Given the availability of good longitudinal data, we sought to extend our research questions to the longitudinal context, in which we test the relations between change in personality traits and change in mental health using the bi-factor model in combination with a latent change model. The current study Given the aforementioned findings and insights, the current research tested alternative models accounting for the broad correlation observed between the Big Five personality traits and the mental health outcomes using data from a large longitudinal panel study. First, we tested whether a common factor model accounted for the shared variance among all of the Big Five and various indices of mental health. Second, we tested various alternative models in which the general factors specific to personality and mental health were specified. Once we triangulated on an acceptable bi-factor model, we tested whether specific personality traits were associated with specific aspects of mental health while controlling for the general factors associated with both personality traits and mental health. We then created a bivariate, longitudinal bi-factor latent change model to test whether the prior findings linking changes in mental health to changes in personality traits could be reproduced in the context of this model.

STUDY 1 We used data from the Health and Retirement Study (HRS), which is a large longitudinal panel study of a representative sample of Americans who are older than 50 and their spouses (Burkhauser & Gertler, 1995). Alternating cohorts of participants are assessed every two years, resulting in parallel 4year longitudinal studies. In Study 1, we used data collected in the eighth (2006) and tenth (2010) waves of assessment (for more information regarding the HRS, go to: http:// hrsonline.isr.umich.edu/). In study 2, we attempted to replicate the findings from Study 1 using the data collected during the 9th (2008) and 11th (2012) waves of assessment, which assessed the second cohort of participants. We selected these waves of assessment as they are the only datasets where both the personality and mental health variables were assessed to allow for examination of the relationship between the two. Sample In 2006, 6015 participants (Mage = 66.79, SD = 10.46, 58% female) had complete data for each of the measures used in the current study. Participants reported an average of 13.10 years of education. A total of 3864 participants (Mage = 65.16 SD = 9.61, 57.8% female) from the first wave had complete data in 2010 for each of the measures used. Participants reported an average of 13.34 years of education (SD = 2.62). The majority of our sample (88.6%) identified themselves as Copyright © 2016 European Association of Personality Psychology

White or Caucasian, 7.7% as African American, and 3.5% as other ethnicities. T-tests for independent samples revealed that individuals who did and did not complete assessment at Time 2 differed from each other in terms of age (d = .44), such that older adults were more likely to drop out of the study. Also, individuals with higher education were less likely to drop out of the sample (d = .24). Measures Personality. Personality was measured using the MIDUS Big Five Adjectival scale (Lachman & Bertrand, 2001), which has been found to have good reliability and construct validity (Mroczek & Kolarz, 1998; Zimprich, Allemand, & Lachman, 2012). Participants rated each adjective on a fourpoint scale (1 = a lot; 4 = not at all). The items in the Openness scale were adventurous, broad-minded, creative, curious, imaginative, intelligent, and sophisticated. The items in the Conscientiousness scale were organized, responsible, hardworking, careless (reverse-scored), and thorough. The items in the Extraversion scale were active, friendly, lively, outgoing, and talkative. The items in the Agreeableness scale were caring, helpful, softhearted, sympathetic, and warm. The items in the Neuroticism scale were moody, nervous, calm (reverse-scored), and worrying. Internal consistencies for the openness, conscientiousness, extraversion, agreeableness, and neuroticism scales in 2006 were α = .78, .67, .77, .79, and .71 respectively. Internal consistencies of these scales in 2010 were α = .79, .68, .76, .79, and .72 respectively. Mental health. The latent variable ‘mental health’ was comprised of depression, anxiety, life satisfaction, loneliness, hopelessness, and purpose in life. Depression. Depression was measured by a short version of the Center for Epidemiologic Studies-Depression scale (CESD; Chow & Roberts, 2014; Radloff, 1977). Participants were asked to rate their agreement to eight items (e.g. felt depressed) on a two-point scale (0 = no; 1 = yes). The CES-D has been found to have good psychometric properties with good utility in predicting concurrent depression (Hertzog, Van Alstine, Usala, Hultsch, & Dixon, 1990; Radloff, 1977). The internal consistencies were .76 in both 2006 and 2010. Anxiety. Anxiety was measured using five items (e.g. I had fear of the worst happening) selected from the widely used Beck Anxiety Inventory (BAI; Beck, Epstein, Brown, & Steer, 1988). Respondents were asked to rate the items on a four-point scale (1 = never; 4 = most of the time). BAI has been shown to distinguish symptoms of anxiety from depression and to be valid for use in older populations (Beck et al., 1988; Wetherell & Areán, 1997). Internal consistencies of the Anxiety scales were .79 in 2006 and .80 in 2010. Life satisfaction. Life satisfaction was measured using The Satisfaction with Life Scale (Diener, Emmons, Larsen, & Griffin, 1985). Respondents were asked to rate the degree to which they agreed with five statements (e.g. In most ways my life is close to ideal). Participants rated each item on a sixpoint scale (1 = strongly disagree, 6 = strongly agree) in 2006 and on a seven-point scale (1 = strongly disagree; 7 = strongly Eur. J. Pers. 30: 467–483 (2016) DOI: 10.1002/per

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Figure 1. Basic format of the bi-factor model of personality. Note. 1) In the actual models used, the number of indicator variables varied depending on the latent variable in question; 2) In the personality part of the model, the extraversion factor was excluded because the findings suggested that there might be no extraversion factor independent of the general personality factor. The extraversion items are indicated with an e.

agree) in 2010.1 Internal consistencies were .90 in 2006 and .89 in 2010. Loneliness. Loneliness was measured using three items2 selected from the UCLA Loneliness scale (Hughes, Waite, Hawkley, & Cacioppo, 2004). Respondents were asked to rate, on a three-point scale (1 = often; 2 = some of the time; 3 = hardly ever or never) the degree to which they felt (i) a lack companionship, (ii) left out, and (iii) isolated from others. The three-item Loneliness scale has been shown to measure loneliness adequately (Hughes et al., 2004). Internal consistencies were .82 in 2006 and .81 in 2010. Hopelessness. Hopelessness was measured using two items from the Hopelessness Scale developed by Beck, Weissman, Lester, and Trexler (1974) and two items selected from another scale measuring hopelessness developed by Everson, Kaplan, Goldberg, Salonen, and Salonen (1997). Respondents were asked to rate on a six-point scale (1 = strongly disagree; 6 = strongly agree) the degree to which they agreed with four statements (i.e. I feel it is impossible for me to reach the goals that I would like to strive for; The future seems hopeless to me and I can’t believe that things are changing for the better; I don’t expect to get what I really want; There’s no use in really trying to get something I want because I probably won’t get it). Internal consistencies were .87 in 2006 and .85 in 2010. Purpose in life. One dimension of well-being, purpose in life, was measured using seven items from the Ryff Measures of Psychological Well-being (Ryff, 1989). Participants were asked to rate on a six-point scale the degree to which they agreed with seven statements (e.g. I enjoy making plans for the future and working to make them a reality). Internal consistencies were .74 in 2006 and .79 in 2010. 1 Given the base rate of people endorsing the ‘1’ category is so low that we collapsed them into the ‘2’ category and ran all the analyses. It made no differences to the results. 2 The HRS study included three items from the UCLA Loneliness scale in the 2006 wave of assessment, and 11 items in the 2010, 2008, and 2012 waves of assessment. We included only the three items that are available in all four waves of assessment in the current study to ensure comparability across time points.

