American Psychological Association © 2020 American Psychological Association ISSN: 0033-2909 Psychological Bulletin 2020, Vol. 146, No. 4, 279-323 http://dx.doi.org/10.1037/bul0000226 Predicting Psychological and Subjective Weil-Being From Personality: A Meta-Analysis Jeromy Anglim and Sharon Horwood Deakin University Luke D. Smillie University of Melbourne Rosario J. Marrero University of La Laguna Joshua K. Wood Deakin University This study reports the most comprehensive assessment to date of the relations that the domains and facets of Big Five and HEXACO personality have with self-reported subjective well-being (SWB: life satisfaction, positive affect, and negative affect) and psychological well-being (PWB: positive relations, autonomy, environmental mastery, purpose in life, self-acceptance, and personal growth). It presents a meta-analysis (n = 334,567, k = 462) of the correlations of Big Five and HEXACO personality domains with the dimensions of SWB and PWB. It provides the first meta-analysis of personality and well-being to examine (a) HEXACO personality, (b) PWB dimensions, and (c) a broad range of established Big Five measures. It also provides the first robust synthesis of facet-level correlations and incremental prediction by facets over domains in relation to SWB and PWB using 4 large data sets comprising data from prominent, long-form hierarchical personality frameworks: NEO PI-R (n = 1,673), IPIP-NEO (n = 903), HEXACO PI-R (n = 465), and Big Five Aspect Scales (n = 706). Meta-analytic results highlighted the importance of Big Five neuroticism, extraversion, and conscientiousness. The pattern of correlations between Big Five personality and SWB was similar across personality measures (e.g., BFI, NEO, IPIP, BFAS, Adjectives). In the HEXACO model, extraversion was the strongest well-being correlate. Facet-level analyses provided a richer description of the relationship between personality and well-being, and clarified differences between the two trait frameworks. Prediction by facets was typically around 20% better than domains, and this incremental prediction was larger for some well-being dimensions than others. Public Significance Statement This meta-analysis provides a comprehensive and detailed overview of the substantial links between personality traits and well-being. It is the first investigation to incorporate the two most widely accepted frameworks for measuring personality (i.e., the Big Five and the HEXACO model) as well as two of the most influential models of human well-being (i.e., subjective and psychological well-being). Results of the meta-analysis provide important insights into the various pathways through which people build well-being in their lives. Keywords: Big Five, HEXACO, personality facets, psychological well-being, subjective well-being Supplemental materials: http://dx.doi.org/10.1037/bul0000226.supp Decades of research shows that personality traits play a critical role in how we experience, approach, and appraise our lives (DeNeve & Cooper, 1998; Headey & Wearing, 1989; Steel, Schmidt, & Shultz, 2008). Many researchers assess the good life in terms of subjective well-being (SWB): a composite of life satisfaction, high levels of positive affect, and low levels of negative This article was published Online First January 16, 2020. © Jeromy Anglim and Sharon Horwood, School of Psychology, Deakin University; Luke D. Smillie, Melbourne School of Psychological Sciences, University of Melbourne; Rosario J. Marrero, Department of Clinical Psychology, Psychobiology, and Methodology, Faculty of Psychology, University of La Laguna; ® Joshua K. Wood, School of Psychology, Deakin University. Data, scripts, materials, and supplemental analyses are available at https://osf.io/42rsy. We are grateful to Jessie Sun and Ingo Zettler for their valuable feedback on an initial draft of this article. Correspondence concerning this article should be addressed to Jeromy Anglim, School of Psychology, Deakin University, Locked Bag 20000, Geelong, 3220 Australia. E-mail: jeromy.anglim@deakin.edu.au 279 280 ANGLIM, HORWOOD, SMILLIE, MARRERO, AND WOOD affect (Diener, 1984). Whereas SWB largely avoids making assumptions about the causes of happiness, other conceptualizations of well-being draw more strongly on eudaimonic and humanistic perspectives in conceptualizing well-being (Waterman, 1993). In particular, the six-dimensional model of psychological well-being (PWB) identifies a broader set of well-being dimensions, comprising positive relations, autonomy, environmental mastery, personal growth, purpose in life, and self-acceptance (Ryff, 1989). Previous research shows that major dimensions of personality are robustly associated with both SWB and PWB, along with other indices of human happiness (e.g., Anglim & Grant, 2016; Sun, Kaufman, & Smillie, 2018). To date, most research examining the personality correlates of SWB has focused on the Big Five (DeNeve & Cooper, 1998; Steel et al., 2008). These five broad domains of personality emerged from decades of research seeking to identify the major lines of covariation among trait terms, and provide a robust organizing framework for personality psychology as a whole (Anglim & O'Connor, 2019; John & Srivastava, 1999). However, the Big Five domains do not provide—nor were they ever intended to provide—a complete description of personality. Personality traits can be hierarchically arranged at multiple levels both above (e.g. Anusic, Schimmack, Pinkus, & Lockwood, 2009; DeYoung, 2006: Digman, 1997; Musek, 2007; Veselka et al., 2009) and below (e.g. Costa & McCrae, 1995; DeYoung, Quilty, & Peterson, 2007: Mottus, Kandler, Bleidorn, Riemann, & McCrae, 2017; Mottus McCrae, Allik, & Realo, 2014) the five broad domains. In addition, a prominent alternative to the Big Five, the six-factor HEXACO model (Ashton, Lee, & De Vries, 2014), has received increasing interest and support. Researchers have thus begun to expand knowledge of the relation between personality and well-being by shifting to different levels in the personality trait hierarchy within the Big Five, as well as within the HEXACO framework (Aghababaei & Arji, 2014; Anglim & Grant, 2016; Marrero Quevedo & Carballeira Abella, 2011; Schimmack, Oishi, Furr, & Funder, 2004; Sun et al, 2018). To strengthen and consolidate this emerging research, we aim to address several fundamental gaps in the literature. First, despite meta-analytic work relating the Big Five domains to SWB (De-Neve & Cooper, 1998; Steel et al., 2008), no equivalent metaanalysis has examined how the Big Five relates to PWB, or how the HEXACO model relates to either SWB or PWB. Second, the meta-analysis of Steel et al. (2008) focused exclusively on the NEO and the meta-analysis of DeNeve and Cooper (1998) largely relied on categorizing personality measures that predated the Big Five. Third, existing research examining facets of the Big Five and their incremental prediction of well-being above and beyond the Big Five domains suffers from several methodological limitations, including small sample sizes, biased statistics, invalid meta-analytically derived correlation matrices, and incomplete reporting (see the section below on Incremental Prediction for details; for a critical review, see Anglim & Grant, 2014). Fourth, there has been no robust examination of how facets of the HEXACO model map to dimensions of well-being. To address these gaps, we present a meta-analysis that synthesizes the existing literature, and a systematic examination of the data sets with the largest sample sizes that have examined facet-level associations of Big Five and HEXACO frameworks with both SWB and PWB. We believe this research provides the most comprehensive assessment yet of how personality traits are linked to indices of human flourishing. Subjective and Psychological Weil-Being Whereas previous studies have adopted a range of different perspectives on well-being (Diener & Choi, 2009; Diener, Oishi, & Lucas, 2003; Diener, Suh, Lucas, & Smith, 1999; Lucas & Diener, 2008), we focus on the complementary perspectives of SWB and PWB. Several decades ago, Ed Diener and colleagues operation-alized SWB as high life satisfaction combined with high levels of positive affect and low levels of negative affect (Deci & Ryan, 2008; Diener, 1984; Lucas, Diener, & Suh, 1996). Contrastingly, Carol Ryff and colleagues have operationalized PWB using a six-dimensional framework comprising positive relations, autonomy, environmental mastery, personal growth, purpose in life, and self-acceptance (McGregor & Little, 1998; Ryan & Deci, 2001; Ryff & Keyes, 1995). Definitions and example items for all of these dimensions are depicted in Table 1. Although all nine well-being dimensions have moderate to large intercorrelations, they each appear to capture discrete aspects of well-being (Anglim & Grant, 2016; Sun et al, 2018). Despite the influence of situational factors on short-term fluctuation in mood, and the longer-term impact that significant life events appear to have on well-being—for example, marital transition (Lucas, Clark, Georgellis, & Diener, 2003), acquiring a disability (Lucas, 2007), or approaching death (Gerstorf et al., 2008)—measures of well-being otherwise appear very stable over time (Fujita & Diener, 2005; Schimmack & Oishi, 2005). For example, in a recent, large panel study, Anglim, Weinberg, and Cummins (2015) obtained 8-year test-retest correlations for life satisfaction approaching .80. Furthermore, twin studies suggest that SWB is reasonably heritable (Weiss, Bates, & Luciano, 2008). For example, in a large sample of Norwegian Twins, R0ysamb, et al. (2018) found the twin-cotwin correlations for life satisfaction for monozygotic twins (r = .31) was much larger than for dizygotic twins (r = .15). Grounded in the idea of the "hedonic treadmill" (Brickman & Campbell, 1971), various set-point theories have been proposed to explain these findings. From this perspective, well-being is a homeostatic process that fluctuates around a relatively stable set-point (Cummins, 2015; Headey & Wearing, 1989, 1992). People differ in their set-points, and personality describes the dispositional mechanisms that influence how people experience and perceive the world, which in turn influences set-point dynamics (Headey & Wearing, 1989, 1992). Descriptive Models of Personality Traits Personality traits describe relatively stable patterns of affect, cognition, and behavior. The early history of research on personality traits was characterized by a huge proliferation of trait constructs and scales to measure them. Subsequently, emerging from the lexical tradition in the United States, the Big Five traits of neuroticism, extraversion, openness, agreeableness, and conscientiousness has functioned as a powerful synthesizing framework (Costa & MacCrae, 1992; Goldberg, 1993; McCrae & John, 1992). However, the Big Five is not the only game in town. In particular, the six-factor HEXACO model, derived from the same lexical approach but in different (European and East Asian) language PERSONALITY AND WELL-BEING 281 Table 1 Components and Sample Items for Personality, SWB, and PWB Construct Components/sample items Big Five Neuroticism Extraversion Openness Agreeableness Conscientiousness HEXACO Honesty-humility Emotionality Extraversion Agreeableness Conscientiousness Openness Interstitial traits SWB Satisfaction with life Positive affect Negative affect PWB Positive relations Autonomy Environmental mastery Personal growth Purpose in life Self-acceptance Facets: Anxiety, Hostility, Depression, Self-consciousness, Impulsiveness, Vulnerability to Stress Aspects: Withdrawal, Volatility Facets: Warmth, Gregariousness, Assertiveness, Activity, Excitement Seeking, Positive Emotion Aspects: Enthusiasm, Assertiveness Facets: Fantasy, Aesthetics, Feelings, Actions, Ideas, Values Aspects: Openness/Creativity, Intellect Facets: Trust, Straightforwardness, Altruism, Compliance, Modesty, Tendermindedness Aspects: Politeness, Compassion Facets: Competence, Order, Dutifulness, Achievement Striving, Self-Discipline, Deliberation Aspects: Orderliness, Industriousness Sincerity, Fairness, Geed Avoidance, Modesty Fearfulness, Anxiety, Dependence, Sentimentality Social Self-Esteem, Social Boldness, Sociability, Liveliness Forgiveness, Gentleness, Flexibility, Patience Organization, Diligence, Perfectionism, Prudence Aesthetic Appreciation, Inquisitiveness, Creativity, Unconventionality Altruism e.g., "In most ways my life is close to my ideal"; "I am satisfied with my life" Frequency of experiencing positive emotions in the last few weeks/months/etc.: e.g., "interested," "excited," "strong," "enthusiastic" Frequency of experiencing negative emotions in the last few weeks/months/etc.: e.g., "depressed," "upset," "guilty," "scared" e.g., "Most people see me as loving and affectionate"; "I enjoy personal and mutual conversations with family members or friends" e.g., "Sometimes I change the way I act or think to be more like those around me" (R); "My decisions are not usually influenced by what everyone else is doing" e.g., "In general, I feel I am in charge of the situation in which I live"; "The demands of everyday life often get me down" (R) e.g., "I am not interested in activities that will expand my horizons" (R); "In general, I feel that I continue to learn more about myself as time goes by" e.g., "I feel good when I think of what I've done in the past and what I hope to do in the future"; "I live life one day at a time and don't really think about the future" (R) e.g., "When I look at the story of my life, I am pleased with how things have turned out"; "I feel like many of the people I know have gotten more out of life than I have" (R) Note. Sample items are from Satisfaction with Life Scale (Diener, Emmons, Larsen, & Griffin, 1985), PANAS (Watson, Clark, & Tellegen, 1988), and Ryff's measure of PWB (Ryff & Keyes, 1995). PANAS = positive and negative affect schedule; PWB = psychological well-being; SWB = subjective well-being; R = reversed item. groups, has emerged as a prominent alternative to the Big Five (see Ashton et al., 2004; De Raad et al., 2014; Lee & Ashton, 2004; Saucier, 2009). HEXACO is an acronym for the six broad traits of honesty-humility, emotionality, extraversion, agreeableness, conscientiousness, and openness. There are strong similarities but also important differences between the Big Five and the HEXACO models (Ashton & Lee, 2005; Ashton et al, 2014; Gaughan, Miller, & Lynam, 2012; Ludeke et al., 2019). In particular, Big Five agreeableness and neuroticism are repartitioned in the HEXACO model to form the three domains of honesty-humility, agreeableness, and emotionality. Honesty-humility, characterized by integrity and modesty, is negatively correlated with antisocial personality traits (e.g., within the Dark Triad framework; Lee & Ashton, 2014) and positively correlated with the modesty and straightforwardness facets from Big Five agreeableness (Ashton & Lee, 2005). HEXACO agreeableness captures patience, forgiveness, and a disposition to not experience anger toward others. Emotionality includes both the negative emotions of anxiety and fearfulness as well as more neutral emotional tendencies such as dependence and sentimentality. In general, conscientiousness, openness, and extraversion in the HEXACO framework are notionally close analogues to their Big Five equivalents (e.g., cross-correlations all above .75 for the NEO-PI R, Gaughan et al, 2012). Both Big Five and HEXACO models are hierarchical frameworks, where each broad domain is characterized by a set of narrower traits or facets (see Table 1; for discussion see Anglim & O'Connor, 2019). In the context of the Big Five, a range of facet-level frameworks have been proposed (e.g., Soto & John, 2017), but the most popular hierarchical framework in research settings has been the NEO Model which characterizes the Big Five in terms of 30 facets (Costa & McCrae, 1995). This model can be measured using the NEO PI-R, NEO PI-3, or the IPIP NEO (a public domain equivalent). More recently, an intermediate level between facets and domains has been proposed, whereby each Big Five domain is divided into two trait 'aspects' (DeYoung et al., 282 ANGLIM, HORWOOD, SMILLIE, MARRERO, AND WOOD 2007). Unlike the facets of the Big Five, the aspects were derived empirically, informed by quantitative genetic models and other considerations, and are thus purported to less arbitrarily