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Statistical analyses To examine the broad relationships between mental health and personality, bi-factor latent models were constructed in Amos 20. We first constructed and compared structural models of personality, of mental health, as well as of the relationship between personality and mental health. In the models we compared, we used items as indicators for variables that were measured with five or fewer items; we used parcels as indicators for variables that were measured with more than five items, because parcels have been shown to produce more reliable latent variables than individual items (Little, Cunningham, Shahar, & Widaman, 2002). Therefore, we randomly aggregated the eight-item depression scale into four parcels: (i) ‘sleep was restless’ and ‘was happy’; (ii) ‘felt sad’ and ‘felt full of energy’; (iii) ‘felt unmotivated’ and ‘enjoyed life’; (iv) ‘felt activities were efforts’ and ‘felt loneliness’. We randomly aggregated the seven-item openness scale into two parcels of two items and one parcel of three items: (i) ‘broad-minded’ and ‘imaginative’; (ii) ‘sophisticated’ and ‘creative’; (iii) ‘adventurous’, ‘intelligent’, and ‘curious’. The Purpose in Life scale has seven items. However because of lack of degrees of freedom, the model was not identified if we used three parcels for this scale. We therefore retained the original seven items as indicators for Purpose in Life. The basic format of the bi-factor latent change models is found in Figure 3. A latent change model uses two waves of data to estimate the latent intercept and the latent slope, representing changes of the latent factors over time (McArdle, 2009). Because of the complexity of the bifactor latent change models, we constructed and compared different parts of this model to obtain the optimal representation of the structures of both personality and mental health respectively. We first compared the fit of three measurement models for personality. The first model we tested was the one factor bi-factor model of personality (Model 1, Figure 1). Model 1 assumes that personality item ratings could be explained by one general factor and five domain specific factors, that is, the Big Five personality traits: openness, conscientiousness, extraversion, agreeableness, and neuroticism. Each item Eur. J. Pers. 30: 467–483 (2016) DOI: 10.1002/per

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had a nonzero loading on the factor that was designed to measure, and zero loadings on the other factors. Because the general factor was hypothesized to extract the common variance among the specific factors, the five specific factors demarcating the Big Five were set to be uncorrelated with each other. Last, the error terms associated with each item were set to be uncorrelated. Following DeYoung (2006), we also tested a bi-factor model with two general factors (Model 2). In Model 2, items for neuroticism, conscientiousness, and agreeableness loaded onto one factor (stability) while extraversion and openness loaded onto a second factor (plasticity). The two general factors were set to be uncorrelated. Similar to what we did with personality, we tested a onefactor bi-factor model for mental health (Model 3, Figure 2). Model 3 assumed the mental health ratings could be explained by one general factor, which we termed as general mental health, and six domain specific factors: depression, anxiety, satisfaction with life, loneliness, hopelessness, and purpose in life. The six domain-specific factors were set to be uncorrelated with each other. We next compared two models testing the relationship between personality and mental health. We first tested a common factor model of personality and mental health (Model 4) in which one general factor was specified that would account for all of the common variance shared by both personality traits and mental health. Specifically, Model 4 specified all indicators of personality and mental health loaded onto a general factor in addition to the domain specific personality and mental health factors. We then tested a two-factor bi-factor model (Model 5), in which the general personality factor and general mental health factors were specified and correlated. This model assumes that the general factor for both personality and mental health are independent enough of each other to remain distinct. We used the results of these cross-sectional models to test whether the specific factors of personality (e.g. the Big Five) correlated with the specific factors of mental health (e.g. depression), above and beyond any general factors of either personality or mental health. Last, we constructed bi-factor latent change models to view the relationship between personality and mental health

(Model 6) over time. Latent change models are promising techniques that allow for modelling of complex patterns of development and change of multiple developmental processes simultaneously (Jackson & Allemand, 2014). It therefore serves well our purpose of testing for shared changes in personality and mental health over time. As shown in Figure 3, the specification of Model 6 would be informed by the results of comparison between the two cross-sectional models (i.e. Model 4 and Model 5). That is, at each time point, Model 6 would be specified as it is specified in the better fitting cross-sectional model. For illustration purposes, Figure 3 represents a putative version of Model 6 based on the two-factor, bi-factor model (Model 5), assuming Model 5 best represents the relationship between personality and mental health. We specified the latent general factors for mental health and personality respectively at each time point. We then specified latent intercept and slope for factors for personality traits and mental health. The intercept and change parameters of personality factors were set to correlate with those of the mental health factors. All the item loadings and item residual variances were fixed to be equivalent across the two waves. In addition, the latent change model was fitted by using full information maximum likelihood estimation, which allows the use of all available data. We employed comparative fit index (CFI), the rootmean-square error of approximation (RMSEA), and the Akaike information criterion (AIC) to judge the model fit. We chose not to rely on the chi-square test because it has been demonstrated to be problematic when the data sets are large (e.g. Hoyle, 1995). Conventional guidelines for cutoff values suggest that RMSEA lower than .05 as a good fit, values lower than .08 as a reasonable fit and values between .08 and .10 as a mediocre fit (Cudeck & Browne, 1992). CFI values greater than .90 have often been considered an indicator of acceptable model fit (Marsh, Hau, & Grayson, 2005). Given the complexity of the relationships studied in the current study, we chose not to focus on maximizing absolute fit of the models. Rather, we focused on the comparative fit of different models for the data.

Figure 2. Basic format of the bi-factor model of mental health. Note. In the actual models used, the number of indicator variables varied depending on the latent variable in question.

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Eur. J. Pers. 30: 467–483 (2016) DOI: 10.1002/per

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Figure 3. Basic format of the latent change bi-factor model of personality and mental health. Note. 1) In the actual models used, the number of indicator variables varied depending on the latent variable in question; 2) In the personality part of the model, the extraversion factor was excluded because the findings suggested there might be no extraversion factor independent of the general personality factor.

RESULTS Table 1 displays the means, standard deviations, and correlations of manifest variables included in the analyses for both Copyright © 2016 European Association of Personality Psychology

waves in Study 1. As can be seen by the raw correlations, in both 2006 and 2010 there were substantial correlations between all of the Big Five and most of the mental health outcomes. While it appears that neuroticism had the most Eur. J. Pers. 30: 467–483 (2016) DOI: 10.1002/per

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Table 1. Means, standard deviations, and correlations between variables (Study 1; N = 3864) 2006

1

2

3

4

5

6

7

8

9

1. Openness 2. Conscientiousness 3. Extraversion 4. Agreeableness 5. Neuroticism 6. Depression 7. Anxiety 8. Life satisfaction 9. Loneliness 10. Hopelessness 11. Purpose in life

1

.43** 1

.53** .37** 1

.39** .39** .56** 1

.21** .23** .22** .10** 1

.15** .21** .26** .05** .38** 1

.17** .22** .21** .08** .50** .43** 1

.14** .19** .25** .12** .29** .41** .34** 1

.16** .18** .28** .11** .38** .41** .38** .45** 1

2010 1. Openness 2. Conscientiousness 3. Extraversion 4. Agreeableness 5. Neuroticism 6. Depression 7. Anxiety 8. Life satisfaction 9. Loneliness 1. Hopelessness 11. Purpose in life

1 1

2 .47** 1

3 .55** .41** 1

4 .43** .45** .57** 1

5 .25** .27** .24** .14** 1

6 .20** .25** .26** .10** .41** 1

7 .21** .26** .22** .10** .53** .45** 1

8 .21** .26** .31** .17** .33** .41** .33** 1

9 .17** .21** .29** .13** .38** .42** .38** .42** 1

11

Mean

SD

.30** .29** .30** .20** .35** .37** .41** .44** .45** 1

.41** .44** .42** .30** .33** .36** .35** .38** .40** .53** 1

2.99 3.42 3.22 3.53 2.04 .18 1.50 4.49 1.44 2.12 4.67

.52 .44 .54 .46 .59 .23 .52 1.17 .53 1.18 .88

10 .34** .34** .32** .23** .37** .38** .40** .43** .42** 1

11 .44** .50** .45** .35** .35** .37** .37** .38** .38** .54** 1

Mean 2.93 3.40 3.18 3.52 1.97 .18 1.50 4.11 1.43 2.24 4.70

SD .55 .47 .56 .48 .59 .23 .55 1.38 .52 1.18 .92

10

**p < .01.

consistent and largest correlations, one finds substantial correlations between personality scales such as conscientiousness and mental health outcomes such as life satisfaction and purpose in life. Similarly, extraversion had notably large negative correlations with loneliness and purpose in life. Cross-sectional measurement models As described in Statistical analyses, we started by comparing the fit of two measurement models for personality across both waves. Fit indices of the models across both waves were presented in Table 2. Factor loadings can be found in the online supporting materials. The first model we tested was the one-factor bi-factor model for personality (Model 1). The fit indices suggested that the model had a mediocre fit of the data according to the CFI (CFIs = .86 and .84), but reasonable fit according to the RMSEA (RMSEAs = .08 and .08, AICs = 4458.54 and 5210.24). However it should be noted that, for both waves, three extraversion items had negative loadings on the extraversion factor. These findings indicated that there might be no extraversion factor independent of the general personality factor. Accordingly, we modified Model 1 by not specifying an independent facet of extraversion (Model 1 Revised). The fit indices of the model were similar as those of Model 1 across both waves (CFIs = .85 and .83, RMSEAs = .08 and .09, AICs = 4770.27 and 5666.52), suggesting the revised model fit the data adequately. No problems with any of the estimates were observed. Following DeYoung (2006), we tested a two-factor bifactor model (Model 2) for both waves, in which items for neuroticism, conscientiousness, and agreeableness loaded onto one bi-factor (stability), while extraversion and Copyright © 2016 European Association of Personality Psychology

openness loaded onto another bi-factor (plasticity). Plasticity and stability were set to be uncorrelated.3 As in Model 1, for both waves, four extraversion items have negative loadings on the extraversion factor. These findings again suggested non-existence of the extraversion factor independent of the higher-order factors. Therefore, as in Model 1, we modified Model 2 by not specifying an independent facet of extraversion (Model 2 Revised). The fit indices of the revised version of Model 2 were similar as those of Model 2 before revision across both waves (CFIs = .77 vs. .79 and .75 vs. .77, RMSEAs = .09 vs. .09 and .10 vs. .10, AICs = 6950.90 vs. 6429.59 and 8202.72 vs. 7543.30), and no problematic estimates were observed. According to the fit indices, across both waves, Model 1 Revised fit the data much better than Model 2 Revised (CFIs = .85 vs. .77 and .83 vs. .75, RMSEAs = .08 vs. .09 and .09 vs. .10, AICs = 4770.27 vs. 6950.90 and 5666.52 vs. 8202.72). Because these two models were not nested, we could not directly test the difference of fit across models. We examined information criteria AIC, which again suggested that Model 1 Revised (Figure 1) had better model fit across both waves. Parallel to what we did with personality, across both waves, we tested a one-factor bi-factor model (Model 3). Model 3 assumed that the mental health ratings could be explained by one general mental health bi-factor and six 3

We also tested the model where the two higher-order bi-factors, plasticity and stability, were set to be correlated. With the 2006 data, the model was not identified. With the 2010 data, the model was identified but the correlation estimate between the two general bi-factors was .92. The high correlation between the two higher-order bi-factors suggested that two general factors were not necessary in this data. Eur. J. Pers. 30: 467–483 (2016) DOI: 10.1002/per

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Table 2. Fit indices of 2006 and 2010 Models (Study 1; N = 3864) Models Model 1. Personality: One Factor Bi-factor Model

Model 1 Revised. Personality: One Factor Bi-factor Model (without the extraversion facet) Model 2. Personality: Two Factor Bi-factor Model

Model 2 Revised. Personality: Two Factor Bi-factor Model (without the extraversion facet) Model 3. Mental health: One Factor Bi-factor Model Model 4. Personality and Mental health: Common Factor Model (without the extraversion facet) Model 5. Personality and Mental health: Correlation Model (without the extraversion facet) Model 6. Personality and Mental Health: Bi-factor Latent Change Model (without the extraversion facet)