cut nature at the joints. The HEXACO model also has a hierarchical representation that includes 25 facets and six domains (four facets for each domain and one interstitial facet; Lee & Ashton, 2018). Personality Traits and Weil-Being: What We Know So Far Most research on the relation between personality and well-being has focused on the Big Five and the three dimensions of SWB (DeNeve & Cooper, 1998; Steel et al., 2008). The results of Steel et al. (2008) were a watershed in this literature, as by this time the Big Five was sufficiently well-established, whereas the earlier meta-analysis by DeNeve and Cooper (1998) required many stand-alone traits to be identified by the authors as proxies of Big Five domains. Focusing exclusively on studies using the Costa and McCrae's NEO, Steel et al. (2008) found that neuroticism was the strongest correlate of SWB followed by extraversion and then conscientiousness. The research also highlighted the unique profile of correlations across the dimensions of SWB where, for example, relatively larger correlations are seen between neuroticism and negative affect, extraversion and positive affect, and openness and positive affect. Although no equivalent meta-analysis exists in relation to PWB, an emerging literature of primary studies has examined correlates with the Big Five (e.g., Grant, Langan-Fox, & Anglim, 2009; Schmutte & Ryff, 1997; Shulman & Hemenover, 2006). Initial research has highlighted the importance of neuroticism, extraversion, and conscientiousness in predicting PWB. Some research suggests that the Big Five may predict PWB more strongly than SWB (Anglim & Grant, 2016). Importantly, each of the six scales have particular Big Five traits that appear to correlate more prominently (Anglim & Grant, 2016; Grant et al, 2009; Melendez, Satorres, Cujino, & Reyes, 2019; Sun et al., 2018), for instance, agreeableness and extraversion with positive relations, openness with personal growth, and conscientiousness with purpose in life. However, meta-analytic estimates are needed to provide a more definitive assessment of these unique cross-correlations. More recently, researchers have correlated the six HEXACO personality domains with dimensions of SWB and PWB (Agha-babaei, 2014; Aghababaei & Arji, 2014; Aghababaei et al., 2016; Maclnnis, Busseri, Choma, & Hodson, 2013; Pollock, Noser, Holden, & Zeigler-Hill, 2016; Romero, Villar, & Lopez-Romero, 2015; Sibley, 2011; Visser & Pozzebon, 2013). Perhaps the most prominent difference seen in the results of these studies, compared to those based on the Big Five, is that HEXACO extra-version is the main correlate of well-being, whereas emotionality has a much weaker relationship. A comparative facet-level analysis of HEXACO and Big Five correlates would assist in understanding these differences. Despite several existing meta-analyses mapping the Big Five domains with dimensions of SWB (DeNeve & Cooper, 1998; Steel, Schmidt, Bosco, & Uggerslev, 2019; Steel et al, 2008), there is a need for an updated meta-analysis of the relationship between the Big Five and SWB. The results of Steel et al. (2008) suggested much stronger and more nuanced relationships between personality and well-being than implied by the meta-analysis of DeNeve and Cooper (1998). However, Steel and colleagues restricted their focus to NEO personality measures, which represents only a fraction of the Big Five personality measures used in research. It is presently unknown whether the results of Steel et al. (2008) generalize to a wider range of Big Five measures. Furthermore, no meta-analysis exists relating the Big Five to the six dimensions of PWB and no meta-analysis exists relating HEXACO domains to either SWB or PWB. Fortunately, as a result of growing interest in these associations, there are now a sufficient number of primary studies to make such a meta-analysis worthwhile. Such an examination would complete the mapping of HEXACO and Big Five domains onto the dimensions of SWB and PWB and provide a more robust assessment of the relationship between Big Five personality and SWB. Research Question 1: What are the meta-analytic correlations of the HEXACO and Big Five personality domains with SWB and PWB? Beyond Domains: How Well Do Narrow Traits Predict Weil-Being? Several researchers have also considered the role of narrow traits of the Big Five in predicting well-being. Some of this research has focused on life satisfaction (Schimmack et al., 2004; Steel et al, 2019), SWB (Marrero Quevedo & Carballeira Abella, 2011; Steel et al, 2008), or both SWB and PWB (Anglim & Grant, 2016; Marrero, Rey, & Hernandez-Cabrera, 2016; Sun et al., 2018). Such research has often highlighted facets such as depression and positive emotions as important predictors, which in turn has highlighted how construct overlap may be relevant. This research fits into a broader literature discussing the importance of narrow traits in providing a more nuanced perspective on criteria of interest (Anglim & Grant, 2014; Anglim & O'Connor, 2019; Judge, Rodell, Klinger, Simon, & Crawford, 2013; Mottus et al., 2017; Ones & Viswesvaran, 1996; Paunonen & Ashton, 2001; Paunonen & Jackson, 2000). It also relates to several unanswered questions about the relative predictive validity of broad and narrow traits, and the need for more empirical evidence regarding the factors that influence the degree of incremental prediction at the facet-level. Such factors may include personality-criteria correspondence, choice of hierarchical personality framework, sample characteristics, criteria characteristics, and measurement approaches. In contrast to the Big Five, no robust facet-level analysis of the HEXACO model and well-being has been conducted. Importantly, reliable estimation would require large samples and the use of the eight-item per facet HEXACO 200 (Anglim & O'Connor, 2019). At present, the best available data come from a facet-level analysis performed by Aghababaei (2014) who correlated the facets of the HEXACO 60 (i.e., two or three items per facet) with a single item measure of life satisfaction in a sample of 288 students. They found that social self-esteem and liveliness had notably stronger correlations than the other HEXACO extraversion facets. The agreeableness facet of patience and the honesty-humility facet of fairness were also notably larger than other facets in their respective HEXACO domains. Also using the HEXACO 60, Aghababaei and Arji (2014) report correlations (n = 215) just for the honesty-humility facets with PWB dimensions and life satisfaction. They PERSONALITY AND WELL-BEING 283 found that sincerity and fairness tended to have slightly larger correlations with PWB than the facets of greed-avoidance and modesty. Although these studies have provided important insights, they have not satisfied the methodological requirements for a robust assessment of facet-level correlations and the incremental prediction of facets (Anglim & Grant, 2014; Anglim & O'Connor, 2019). First, facets and domains need to be measured reliably. In particular, a valid assessment of incremental prediction by facets requires reliable measurement of the variance in facets not shared with personality domains. This is best achieved through the use of long-form measures of personality such as the HEXACO 200, IPIP 300, and NEO PI R 240. Second, large samples are also required. A comprehensive examination of the facet-level correlates of HEXACO with well-being should also help to explain the differences between the HEXACO and Big Five frameworks. Furthermore, relatively little research has systematically examined facet-level correlates between Big Five and SWB/PWB. Some studies have suffered from small sample sizes, and there is a need for a consistent data analytic approach. In particular, examining semi-partial correlations between facets and criteria, after overlap with broad traits is removed provides a powerful way to identify which facets provide unique prediction. Thus, there is a need for large sample studies combining different personality frameworks including the Big Five and HEXACO perspectives. Research Question 2: What are the correlations of the HEXACO and Big Five personality facets with SWB and PWB? Incremental Prediction