Data

χ2

df

CFI

RMSEA

AIC

06

4282.54

187

.86

.08

4458.54

10

5034.24

187

.84

.08

5210.24

06

4604.27

192

.85

.08

4770.27

10 06

5500.52 6253.59

192 187

.83 .79

.09 .09

5666.52 6429.59

10

7367.30

187

.77

.10

7543.30

06

6784.90

192

.77

.09

6950.90

10 06

8036.72 2582.13

192 322

.75 .95

.10 .04

8202.72 2806.13

10 06

3352.19 14 514.35

322 1130

.94 .83

.05 .06

3576.19 14 904.35

10 06

15 667.44 9154.92

1130 1095

.83 .90

.06 .04

16 057.44 9614.92

10 06 and 10

11 039.61 26 884.53

1095 4773

.88 .89

.05 .04

11 499.61 27 638.53

domain specific bi-factors (depression, anxiety, satisfaction with life, loneliness, hopelessness, and purpose in life). The six domain-specific factors were set to be uncorrelated with each other. Model 3 (Figure 2) had excellent fit across both waves (CFIs = .95 and .94, RMSEAs = .04 and .05, AICs = 2806.13 and 3576.19). We next constructed and compared two models to examine the relationship between personality and mental health using the personality and mental health models with superior fit indices, that is, Model 1 Revised and Model 3. Across both waves, we first tested the common factor model of personality and mental health (Model 4). Model 4 specified all indicators of personality and mental health loaded onto a general factor in addition to the domain specific factors. The fit indices suggested that the model fit was mediocre for the CFI (CFIs = .83 and .83) but good for the RMSEA (RMSEAs = .06 and .06, AICs = 14 904.35 and 16 057.44). We then tested Model 5, the two-factor bi-factor model of personality and mental health, in which the general personality factor was related with the general mental health factor. The fit indices of the model were good across both waves (CFIs = .90 and .88, RMSEAs = .04 and .05, AICs = 9614.92 and 11 499.61), suggesting that the model fit the data adequately and better than the common factor model (Model 4). The AICs also suggested much better fit for the bi-factor correlation models (Model 5). Copyright © 2016 European Association of Personality Psychology

Notes Three extraversion items have negative loadings on the extraversion bifactor. Three extraversion items have negative loadings on the extraversion bifactor.

Four extraversion items have negative loadings on the extraversion bifactor. Four extraversion items have negative loadings on the extraversion bifactor.

Bi-factor latent change model Our initial analyses indicated that the optimal model included general latent factors for both personality traits and mental health. In order to test our primary research questions about the relation between specific personality traits and specific components of mental health, we constructed a bi-factor latent change model (Model 6, Figure 3). Using this model, we could test the relation among levels, which would correspond to average overall levels of both the general factors and the specific factors, as well as the relations among change in general and specific factors over time. Fit indices suggested that the model fit the data adequately (CFI = .89, RMSEA = .04). To estimate the correlation between personality and mental health factors, we constructed models in which the personality factors were related to one mental health factor at a time. Fit indices of these models were mediocre for the CFI (ranging from .86 to .88), but good for the RMSEA, being .04 consistently across this set of models. The correlation estimates obtained through these analyses are presented in Tables 3 and 4. Consistent with our expectation, the average level of the general personality factor was strongly correlated with that of the general mental health factor, r = .52, p < .01. Furthermore, changes in the general personality factor were also strongly correlated with changes in the general Eur. J. Pers. 30: 467–483 (2016) DOI: 10.1002/per

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Table 3. Correlate estimates between levels of personality and mental health in the bi-factor latent change models (Study 1; N = 3864) Variables

GP

GMH Depression Anxiety Life satisfaction Loneliness Hopeless Life purpose

.52** .12** .11** .02 .13** .15** .62**

O .03 .15** .06** .13** .17** .22** .18**

C

A

N

.18** -.04 -.02 .00 .11** -.04 .32**

.12** .29** .08** -.04 .13** .12** -.03

.55** .27** .69** -.01 .22** .05* -.03

Note: GMH = general mental health; GP = general personality; O = openness; C = conscientiousness; E = extraversion; A = agreeableness; N = neuroticism. **p < .01 *p < .05.

Table 4. Correlate estimates between changes of personality and mental health in the bi-factor latent change models (Study 1; N = 3864) Variables

GP

GMH Depression Anxiety Life satisfaction Loneliness Hopeless Life purpose

.47** .10** .11** .08** .15** .13** .50**

O

C

A

N

-.04 -.02 -.04 -.02 .07 .09* .03

.22** .02 .14** .08 .15** -.01 .33**

.11** .05 .06 .10* .07 .06 -.05

.48** .23** .53** -.03 .18** .02 -.05

Note: GMH = general mental health; GP = general personality; O = openness; C = conscientiousness; E = extraversion; A = agreeableness; N = neuroticism. **p < .01.

mental health factor, r = .47, p < .01. It should be noted that the general mental health factor was positively loaded by depression, anxiety, hopelessness, and loneliness, but negatively loaded by life satisfaction and purpose in life. Therefore, the higher the mental health factor is, the worse mental health it implies. We next examined whether specific personality traits were associated with specific aspects of mental health while controlling for the general factors associated with both personality traits and mental health. Compared to the raw correlations presented in Table 1, many of the resulting associations were substantially reduced. This indicates that much of the covariance between specific personality traits and specific mental health outcomes was attributable to the general factors underlying each domain rather than the specific variance of each component. For example, the raw correlation between life satisfaction and neuroticism in 2006 was r = .29, p < .01 and in 2010 r = .33, p < .01. However, in the bi-factor latent change model the correlation between the level of life satisfaction and neuroticism was indistinguishable from zero, r = .01, p = .60. Similarly, the correlation between changes in life satisfaction and changes in neuroticism was not significant either, r = .03, p = .10. That is, if we controlled for the general factors of personality and mental health, the relationships between life satisfaction and neuroticism became insignificant. Similarly, the associations between other personality traits, such as agreeableness and conscientiousness, and life satisfaction were reduced Copyright © 2016 European Association of Personality Psychology

markedly in magnitude when the latent general factors were modelled. Furthermore, similar patterns emerged across the remaining indicators of mental health, such as depression and loneliness. Despite the marked reduction in the magnitude of many of the relations between specific personality traits and specific components of mental health, some relations among specific mental health variables and specific personality traits remained large and relatively unaffected by the inclusion of the general factors. For example, the neuroticism factor remained significantly related to all the specific mental health factors. The level of the neuroticism was positively related to that of depression (r = .27, p < .01), anxiety (r = .69, p < .01), and loneliness (r = .22, p < .01). Changes in neuroticism were also positively related to changes in depression (r = .23, p < .01), changes in anxiety (r = .53, p < .01), and changes in loneliness (r = .18, p < .01). Taken together, these findings suggest that the general factor, which is strongly associated with the domain of extraversion, and the specific factor of neuroticism are the only meaningful personality trait predictors of mental health.