of Facets Over Domains Beyond estimating facet-level correlates, the degree to which facets provide incremental prediction of well-being remains a fundamental question. In particular, incremental prediction of facets overs domains is important for justifying the loss of parsimony that results from facet-level analyses. The degree to which facets incrementally predict well-being has been actively debated in the literature, especially in relation to life satisfaction (Anglim & Grant, 2016; Steel et al, 2008, 2019). Although some data suggest that the variance explained in life satisfaction might double at the facet-level (Marrero Quevedo & Carballeira Abella, 2011; Steel et al., 2008, 2019; Stephan, 2009), we suspect that the incremental prediction, though substantial, may be more modest than these data suggest. First, Marrero Quevedo and Carballeira Abella (2011) compared predictive validity of the NEO Big Five with a model that includes both the 30 facets of the NEO as well as optimism, self-esteem, and social support (i.e., variables outside the NEO framework). When focusing only on the 30 facets, incremental prediction was around 50%. Second, Stephan (2009) examined the incremental validity of facets only with respect to their parent domain (i.e., the facets of openness were compared only to the domain of openness). However, this approach does not control for overlap that facets have with all other domains. It therefore risks overestimating incremental variance explained by facets. Third, some early literature using small sample sizes (e.g., <200) compared unadjusted r-squared values of domain versus facet regression models. As discussed in Anglim and Grant (2014), applying a correction for the number of predictors in order to obtain unbi- ased estimates of population variance explained is essential, and one reasonable approach is to use an adjusted r-squared correction. This is particularly important in the context of domain and facet regression comparison because of the large difference in the number of predictors. Fourth, Steel and colleagues (Steel et al., 2008, 2019) have conducted meta-analytic regression models to estimate facet-level prediction. However, because researchers rarely report facet-level intercorrelations, these meta-analytic facet-level regressions have to rely on sources other than the primary studies (e.g., test manuals). Facet-level correlations vary from study to study and the inability to accurately represent multicollinearity can dramatically inflate or distort variance explained in regression equations. This is already problematic for meta-analytic regression involving the Big Five domains, and is of more serious concern for regressions comprising 30 highly correlated facet predictors. Finally, the few studies that have compared domain and facet regression models predicting life satisfaction using the NEO framework, and reasonable sample sizes have obtained the following domain and facet adjusted r-squared values, respectively: .40 versus .52 with n = 337 (Anglim & Grant, 2016); .16 versus .22 with n = 554 (based on stepwise facet regression, Marrero Quevedo & Carballeira Abella, 2011); and .24 versus .32 with n = 1,516 (R0ysamb et al., 2018). Thus, an increase in prediction by facets relative to domains of between 20% and 60% seems more likely for life satisfaction. Beyond life satisfaction, Anglim and Grant (2016) also examined incremental prediction in relation to the nine SWB and PWB variables. Although their sample size was too small to yield precise estimates, they found some evidence for levels of incremental prediction varying across outcomes whereby life satisfaction, autonomy, purpose in life, and self-acceptance had relatively more incremental prediction. In summary, the question of incremental prediction of facets over domains in relation to well-being remains unanswered, and methods for synthesizing research findings regarding incremental prediction are still in their infancy. We propose that in addition to measuring criteria of interest, primary studies need to measure reliable full-length hierarchical measures of personality (i.e., typically 8 or more items per facet), and they need to provide (a) raw data, (b) a full intercorrelation matrix between facets, domains, and criteria, or (c) a valid estimate of incremental variance explained consistent with the approach adopted in the metaanalysis; that is, typically this would be the difference in adjusted r-squared between domain and facet regression models, but other approaches such as bifactor models also have merit (Anglim, Morse, De Vries, MacCann, & Marty, 2017; Chen, Hayes, Carver, Laurenceau, & Zhang, 2012). In addition, particularly large samples are needed when estimating incremental prediction of facets with the necessary precision. By obtaining such data, it would be possible to estimate incremental prediction of facets in each sample, and synthesize these findings. Such research could examine how incremental prediction of facets varies across well-being scales (e.g., SWB and PWB scales), personality questionnaires (e.g., IPIP NEO vs. NEO PI), personality frameworks (Big Five vs. HEXACO), and target populations. 284 ANGLIM, HORWOOD, SMILLIE, MARRERO, AND WOOD Research Question 3: What is the relative prediction of broad and narrow personality traits in relation to SWB and PWB and how does this vary across the Big Five and HEXACO? The Present Research In seeking to answer these three research questions, the overall objective of this research is to thoroughly describe relations that the domains and facets of HEXACO and Big Five personality have with the dimensions of SWB and PWB. To achieve these aims, we conducted a set of comprehensive analyses of published domain-level correlations and facet-level data sets. To understand domain-level correlations (RQ1), we conducted a meta-analysis of the domain-level correlates of HEXACO and Big Five personality with the dimensions of SWB and PWB. To provide a systematic assessment of facet-level correlations (RQ2) and incremental prediction of facets-over-domains (RQ3) across well-being measures and various Big Five and HEXACO frameworks, we adopted a multipronged approach. This included collecting new data, reanalyzing partially reported raw-data, merging data sets where equivalent measures were used, and analyzing complete correlation matrices where these were reported. All of the data sets involved included (a) the nine well-being variables, (b) reliable, full-length personality measures, and (c) moderate to large sample sizes. Importantly, the combined sample size of these data sets is an order of magnitude larger than previous attempts to estimate incremental prediction of facets, and will thus provide the first robust examination of that question. Method All data, scripts, materials, and supplemental analyses are available on the Open Science Framework: https://osf.io/42rsy. Meta-Analysis Our meta-analysis served to estimate cross-sectional self-report relations that the HEXACO and Big Five Domains have with SWB and PWB. Literature Search The literature search sought to identify any study that reported a correlation between Big Five or HEXACO Personality and the dimensions of SWB or PWB. The final literature search reported in this paper was conducted in August, 2019. Keyword searches were conducted in Scopus and PsycINFO, which included dissertations and foreign language articles. The primary search sought to identify articles that included (a) at least one personality-related keyword indicating that the Big Five or HEXACO was used, which included any personality domain name (e.g., extraversion, neuroticism, honesty-humility) or a common test or framework name (e.g., BFI, NEO, HEXACO, Big Five, Big 5, FFM, Five Factor Model, etc.), (b) the word personality, and (c) a well-being related term (e.g., SWB, PWB, subjective well-being, life satisfaction, satisfaction with life, positive affect, negative affect, etc.). Second, a search for well-being related terms was performed on the more than 600 HEXACO-related references listed on http:// hexaco.org/references. Third, references from key meta-analyses on personality and well-being were included (i.e., DeNeve & Cooper, 