STUDY 2 In an attempt to replicate the findings of Study 1, we conducted the same analyses using data collected in the ninth (2008) and eleventh (2012) waves of assessment from the HRS.

METHOD Sample In 2008, 5381 participants (Mage = 68.34, SD = 9.95, 59.4% female) had complete data for each of the measures used in the current study. Participants reported an average of 12.89 years of education. A total of 2953 participants (Mage = 66.66, SD = 8.93, 60.6% female) from the first wave had complete data in 2012 for each of the measures used. Participants reported an average of 13.40 years of education. Across both time points, the ethnic background of our sample was 75.0% European American, 19.7% African American, and 5.3% other. As in Study 1, T-tests for independent samples revealed that individuals who did and did not complete the assessment at Time 2 differed from each other in terms of age (d = .38) such that older adults were more likely to drop out of the study. Also, individuals with higher education were less likely to drop out of the sample (d = .39). Measures The same measures used in Study 1 were used to assess personality and mental health variables in Study 2. Personality was measured using the MIDUS Big Five Adjectival scale (Lachman & Bertrand, 2001). Internal consistencies for the openness, conscientiousness, extraversion, agreeableness, and neuroticism scales in 2008 were α = .79, .66, .76, .78, and .73 Eur. J. Pers. 30: 467–483 (2016) DOI: 10.1002/per

Change or specificity? respectively; and α = .79, .67, .76, .80, and .72 respectively in 2012. For mental health, depression was measured using CES-D (Radloff, 1977). The internal consistencies of the scale were .77 in 2008 and .76 in 2012. Anxiety was measured using five items selected from BAI (Beck et al., 1988). Internal consistencies were .79 in 2008 and 2012 respectively. Life satisfaction was measured using the The Satisfaction with Life Scale (Diener et al., 1985). Internal consistencies were .89 in 2008 and 2012 respectively. Loneliness was measured using three items selected from the UCLA Loneliness scale (Hughes et al., 2004). Internal consistencies were .81 in 2008 and .80 in 2012. Hopelessness was measured using two items from the Beck Hopelessness Scale (Beck et al., 1974) and two items selected from another hopelessness scale developed by Everson et al. (1997). Internal consistencies were .84 in 2008 and .87 in 2012. Purpose in Life was measured using seven items from the Ryff Measures of Psychological Well-being (Ryff, 1989). Internal consistencies were .77 in 2008 and .78 in 2012.

477

and a general personality factor. Fit indices suggested that the model fit was mediocre according to the CFI (CFIs = .85 and .81) and reasonable for the RMSEA (RMSEAs = .08 and .09, AICs = 3671.96 and 5002.98). Notably, as in Study 1, for both waves, four extraversion items had negative loadings on the extraversion bi-factor, indicating that there was not an extraversion bi-factor independent of the general personality factor. Accordingly, as in Study 1, we modified Model 1 by not specifying an extraversion bi-factor. The fit indices of the model were similar as those of Model 1 across both waves (CFIs = .83 and .78, RMSEAs = .08 and .10, AICs = 4197.82 and 5735.70), suggesting that the revised model fit the data adequately without causing any problems with any of the estimates. Following DeYoung (2006), then we fitted for both waves Model 2, the two-factor bi-factor models with the Big Five personality factors and two general personality factors, plasticity and stability. As in Model 1, four extraversion items have negative loadings on the extraversion bi-factor across both waves. We modified the model by not specifying the independent extraversion bi-factor (Model 2 Revised). Across both waves, the fit indices of the revised version of Model 2 were similar as those of Model 2 before revision (CFIs = .76 vs. .78 and .71 and vs. .73, RMSEAs = .10 vs. .09 and .11 vs. .11, AICs = 5738.46 vs. 5203.89 and 7670.17 vs. 7075.13), and no problematic estimates were observed anymore. Therefore, as in Study 1, the fit indices suggested that Model 1 Revised fit the data than Model 2 Revised (CFIs = .83 vs. .76 and .78 vs. .71, RMSEAs = .08 vs. .10 and .10 vs. .11, AICs = 4197.82 vs. 5738.46 and 5735.70 vs. 7670.17). That is, the bi-factor model with a general personality factor and four domain specific facets showed better fit. Despite the less

RESULTS Table 5 displays the means, standard deviations, and correlations of manifest variables included in the analyses for both waves in Study 2. Similar to Study 1, the correlations between the Big Five personality traits and the various measures of mental health were moderate in magnitude and pervasive across most of the personality scales. Cross-sectional models As with Study 1, we fitted two measurement models for personality. Fit indices of Models were presented in Table 6. We first fit Model 1 for both waves, the one-factor bi-factor model of personality with the Big Five personality factors

Table 5. Means, standard deviations, and correlations between variables (Study 2; N = 2953) 2008

1

2

3

4

5

6

7

8

9

1. Openness 2. Conscientiousness 3. Extraversion 4. Agreeableness 5. Neuroticism 6. Depression 7. Anxiety 8. Life satisfaction 9. Loneliness 10. Hopelessness 11. Purpose in life

1

.41** 1

.52** .34** 1

.36** .37** .53** 1

.24** .26** .23** .12** 1

.17** .23** .26** .05** .40** 1

.18** .27** .20** .08** .53** .47** 1

.19** .21** .26** .12** .34** .45** .38** 1

.13** .19** .27** .14** .37** .44** .38** .45** 1

2012 1. Openness 2. Conscientiousness 3. Extraversion 4. Agreeableness 5. Neuroticism 6. Depression 7. Anxiety 8. Life satisfaction 9. Loneliness 10. Hopelessness 11. Purpose in life

1 1

2 .45** 1

3 .56** .39** 1

4 .43** .44** .55** 1

5 .27** .30** .25** .14** 1

6 .16** .25** .23** .07** .42** 1

7 .16** .27** .21** .10** .53** .49** 1

8 .17** .22** .26** .13** .32** .44** .39** 1

9 .14** .17** .26** .13** .37** .43** .37** .44** 1

11

Mean

SD

.29** .30** .28** .19** .34** .37** .42** .43** .42 1

.39** .44** .42** .29** .36** .39** .39** .38** .38** .53** 1

2.99 3.43 3.23 3.56 1.99 .18 1.49 5.12 1.42 2.16 4.81

.54 .45 .55 .45 .60 .23 .53 1.47 .51 1.16 .88

10 .28** .30** .28** .20** .36** .38** .37** .39** .41** 1

11 .44** .50** .43** .32** .39** .38** .35** .37** .38** .51** 1

Mean 2.93 3.40 3.18 3.54 1.96 .19 1.51 4.97 1.43 2.17 4.66

SD .55 .47 .56 .47 .59 .23 .55 1.50 .51 1.20 .90

10

**p < .01. Copyright © 2016 European Association of Personality Psychology

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Table 6. Fit indices of 2008 and 2012 Models (Study 2; N = 2953) Models Model 1. Personality: One Factor Bi-factor Model

Model 1 Revised. Personality: One Factor Bi-factor Model (without the extraversion facet) Model 2. Personality: Two Factor Bi-factor Model

Model 2 Revised. Personality: Two Factor Bi-factor Model (without the extraversion facet) Model 3. Mental health: One Factor Bi-factor Model Model 4. Personality and Mental Health: Common Factor Model (without the extraversion facet) Model 5. Personality and Mental Health: Correlation model (without the extraversion facet) Model 6. Personality and Mental Health Bi-factor Latent Change Model (without the extraversion facet)

χ2

Data

df

CFI

RMSEA

AIC

08

3495.96

187

.85

.08

3671.96

12

4826.98

187

.81

.09

5002.98

08

4031.89

192

.83

.08

4197.82

12 08

5569.70 5027.89

192 187

.78 .78

.10 .09

5735.70 5203.89

12

6899.13

187

.73

.11

7075.13

08

5572.49

192

.76

.10

5738.46

12 08

7504.17 2380.71

192 322

.71 .94

.11 .05

7670.17 2604.71

12 08

2794.97 11 975.77

322 1130

.93 .82

.05 .06

3018.97 12 365.77

12 08

13 966.16 8142.88

1130 1095

.80 .88

.06 .05

14 356.16 8602.88

12 08 and 12

10 050.98 23 716.92

1095 4773

.86 .87

.05 .04

10 510.98 24 470.92

than ideal fit for the bi-factor model, we nonetheless proceeded to use it in order to replicate the analyses from Study 1. We fit for both waves Model 3, the bi-factor models of mental health with six domain specific factors (depression, anxiety, satisfaction with life, loneliness, hopelessness, and purpose in life) and a general mental health factor. Fit indices suggested good model fit (CFIs = .94 and .93, RMSEAs = .05 and .05, AICs = 2604.71 and 3018.97). We next constructed and compared two models to examine the relationship between personality and mental health using the personality and mental health models with superior fit indices, that is, Model 1 Revised and Model 3. We first fit Model 4, the common factor model of personality and mental health in which all the manifest indicators of personality and mental health loaded on both their domain specific factors and a general factor for both waves. As in Study 1, fit indices suggested that the model fit was mediocre for the CFI (CFIs = .82 and .80) but good for the RMSEA (RMSEAs = .06 and .06, AICs = 12 365.77 and 14 356.16). Next, we tested the bi-factor models with general factors for both personality and mental health for both waves (Model 5). Fit indices indicated mediocre fit according to the CFI (CFIs = .88 and .86) and good fit according to the RMSEA (RMSEAs = .05 and .05, AICs = 8602.88 and 10 510.98), suggesting the model fit the data adequately and better than Copyright © 2016 European Association of Personality Psychology

Notes Four extraversion items had negative loadings on the extraversion bifactor. Four extraversion items had negative loadings on the extraversion bifactor.

Four extraversion items had negative loadings on the extraversion bifactor. Four extraversion items had negative loadings on the extraversion bifactor.

the common factor model (Model 4). The AICs suggested better model fit for the bi-factor models that included latent factors for both personality and mental health. Bi-factor latent change model As in Study 1, we next constructed a bi-factor latent change model to estimate the relationship between personality and mental health (Model 6, Figure 1). Fit indices indicated mediocre fit for the CFI (CFI = .87) and good fit for the RMSEA (RMSEA = .04). To estimate the correlation between personality and mental health factors, we constructed models in which the specific personality factors were set to be related to one mental health factor at a time. Fit indices of these models were mediocre for the CFI ranging from .86 to .87, and good for the RMSEA being identically .04 for all this set of models. Consistent with findings in Study 1, the level of the general personality factor was negatively and significantly correlated with that of the general mental health general factor, r = .53, p < .01. Meanwhile, changes in the general personality factor were also negatively and significantly correlated with changes in the general mental health factor, r = .51, p < .01, indicating that increases in personality traits over time were significantly associated with increases in mental health. We next tested the associations between specific personality traits and specific domains of mental health while Eur. J. Pers. 30: 467–483 (2016) DOI: 10.1002/per

Change or specificity? controlling for the general personality and general mental health factors. As in Study 1, when the general factors of personality and mental health were included, the correlations between specific personality traits and specific mental health domains decreased substantially for both the level and change variables (see Tables 7 and 8). Also consistent with Study 1, the magnitude of the neuroticism associations with specific mental health outcomes remained largely unchanged for both levels and change. While many of the remaining Big Five correlations were statistically significant, most were substantially reduced in magnitude from the raw correlations and were also much smaller than the correlations of neuroticism.

DISCUSSION The current research aims to address a set of fundamental questions arising from the observations that specific personality traits are broadly related with different aspects of mental health. To account for these observations, we proposed the ‘barometer hypothesis’, positing that behind both ratings of mental health and personality lies a barometer that indicates one’s general feelings of positivity or negativity, which reflect not the specific variance associated with either aspects of mental health or specific traits, but the general, global evaluations of life. To the extent that both the general factor of mental health and the GFP reflect this same barometer, then we would Table 7. Correlate estimates between levels of personality and mental health in the bi-factor latent change models (Study 2; N = 2953) Variables

GP

GMH Depression Anxiety Life satisfaction Loneliness Hopeless Life purpose

.53** .09** .07** .06 .07** .13** .66**

O

C

A

N

.04 .15** .05 -.07 .17** .19** .11**

.19** -.04 -.05 -.05 .11** -.08 .28**

.07** .27** .03 .09** -.01 .10** .07**

.54** .24** .73** -.01 .19** .05 -.01

Note: GMH = general mental health; GP = general personality; O = openness; C = conscientiousness; E = extraversion; A = agreeableness; N = neuroticism. **p < .01.