1998; Heller, Watson, & Hies, 2004; Lucas & Fujita, 2000; Steel et al., 2008, 2019). After merging the above sources and removing obvious duplicates, the Combined Dataset consisted of 2472 articles. Based on title and abstracts screening, the full-text was examined for 60.5% of these articles. In addition to the articles that met the inclusion criteria, a further 249 articles were identified where relevant variables were measured but the correlations were not reported or not completely reported. The corresponding author of each of these articles was sent an e-mail inviting them to provide either the correlation matrix or the data from which we could compute the correlation matrix. When a working corresponding author's e-mail could not be found, another author or Doctoral supervisor was emailed. Contacted authors also provided several additional studies that met the inclusion criteria of our meta-analysis. Several of these additional studies were unpublished or from articles where the correlations were not reported. This process of contacting authors resulted in 68 additional studies being included in the metaanalysis (11 supplied data; 57 supplied correlation matrices). Several additional sources of correlations were as follows: We obtained correlations from six studies in which the correlation matrices were not otherwise published that were reported in the meta-analysis on personality and various forms of satisfaction by Heller et al. (2004). We included the domain-level correlations from the two facet-level studies reported in the current paper that have not previously been reported (i.e., the Combined Dataset and the NEO Dataset). We also computed correlations for six studies that did not report correlation matrices but included a dataset with the publication (e.g., data on the OSF, PlosOne, other data repository). After collating the studies, 17 studies were excluded for one of the following reasons. First, studies were excluded if they reported correlations that used a sample that overlapped with another study. This was common with large panel studies such as the GSOEP, HILDA, BHPS, and MIDUS as well as some individual small-scale studies. In these cases, we sought to retain the article that provided the most comprehensive study in terms of sample and measurement. Second, several studies were excluded because they used nonstandard measurement of personality or well-being that was not initially excluded by our exclusion rules, but were flagged because they produced outlier correlations (e.g., IPIP HEXACO, asking about life satisfaction in the past, etc.). Third, we excluded studies that had outlier correlations combined with other concerns about data integrity. In several studies, there were strong indicators that a large proportion of participants were not completing the study conscientiously as evidenced by use of samples such as Mechanical Turk, very large average correlations between the Big Five (e.g., above .6), exclusion of large numbers of participants due to failing attention checks combined with attention checks that would not be sufficient to identify all nonconscientious responders, and relatively undifferentiated personality-well-being correlations. Other indicators of concern included correlations close to zero between well-being variables and poorly written articles. The final cleaned database consisted of 377 articles and 462 studies. Note that in six samples both HEXACO and Big Five personality were measured, and these were treated as two separate studies. Likewise, some articles reported correlations PERSONALITY AND WELL-BEING 285 separately for different groups (e.g., males and females; patients and controls), and these were also treated as separate studies. Articles were retained if they reported a correlation between a relevant personality variable (i.e., HEXACO or Big Five) and a relevant well-being variable. In order to focus our primary meta-analytic estimates on studies that used reliable measures, we classified correlations into core and noncore. If the personality trait was measured with eight or more items and the well-being dimension was measured with five or more items, the correlation was classified as core. For reporting purposes, we classified a study as core if it had one or more core correlations. Sixteen studies had a mix of core and noncore correlations. Importantly, in recent years there has been a proliferation of short-form measures of personality (e.g., TIPI, BFI 10, Mini-IPIP, etc.). There are also a wide range of short-form adaptations used in individual studies. In contrast, studies classified as core tended to use reliable, well-validated and well-established measures of personality and well-being. The focus on these core studies also makes results more comparable across the Big Five and HEXACO, where HEXACO personality is typically measured with 60, 100 and 200 item formats. It also enables more direct comparison with the meta-analysis by Steel et al. (2008) which focused exclusively on the NEO where the most common formats involve 12 (NEO FFI) and 48 (NEO PI R) items per factor, respectively. It also reduces the need to rely on problematic assumptions related to estimating reliability and correcting for measurement error. Nonetheless, we do report results for the full set of studies in the section on moderator analysis. Eligibility Criteria and Data Coding Procedures Several criteria needed to be satisfied for correlations to be retained in the meta-analysis. For consistency, the study needed to involve self-report measurement of both personality and well-being. Second, personality needed to be measured with either a standard measure of the HEXACO (e.g., HEXACO 60, 100, 192, 200, etc.) or a measure explicitly designed to assess the Big Five. We excluded the one study by Churchyard, Pine, Sharma, and Fletcher (2014) that used the IPIP HEXACO, largely because this is based on an early model of HEXACO that excluded social self-esteem. This also resulted in the exclusion of studies that used the Eysenck Personality Inventory (EPI) or the Eysenck Personality Questionnaire (EPQ). Detailed meta-analysis of the EPI and EPQ are already available in Steel et al. (2008), and we wanted to focus on measures that were explicitly designed to partition personality trait variance into the Big Five or HEXACO. We similarly excluded measures that can be scored to derive a Big Five measure but were not designed to measure the Big Five. Third, the well-being measure needed to be designed to measure satisfaction with life, positive affect, negative affect (i.e., SWB) or the six scales of Ryff's measure of PWB. In relation to life satisfaction, we sought to only include pure measures of life satisfaction. Life satisfaction was typically (82%) measured using Diener's Satisfaction with Life Scale (Diener, Emmons, Larsen, & Griffin, 1985). We also included single-item measures of life satisfaction, composite measures of life satisfaction that sum satisfaction with various life domains (e.g., Personal Weil-Being Index), modified versions of the Satisfaction with Life Scale, and a few other focused scales. We excluded any life satisfaction measure which included a broader set of well-being indicators. To be included, positive affect and negative affect needed to be measured as the sum of items asking about the frequency of experiencing a set of positive and negative emotions, respectively. The vast majority (86%) of studies used the PANAS (Watson, Clark, & Tellegen, 1988) or a variant of the PANAS. We excluded studies that measured affect using experience sampling methods because there was a lack of standardization in how affect was measured and aggregated to the person-level. We also excluded measures of affect that were obtained following experimental manipulation or that were in response to stimuli. To be included, PWB needed to be measured using an official measure of Ryff s conception of the six dimensions of PWB. This mostly included 42-, 54-, and 84-item versions of Ryff s scales and their translations. We focused exclusively on the six scales and not overall measures of PWB. Data Extraction For each included study, we extracted the following study features: sample size, personality measure, life