Table 8. Correlate estimates between changes of personality and mental health in the bi-factor latent change models (Study 2; N = 2953) Variables

GP

GMH Depression Anxiety Life satisfaction Loneliness Hopeless Life purpose

.51** .12** .15** .06 .13** .15** .50**

O

C

A

N

.06 .14 .13 .11 .25* .16 .30**

-.12 .08 -.05 -.02 .28** -.06 .25**

.17** .10 .06 -.10 .13 -.09 .18**

.51** .19** .55** .10** .16** .02 -.09

Note: GMH = general mental health; GP = general personality; O = openness; C = conscientiousness; E = extraversion; A = agreeableness; N = neuroticism. **p < .01. Copyright © 2016 European Association of Personality Psychology

479

expect them to be correlated. In testing this hypothesis we asked three questions: (i) is there a single factor underlying both the shared variance in personality traits and mental health; (ii) do the relations between specific traits and specific components of mental health hold when the shared variance among the domains of personality and mental health are objectively assessed; and (c) do these patterns replicate across longitudinal analyses of the associations between changes in personality traits and changes in mental health? In addressing these questions, the current research tested alternative models accounting for the broad correlation between the Big Five personality traits and the mental health outcomes using data from a large longitudinal panel study, the HRS. These data afforded us the opportunity to examine our questions using two cohorts of participants who were assessed every two years, resulting in parallel 4-year longitudinal studies. We found very similar patterns across both studies. In addressing our first question with regard to whether there exists a single factor underlying both the shared variance in personality traits and mental health, we constructed and compared two models. We first tested whether a common factor model accounted for the share variance among all of the Big Five and various indices of mental health. Second, we tested correlation models in which the two correlated general factors specific to personality and mental health were specified. Our cross-sectional modelling revealed that the model with general factors for both personality and mental health was preferable. We then tested the bi-factor latent change model with two general factors of personality and mental health specified. The model fit the longitudinal data well in both studies, which provided further support for the barometer hypothesis that two correlated general factors underlie specific personality traits and components of mental health. One feature of the resulting model with two general factors should be noted. We consistently found, across both waves and both studies, low and even negative loadings of extraversion items on the extraversion factor in the personality part of the bi-factor models. The low and negative loadings indicated that there was no extraversion factor independent of the general personality factor. Accordingly, we modified the two-factor bi-factor model by not specifying an independent facet of extraversion. While this eliminated the possibility of testing the specific relation of extraversion to specific components of mental health, it does lend itself to informing the meaning of the general factor, which we discuss below. We next addressed the other primary question about the relation between specific personality traits and specific components of mental health. Pearson bivariate correlations revealed that, across both studies, there were substantial correlations between all of the Big Five and most of the mental health outcomes. We next tested the relations again within the context of the bi-factor latent change models. On the one hand, compared to the broad and substantial raw correlations mentioned above, many of the resulting associations were substantially reduced. This indicates that much of the covariance between personality traits and mental health is attributable to the general factors underlying each domain rather than the specific variance of each component. On the other Eur. J. Pers. 30: 467–483 (2016) DOI: 10.1002/per

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hand, despite the marked reduction in the magnitude of many of the relations between specific personality traits and specific components of mental health, some relations among specific mental health variables and specific personality traits remained large and relatively unaffected by the inclusion of the general factors. For example, the neuroticism factor remained significantly related to all the specific mental health factors. Taken together, these findings suggest that general factor, which is strongly associated with the domain of extraversion, and the specific factor of neuroticism are the strongest personality trait predictors of mental health. Finally, we showed that the findings not only replicated across two longitudinal studies, but also in the associations between change in personality and change in mental health. Building and using replication designs is now widely advised given the problems science and psychology in particular has been having recently in replicating results (Asendorpf et al., 2013). We must laud the organizers of the HRS for designing their longitudinal assessments in such a way that direct replications are intrinsic to the nature of the study. The extension of the findings to changes in personality and changes in mental health is important to the ongoing dialogue on the relation of personality traits to clinical phenomenon. Most portraits of the association between personality and clinical psychology view personality traits as static causes of clinical problems (see Durbin & Hicks, 2014). In contrast, the enterprise of clinical psychology is intrinsically that of change. The goal of therapy is to improve mental health. The role of changes in personality to changes in mental health has only recently become a focus of interest. Yet, provocative initial findings have shown that personality change covaries with changes in mental health (e.g. Chow & Roberts, 2014), that personality traits change as a result of therapy (De Fruyt, Van Leeuwen, Bagby, Rolland, & Rouillon, 2006), and that personality trait change in particular may be the reason why therapy is successful (Tang et al., 2009). Given the increased focus on personality trait change being intrinsic to the therapeutic process, it is important to establish the relations between changes in personality traits and changes in mental health in a more defensible fashion and the bi-factor latent change model provides one means to achieve that goal. In terms of the general factors of both personality and mental health, by considering it a ‘barometer’ of functioning we have been somewhat non-committal about their meaning, at least in comparison to other efforts (e.g. DeYoung, 2006; Rushton, Bons, & Hur, 2008). Instead, we preferred to emphasize the methodological and conceptual advantages that the bi-factor model brings to the question of the relations between personality traits and mental health. By specifying the global factors, we have made for a more rigorous test of whether the relations identified as the ‘largest’ or most important are truly that. And, for the most part our findings confirm the previous work showing that neuroticism is the key domain of normal personality that is associated with a wide variety of mental health indicators. Where the bi-factor model becomes more interesting is on the margins. For example, we have noted that conscientiousness, while clearly not as important as neuroticism, still has substantial associations with a wide variety of mental Copyright © 2016 European Association of Personality Psychology

health measures. The prototypical approach that we and others have taken in drawing this conclusion is that the relationship holds when controlling for one or more of the Big Five, such as neuroticism (see Chow & Roberts, 2014; Fayard, Roberts, Robins, & Watson, 2012). We prefer the bi-factor approach for two reasons. First, it forces one to confront the existence of the shared variance among all of the Big Five and its meaning, which one typically does not have to do when simply controlling for an additional scale or two. Second, it provides a stronger test of the independence of conscientiousness and lets one systematically vary the paths to outcomes to better understand how and why specific factors might be associated with mental health. A case in point for conscientiousness is its relation to life satisfaction and purpose. In past research, it appeared that conscientiousness was a good predictor of life satisfaction above and beyond neuroticism, for example (Fayard et al., 2012). But, as can be seen in our results, the relatively substantial correlations between conscientiousness and life satisfaction disappear once the general factors of both personality traits and mental health are specified. On the other hand, the substantial correlation with purpose in life remained despite the even larger association between the general personality trait and purpose. This gives us more confidence that the conscientiousnesspurposefulness association is distinct. Of course, specifying the general factors for both personality and mental health does necessitate some discussion of just what those factors mean. For mental health, there appears to be less controversy. Clinical psychology has long employed general assessments of mental health, such as the General Assessment of Functioning ratings, which are part of the DSM system. For personality, the existence of general factors has had a mixed history. Ever since Rorer (1965) argued that the first unrotated principal component of the MMPI was method artifact, the meaning and existence of the general factor has been met with controversy. More recent arguments are centred on substantive interpretations of the general factor with arguments being made for one (Musek, 2007) or two factors (DeYoung, 2006; Digman, 1997; Saucier et al., 2014). First, we should note that we do not believe our findings are relevant to whether there are one or two higher-order factors. The number of higher-order factors will be, in part, determined by the assessment instrument used, and in this case the MIDUS personality measure does not provide the type of coverage one would need to identify more than one general factor. In respect to the meaning of the GFP that we did specify, it is clear from our results that the extraversion component of personality was synonymous with that factor. Thus, one interpretation would be that the general positivity intrinsic to extraversion was the proper way to interpret this factor. The remaining Big Five dimensions loaded on this factor to the extent that they contained general positivity variance. This is consistent with our barometer idea. This type of interpretation helps to explain the rather large association the GFP had with general mental health. Although not the same construct, they were quite highly related. Interpreting the general factor as positivity, or as the barometer of functioning poses interesting possibilities and Eur. J. Pers. 30: 467–483 (2016) DOI: 10.1002/per