satisfaction measure, positive affect measures, PWB measure, proportion female, mean age, country of sample, type of sample (e.g., university students, Mechanical Turk, Workers, Community, etc.), the source of the correlations (e.g., from the article, provided following correspondence with author, etc.), reference details, and additional notes. Correlations were extracted by copying the correlation matrix into Excel, extracting the correlations in the order they appeared in the correlation matrix and then using data transformations to convert into a standardized order. All study feature and correlation extraction was performed by the first- and fifth-author of this paper. All correlations were extracted by one author and checked for accuracy by the other. To further identify data entry errors, reporting errors by original authors, and problematic studies, we obtained z-scores for all correlations by correlation type (i.e., there were 99 different types of correlations based on the 11 personality traits and 9 well-being variables). We closely examined correlations with absolute z-scores larger than 2.5. In a few cases, researchers had made an error in reporting their correlations (e.g., omitting the minus sign on correlations with neuroticism) and this was corrected. In other cases, we examined the study more carefully and identified indicators that the study was problematic (nonconscientious participants; failure to exhibit universal features of correlations in this area such as correlations between well-being), and these studies were excluded as described earlier. Data Analytic Approach Meta-analytic correlations were estimated using a random-effects model using the metafor package in R (Viechtbauer, 2010). The standard deviation of true effect sizes (i.e., t) was estimated using restricted maximum-likelihood estimation. Meta-analytic estimates were obtained using both observed correlations and correlations corrected for measurement error. Relatively few studies provided scale-level reliability information, so we relied on more general sources based on the test 286 ANGLIM, HORWOOD, SMILLIE, MARRERO, AND WOOD used, and where this was not available we estimated reliability as the average reliability for tests in the database with equivalent numbers of items per factor. Facet-Level Analysis Identifying data sets. To provide a comprehensive assessment of facet-level correlates and incremental prediction, we sought to identify all studies that had included a hierarchical measure of personality that enabled reliable facet-level measurement, and that included measurement of SWB and PWB. In order to estimate incremental prediction, we needed to have either (a) the raw data, (b) the full correlation matrix between facets, domains, and criteria, or (c) the adjusted r-squared values for the domain and facet regression equations. Based on these criteria, we identified three existing data sets that could be analyzed: the NEO Dataset (Marrero et al., 2016), the IPIP NEO Dataset (Anglim & Grant, 2016), and the Big Five Aspects Dataset (Sun et al, 2018). We also conducted an additional study that measured 200-item HEXACO PI R, 300-item IPIP NEO, and well-being. Importantly, this study provided a facet-level assessment using the HEXACO model, and substantially increased the sample size for the IPIP NEO. The resulting four data sets each provide the large samples needed for assessment of incremental variance explained by facets over domains. We note that the identification of the above data sets was based on a systematic search of studies measuring personality facets with any measure of SWB or PWB. Common issues included (a) very small sample sizes for estimating incremental prediction (e.g., under 200), (b) only partial measurement of facets, (c) focus on a limited set of well-being measures (e.g., only life satisfaction was common), (d) use of nonstandard measures of PWB, (e) the study was a meta-analysis, (f) the study was a reanalysis of existing data, or (g) the personality assessment had poor facet-level psychometric properties. We briefly note two relevant data sets that did involve large samples. First, R0ysamb et al. (2018) does provide a valid estimate of incremental prediction of life satisfaction by the NEO PI-R. However, they did not measure any other well-being indicators. Second, Romero et al. (2015) reported domain-level correlations (but nothing at the facet-level) between personality (HEXACO 100 and NEO PI-R) and dimensions of SWB and PWB. However, we were unable to obtain the data or full facet-level correlations needed to estimate incremental prediction in this dataset. Data sets. NEO dataset. Participants were 1,673 Spanish adults (52% female; age in years M = 38.9, SD = 13.3, range: 17 to 89). Participants were recruited by university students instructed to target participants of different ages and professions. Participants completed Spanish translations of the NEO PI R and well-being measures, administered individually. Although a subset of this data was analyzed in Marrero et al. (2016), facet-level correlations and incremental prediction by facets were not reported. Thus, the analyses presented here are novel. Moreover, this is the largest sample yet reported examining a hierarchical measure of personality in combination with a full set of SWB and PWB measures. This large sample is particularly crucial for deriving precise estimates of incremental prediction. Combined dataset. We conducted a new study in which me measured the HEXACO PI R, the IPIP NEO, and both SWB and PWB. This enabled (a) the first rigorous estimate of HEXACO correlates of SWB and PWB at the facet-level, (b) a more robust assessment of the correlates of the IPIP NEO with SWB and PWB, (c) clarity regarding the similarities and differences between the HEXACO and IPIP NEO frameworks, and (d) an opportunity to examine the combined prediction of HEXACO and the IPIP NEO. The final sample consisted of 465 Australian university students (79% female; age in years M = 25.1, SD = 7.8, range: 18 to 56), based on an initial sample of 578, from which 113 cases were dropped because of incomplete data. Because of the large number of items, data was collected online over two sessions. In the first session, participants completed demographics, the 300-item IPIP personality measure, the well-being measures, and measures that did not form part of this study (i.e., problematic smartphone usage, reported in Horwood & Anglim, 2018; Horwood & Anglim, 2019). In the second session, completed on average 28 days later, participants completed the 200-item HEXACO PI R. IPIP dataset. This sample (n = 903) combines data from three related sources. First, it uses the IPIP NEO data from the Combined Dataset (n = 465). Second, it includes cases from the Combined Dataset that were excluded because they did not have matching HEXACO data (n = 102). Finally, 336 cases were obtained from Anglim and Grant (2016), which was also based on an Australian university student sample and used identical measures of personality (i.e., the 300 item IPIP NEO Inventory) and well-being to those used in the Combined Study. HEXACO dataset. This is the Combined Dataset focusing on the HEXACO-PI-R data (n = 465). Big Five aspects dataset. A study by Sun et al. (2018) examined the Big Five Aspects in relation to SWB and PWB across two samples (nl = 205, n2 = 501). We pooled the correlations across the two data sets by weighting correlations by their respective sample sizes, giving a final sample size of 706. Although Sun et al. (2018) reported the variance explained by the 10 aspects, they did not report the variance explained by the Big Five. Thus, we sought to compute this value and thereby assess the incremental prediction of the 10 aspects over and above the Big Five. We calculated adjusted r-squared using the setCor function in the psych package in R (Revelle, 2018) which enables regression analyses to be performed on correlation matrices. Measures Satisfaction With Life Scale. This well-established five-item measure (Diener et al., 1985) provides a measure of overall life satisfaction. Items were rated on a 7-point scale (1 = strongly disagree, 2 = disagree, 3 = slightly disagree, 4 = neither agree