Change or specificity? some difficult questions. In respect to the possibilities, we prefer the barometer hypothesis to allusions to better interpersonal functioning (e.g. Rushton et al., 2009) because it requires fewer strong assumptions about the meaning of the dimension. An underlying evaluative factor has long been a component in psychological assessment and is clearly pervasive throughout the Big Five (Saucier, Ostendorf, & Peabody, 2001). That it would be a key factor on the relation between personality traits and mental health makes good sense given the evaluative nature of both domains. Simultaneously, the barometer hypothesis is preferable to arguments that the factor is an artifact, especially given the potential consequences of positive evaluations of one’s life. In terms of the difficult questions it poses, in the clinical outcome literature, it is common to argue that personality trait measures contain significant state-level variance. If this is the case, then it is possible that the general factor identified in the present study is simply state-level positivity. Countering this argument, the test retest stability of the general factor was quite high (r = .75). Another question is what is the meaning of agreeableness or conscientiousness when they are stripped of their positivity? Clearly, enough unique variance remained for constructs similar to agreeableness and conscientiousness to be specified, but the bi-factor structure by necessity changes the meaning of the facets too. While it is common for researchers to use hierarchical regression or other multivariate models similar to the bi-factor model when trying to establish the incremental validity of a construct, what is seldom confronted is that the multivariate meaning of that construct is different than the univariate meaning. The bi-factor model does a nice job of exposing this issue and making it conceptually clearer. We suspect that better data will be needed to address some of these questions. For example, more thorough and continuous assessments of states and traits would be necessary to test whether the general factor is more state-like or trait like. Also, using multiple methods would help to better clarify the meanings of the specific factors in addition to a broader set of outcomes with which to establish their construct validity (e.g. DeYoung, 2006). Future research should endeavour to address these limitations by conducting deeper assessments of the constructs of interest more often. It should be noted that in both studies, individuals at a younger age or those with higher education are less likely to drop out of the samples. Michaud, Kapteyn, Smith, and Van Soest (2011) found a similar pattern among the sample of the first cohort of the HRS study where younger individuals are more likely to retain in the sample and they found that such an attrition is mostly a result of mortality. Other large longitudinal studies of health and personality with a similar design also observed individuals with higher education are less likely to drop out (e.g. Luo & Roberts, 2015). We are unaware of any good reasons to expect that participants of older age or lower education would differ from the rest of the participants with regards to the relationship between their personality traits and mental health. However it will be important for future studies to further examine this issue to verify if this is indeed the case. Copyright © 2016 European Association of Personality Psychology

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In conclusion, we have used the bi-factor model in conjunction with a latent change model to better understand the relations between personality traits and mental health. We found support for the barometer hypothesis, suggesting that general factors of personality traits and general factors of mental health help to explain much of the shared variance of these two domains, but we also found support for the idea that specific personality traits are related to mental health, above and beyond the common factors. Specifically, we found that neuroticism is the primary factor associated with mental health even when controlling for the GFP. Moreover, these findings extended to the longitudinal models. In general, we believe that the use of the bi-factor model helps to clarify the nature of the relation between personality and mental health. ACKNOWLEDGEMENTS We would like to thank Rodica Damian for her helpful comments on a previous version of this manuscript. SUPPORTING INFORMATION Additional supporting information may be found in the online version of this article at the publisher’s web-site. REFERENCES Allan, N. P., Albanese, B. J., Short, N. A., Raines, A. M., & Schmidt, N. B. (2015). Support for the general and specific bifactor model factors of anxiety sensitivity. Personality and Individual Differences, 74, 78–83. doi:10.1016/j.paid.2014.10.003. American Psychiatric Association (2000). Diagnostic and statistical manual of mental disorders DSM (4th ed., text revision). Asendorpf, J. B., Conner, M., De Fruyt, F., De Houwer, J., Denissen, J. J. A., Fiedler, K., & Wicherts, J. M. (2013). Recommendations for increasing replicability in psychology. European Journal of Personality, 27, 108–119. doi:10.1002/per.1919. Beck, A. T., Epstein, N., Brown, G., & Steer, R. A. (1988). An inventory for measuring clinical anxiety: Psychometric properties. Journal of Consulting and Clinical Psychology, 56, 893–897. doi:10.1037/0022-006X.56.6.893. Beck, A. T., Weissman, A., Lester, D., & Trexler, L. (1974). The measurement of pessimism: The hopelessness scale. Journal of Consulting and Clinical Psychology, 42, 861–865. doi:10.1037/ h0037562. Burkhauser, R. V., & Gertler, P. J. (1995). Introduction to special issue on the Health and Retirement Survey/data quality and early results. Journal of Human Resources, 30, S1–S6. Caspi, A., Houts, R. M., Belsky, D. W., Goldman-Mellor, S. J., Harrington, H., Israel, S., … Moffitt, T. E. (2014). The p factor: One general psychopathology factor in the structure of psychiatric disorders? Clinical Psychological Science, 2, 119–137. doi: 10.1177/2167702613497473 Chen, F. F., Hayes, A., Carver, C. S., Laurenceau, J.-P., & Zhang, Z. (2012). Modeling general and specific variance in multifaceted constructs: A comparison of the bifactor model to other approaches. Journal of Personality, 80, 219–251. doi:10.1111/ j.1467-6494.2011.00739.x. Chen, F. F., West, S. G., & Sousa, K. H. (2006). A comparison of bifactor and second-order models of quality of life. Multivariate Behavioral Research, 41, 189–225. doi:10.1207/s15327906mbr4102_5. Eur. J. Pers. 30: 467–483 (2016) DOI: 10.1002/per

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