nor disagree, 5 = slightly agree, 6 = agree, 1 = strongly agree). The scale score was the mean of items. The NEO Dataset used the Spanish version of the measure (Vazquez, Duque, & Hervas, 2013), and the English version was used in all other data sets. Positive and negative affect. The IPIP, HEXACO, and NEO data sets measured positive and negative affect using the PANAS (Watson et al., 1988). The PANAS consists of two scales that measure the frequency with which positive and negative affect is experienced. In the current study, participants were asked about how frequently they had experienced the emotions in the past few PERSONALITY AND WELL-BEING 287 weeks. The 20 items each concerned a different emotion and were rated on a 5-point scale (1 = very slightly or not at all, 2 = a little, 3 = moderately, 4 = quite a bit, 5 = extremely). Scales were scored as the mean of items. The NEO Dataset used a version of the measure translated into Spanish by Marrero et al. (2016). The Big Five Aspects Dataset measured positive and negative emotions using six-items from the PERMA-Profiler (Butler & Kern, 2016). Psychological well-being. Ryff's (1989) scales were used to measure the six proposed dimensions of psychological well-being. Items were rated on a 6-point scale (1 = strongly disagree, 2 = disagree somewhat, 3 = disagree slightly, 4 = agree slightly, 5 = agree somewhat, 6 = strongly agree). The scale consisted of positively and negatively worded items, and scale scores were the mean after item reversal. The NEO Dataset used the 84-item Spanish translation of Ryff's PWB measure (Diaz et al., 2006). The IPIP and HEXACO data sets used the standard 84-item version. The Big Five Aspects data sets included two samples, where Sample 1 used the 54-item version and Sample 2 used the 42-item version. NEO personality. The NEO Dataset measured the Big Five and 30 Facets of the NEO model of personality using the official Spanish translation of the 240-item Revised NEO Personality Inventory. Four items were excluded because of low corrected-item-total correlations (<.20). IPIP NEO personality. The IPIP and Combined Data Sets measured the 30 facets and five domains of the NEO model (Costa & McCrae, 2008) using the 300 item IPIP-NEO Inventory (Goldberg, 1999; Goldberg et al., 2006). Items were rated on a 5-point scale (1 = very inaccurate, 2 = moderately inaccurate, 3 = neither inaccurate nor accurate, 4 = moderately accurate, 5 = very accurate). Scale scores were the mean after any item reversal. The scales have an average correlation with corresponding NEO-PI-R scales of .73, or .94 when corrected for measurement error (Goldberg, 1999). HEXACO personality. The HEXACO Dataset measured personality traits using the full-length 200-item version of the HEXACO PI-R (Ashton et al., 2014; Lee & Ashton, 2004, 2006). The measure consists of six domain scales and 25 facet scales. Each domain scale consists of four facet scales, and there is one interstitial facet, altruism. Participants responded to items on a scale from 1 = strongly disagree to 5 = strongly agree. Scale scores were obtained as the mean of items after any necessary item reversal. To increase comparability with the Big Five, a HEXACO Neuroticism factor was computed as weighted composite facets as set out in Lee and Ashton (2013): HEXACO Neuroticism = Fearfulness + 3 X (Anxiety) + Dependence + 3 X (6 - Social Self-Esteem) + (6 - Liveliness) + (6 - Patience) + (6 - Prudence). Big Five aspects personality. In the Big Five Aspects Data-set, the five domains and 10 aspects were measured using the 100-item Big Five Aspect Scales (DeYoung et al., 2007). The Big Five Aspect Scales were developed using items from the IPIP. The response scale ranged from 1 = strongly disagree to 5 = strongly agree. Data analytic approach. We broadly followed the methodology for reporting facet-level correlations and incremental prediction set out in Anglim and Grant (2014). For each personality measure we report zero-order correlations between facets and the dimensions of SWB and PWB. In the online supplemental mate- rials, we report semipartial correlations that remove the shared variance between the facet and the five domain-level personality factors. They provide an estimate of the unique prediction provided by the facets over and above the domains. The square of the semipartial correlation is equivalent to the percentage of incremental variance explained by a regression model that adds the facet of interest (e.g., gregariousness) as a predictor to one with only the domains (e.g., the Big Five). Incremental prediction of facets over domains was obtained by taking the difference in the adjusted r-squared values for a regression model with domains as predictors to one with facets as predictors. Results Summary of the Literature A summary of the studies included in the meta-analysis is provided in Table 2, with further details provided in the OSF repository. In total, the meta-analysis included 4,153 correlations (3,246 core; 907 noncore). Table 3 provides an overview of the included studies for the combined, core, and noncore samples. The combined sample consisted of 462 studies and a total sample of 334,567 participants. Most scales of personality measures involved 8 to 15 items. The most common personality frameworks were the NEO and the BFI. The number of studies that met the inclusion criteria has grown dramatically since the meta-analysis by Steel et al. (2008). More studies were from the 5-year period from 2010 to 2014 than from before 2010, and in the last 4.5 years the number of studies per year has increased even further. This may reflect the general growth in science, the expanding number of journals, the accessibility of international journals and PhD theses, and the increasing popularity of the Big Five, the PAN AS, and life satisfaction measurement. Meta-Analytic Correlations Table 4 provides an overall summary of the meta-analytic correlations between personality and well-being based on the core studies. Detailed reporting of the meta-analytic observed and reliability-corrected correlations between Big Five and SWB (see Table 5), Big Five and PWB (see Table 6), HEXACO and SWB (see Table 7), and HEXACO and PWB (see Table 8) are presented for the core studies. Overall, the average correlation between personality domains and well-being was .28. If negative affect is reversed, the mean meta-analytic correlation averaged over the nine well-being indicators for the Big Five domains were —.46 (neuroticism), .37 (extraversion), .19 (openness), .25 (agreeableness), and .36 (conscientiousness). The corresponding values for HEXACO domains were .16 (honesty-humility), —.16 (emotionality), .48 (extraversion), .18 (agreeableness), .28 (conscientiousness), and .16 (openness). Thus, for the Big Five, neuroticism was the strongest correlate followed by extraversion and conscientiousness; correlations for openness and agreeableness were more moderate. For HEXACO, extraversion was clearly the strongest correlate. As discussed earlier, although the content of HEXACO emotionality has some similarity with Big Five neuroticism, it also has important differences, and thus it is perhaps not surprising that it had a much weaker correlation with well-being. HEXACO conscientiousness and 288 ANGLIM, HORWOOD, SMILLIE, MARRERO, AND WOOD Table 2 Summary of Studies Included in Meta-Analysis Study N Framework Items SWL PA NA PWB I- Age Country Core Source Aghababaei and Arji (2014) Big 5 Study 3 215 IPIP 10 D W 61 22 IR C FA Aghababaei and Arji (2014) HEXACO Study 3 215 HEXACO 10 D w 61 22 IR C 1 A Aghababaei et al. (2016) Sample 1 422 HEXACO 10 D 70 23 IR C I-A Aghababaei et al. (2016) Sample 2 221 HEXACO 10 D 77 22 PL c I-A Aghababaei et al. (2016) Sample 3 255 HEXACO 10 D 76 24 MY C FA Aghababaei et al. (2016) Sample 4 251 HEXACO 10 D w 68 22 IR C FA Aghababaei et al. (2016) Sample 5 226 HEXACO 10 13 w 91 20 PL C FA Ahadi and Puente-Diaz (2011) Study 1 107 NEO 36 15 P P 50 20 US c FA Ahadi and Puente-Diaz (2011) Study 2 88 NEO 36 15 P P 62 21 US c FA Albrecht, Dilchert, Deller, and Paulus (2014) 913 NEO 48 15 32 37 c FA Albuquerque, de Lima, Matos, and Figueiredo (2012) 398 NEO 48 15 P P 72 41 PT c FA Alfonsi, Conway, and Pushkar (2011) 341 NEO 12 P 53 59 CA c FA Anand, Vidyarthi, Singh, and Ryu (2015) 756 NEO 12 D 58 39 US c FA Anglim and Grant (2016) 337 NEO 60 D P P w 76 21 AU c FA Anglim and Horwood (2019) Big 5 465 NEO 60 15 P P w 79 25 AU c I-A Anglim and Horwood (2019) HEXACO 465 HEXACO 32 15 P P w 79 25 AU c I-A Anwar (2017) 274 BFI 9 P P 22 47 PK c FA Austin, Saklofske, and Mastoras (2010) 475 Adjectives 8 D P P 70 21 CA c FA Aykac et al. (2011) 131 HEXACO 32 15 51 32 GB c FA Baltes, Zhdanova, and Clark (2011) 289 IPIP 10 P 61 38 US c FA Barr (2018) 142 BFI 9 P P 98 AU c FA Baselmans et al. (2019) 8,622 NEO 12 15 36 42 NL c FA Baudin, Aluja, Rolland, and Blanch (2011) 313 NEO 48 15 26 23 FR c FA Bauer and McAdams (2010) 145 BFI 9 15 P P 74 20 US c CA Beer, Watson, and McDade-Montez (2013) 395 BFI 9 P P 50 32 US c DA Belsky, Crnic, and Woodworth (1995) fathers 69 NEO 36 P P 0 31 us c 1 A Belsky et al. (1995) Mothers 69 NEO 36 P P 100 28 us c I-A Benet-Martinez and Karakitapoglu-Aygiin (2003) Asian 199 BFI 9 D 59 20 us c FA Benet-Martinez and Karakitapoglu-Aygtin (2003) European 122 BFI 9 15 59 20 us c FA Benotsch, Lutgendorf, Watson, Fick, and Lang (2000) 198 BFI 9 P P 52 54 us c CA Bianchi, Rolland, and Salgado (2018) Men 222 NEO 12 15 0 43 FR c FA Bianchi et al. (2018) Women 941 NEO 12 15 100 43 FR c FA Biderman, McAbee, Job Chen, and Hendy (2018) Big 5 1,195 NEO 12 P P 76 20 US c FA Biderman et al. (2018) HEXACO 1,195 HEXACO 16 P P 76 20 us c FA Blatný, Millová, Jelínek, and Osecká (2015) 138 NEO 12 15 61 40 CZ c I-A Bogin (2018) 283 Adjectives 8 15 67 18 US c I-A Boland and Cappeliez (1997) 113 NEO 36 D 100 73 CA c FA Bono (2011) 228 NEO 12 15 US c FA Boudreau, Boswell, and Judge (2001) Americans 1,885 NEO 12 15 10 47 us c FA Boudreau et al. (2001) Europeans 1,871 NEO 12 15 6 42 c FA Brajša-Zganec, Ivanovic, and Lipovčan (2011) 392 IPIP 10 15 P P 50 20 HR c FA Bratko and Sabol (2006) 1,166 IPIP 10 15 66 26 HR c FA Brenner, St-Hilaire, Liu, Laplante, and King (2011) Community 29 NEO 12 15 29 28 CA c FA Brenner et al. (2011) Schizophrenia 30 NEO 12 15 30 20 CA c FA Buries et al. (2014) 179 XEO 60 P P 75 20 CA c CA Burton, Plaks, and Peterson (2015) Study 1 619 BFAS 20 15 55 32 US c I-A Burton et al. (2015) Study 2 700 BFAS 20 D 52 33 US c I-A Bye and Pushkar (2009) 385 XEO 12 P P 52 60 CA c FA Cabrera-Darias and Marrero-Quevedo (2015) Online 108 NEO 48 15 P P 71 36 ES c FA Cabrera-Darias and Marrero-Quevedo (2015) Paper 45 NEO 48 15 P P 71 36 ES c FA PERSONALITY AND WELL-BEING 289 Table 2 (continued) Study N Framework Items SWL PA NA PWB F Age Country Core Source Caprara, Fratte, and Steca (2002) Females 300 Other 12 13 100 17 IT C FA Caprara et al. (2002) Males 292 Other 12 1) 0 17 IT C FA Caprara et al. (2012) Study 3 3,589 Other 12 15 58 39 IT C l'A Caprara et al. (2012) Study 5 Italy 689 Other 12 D 56 19 IT C FA Caprara et al. (2012) Study 5 Japan 281 Other 12 D 60 20 JP C l'A Caprara et al. (2012) Study 5 Spain 302 Other 12 D 64 28 ES C I-'A Carmona-Halty and Rojas-Paz (2014) 235 Other 19 D 34 21 CL C FA Carrillo, Prado-Gasco, Fiszman. and Varela (2012) 356 BFI 9 D 24 24 ES C FA Castro Solano and Cosentino (2018) 302 BFI 9 D 52 39 AR C CA Cellini, Duggan, and Sarlo (2017) 498 BFI 9 P P 71 27 IT C FA Chambers (2004) 238 NEO 12 13 P P 0 30 C FA Chan, Luciano, and Lee (2018) 349 BFI 9 D P P 55 62 c CA Chen and Carey (2009) 113 NEO 12 13 54 20 HK c l'A Chen (2011) 107 NEO 48 13 63 35 US c l'A Chen, Hayes, Carver, Laurenceau, and Zhang (2012) 383 NEO 48 D P P 58 19 US c FA Chen (2015) 371 NEO 12 13 P P 75 21 CX c: l'A Choi and Lee (2014) 373 IPIP 10 13 23 33 KR c l'A Clark, Lelchook, and Taylor (2010) 322 IPIP 10 P P 73 24 US c l'A Clifton et al. (2019) Study 2 562 BFI 9 13 O O 51 37 US c CA Compton, Smith, Cornish, and Quails (1996) 338 NEO 36 13 39 26 US c FA Costa and MacCrae (1992) 364 NEO 48 o o c l'A Cotter and Fouad (2011) 172 NEO 12 13 67 21 US c l'A Courneya et al. (2000) 56 NEO 12 13 o o 41 60 CA c l'A Cowan (2019) 159 NEO 12 13 64 56 US c l'A Crouch (2016) 562 NEO 12 13 41 21 US c FA Crowe, LoPilato, Campbell, and Miller (2016) 914 IPIP 12 13 P P 62 34 US c CA de Frias. Dixon, and Backman (2003) 528 NEO 36 o o 67 68 CA c l'A De Gucht, Fischler, and Heiser (2004) 377 NEO 12 P P 73 44 c l'A Delfabbro, Winefield, Anderson, Hammarstrom, and Winefield (2011) 2,266 NEO 12 () 60 15 AU c CA Di Fabio and Saklofske (2014) 164 Other 12 13 56 18 IT c l'A Di Fabio and Palazzeschi (2015) 168 Other 12 13 p 63 20 IT c l'A Di Fabio, Palazzeschi, and Bucci (2017) 258 Other 12 13 41 46 IT c l'A Di Fabio and Kenny (2018) 241 Other 12 13 p p 63 24 IT c l'A Di Nuovo (2009) 1,080 Other 12 13 50 IT c l'A Dimotakis, Conlon, and Hies (2012) 112 NEO 48 p 39 21 US c l'A Donofrio (2005) 138 NEO 48 13 75 33 US c FA Drezno, Stolarski, and Matthews (2019) 379 IPIP 10 13 34 36 PL c FA Drobnjakovic. Dinic, and Mihic (2017) Study 1 400 HEXACO 16 p p 74 RS c DA Drobnjakovic (2019) 377 HEXACO 10 p p 49 33 RS c DA Dumitrache et al. (2015) 400 NEO 12 13 W 62 75 ES c CA Egan, Chan, and Shorter (2014) 860 IPIP 10 13 69 30 I c CA Etxeberria, Urdaneta, and Galdona (2019) 65 to 84 155 NEO 12 13 p p 58 74 ES c l'A Etxeberria et al. (2019) 85 to 104 102 NEO 12 13 p p 61 94 ES c l'A Fagley (2012) 243 BFI 9 D 63 23 US c CA Fagley (2018) 236 BFI 9 p p 64 19 US c l'A FitzMedrud (2009) 119 NEO 12 13 p p 82 35 US c l'A Fortunato (2002) 206 Adjectives 8 13 34 50 US c FA Fossum and Barrett (2000) Sample 1 205 NEO 48 p p 71 US c FA Fossum and Barrett (2000) Sample 2 241 NEO 4S p p 65 US c: l'A Fowler, Davis, Both, and Best (2018) 448 BFI 9 13 75 29 CA c l'A Fox and Moore (2019) 142 NEO 12 p p 70 21 I c CA Froehlich (2005) 350 NEO 12 13 0 US c l'A Furr and Funder (1998) 146 NEO 36 13 56 US c FA Galea (2014) 121 BFI 9 13 65 MT c FA Ganginis Del Pino (2012) 305 BFI 9 13 100 38 US c l'A Gannon and Ranzijn (2005) 191 NEO 12 13 67 36 AU c l'A Garcia and Erlandsson (2011) 151 NEO 48 13 67 23 SE c l'A Garcia (2011) 98 NEO 48 13 p p 68 17 SE c l'A (table continues) 290 ANGLIM, HORWOOD, SMILLIE, MARRERO, AND WOOD Table 2 (continued) Study N Framework Items SWL PA NA PWB F Age Country Core Source Goldberg et al. (2017) 156 BFI 9 P 1' w 62 19 i;s C DA Golden (2002) 321 Adjectives 16 D 19 51 US c l'A Gore et al. (2014) Study 2 260 IPIP 10 D 71 US c FA Grady (1996) 140 NEO 48 P P 100 39 CA c FA Graham (2012) Entrepreneurs 88 NEO 12 D 25 US c FA Graham (2012) Students 102 NEO 12 D 54 17 us c l'A Grant, Langan-Fox, and Anglim (2009) 21 1 NEO 12 D P P w 58 36 AU c I;A Guilera et al. (2018) 364 BFI 9 D 60 38 i:s c AD Gutierrez, Moreno-Jimenez, Hernandez, and Puente (2005) 236 NEO 12 O O 86 35 HS c l'A Habarth (2009) 576 Adjectives 8 D 55 45 US c l'A Halama and Dedovä (2007) 148 NEO 12 D 51 17 SK c l'A Halama (2010) 451 NEO 12 D 52 20 SK c l'A Harris (2002) 147 BFI 9 D P P 74 22 us c l'A Hart (1999) Wave 1 282 NEO 48 D 10 34 AU c l'A Hayes and Joseph (2003) 129 NEO 12 D 58 38 GB c FA Hebert and Weaver (2014) 270 HEXACO 10 D 62 25 I c FA Heller, Judge, and Watson (2002) 159 NEO 12 15 P 1' US c FA Heller (2004) 76 BFI 9 D P 1' 80 US c FA Hemenover (2001) 236 NEO 48 P 1' 71 20 us c l'A Hengartner, Graf, and Schreiber (2017) S3I IPIP 48 o C) 66 34 CH c l'A Henriett (2018) 421 BFI 9 D 61 24 HU c l'A Herringer (1998) 162 NEO 48 D 65 22 US c l'A Hill and Allemand (2011) 962 BFI 9 D o o 57 52 CH c l'A Hirsh, Guindon, Morisano, and Peterson (2010) 137 BFI 9 P 1' 72 20 CA c CA Hofer, Busch, and Kiessling (20081 131 XEO 12 D w 55 25 Di: c FA Hogan (2006) 31S IPIP 10 P 1' 85 60 us c FA Holder, Love, and Timoney (2015) 437 NEO 12 15 P 69 20 CA c CA Hossack (1997) 520 NEO 12 D 50 CA c FA Howell (2006) 314 BFI 9 D 62 19 US c l'A Hudson and Roberts (2014) 264 BFI 9 D 53 19 US c l'A Hutz, Midgett, Pacico, Bastianello, and Zanon (2014) American 179 NEO 48 D p I' 63 25 US c l'A Hutz et al. (2014) Brazilian 168 Other 25 D p p 60 22 BR c FA Ioannidis and Siegling (2015) 203 BFI 9 p 1' 71 23 GB c FA Isaacowitz and Smith (2003) 516 NEO 36 p 1' 85 DE c FA Isik and Üzbe (2015) 335 Adjectives S p 1' 57 46 TR c l'A Jacques-Hamilton, Sun, and Smillie (2019) 223 BFAS 20 D p p 68 23 AU c AD Jaksic et al. (2015) 319 IPIP 10 D 58 44 HR c CA James, Bore, and Zito (2012) 150 IPIP 20 D 53 21 AU c l'A Jensen, Kirkegaard Thomsen, O'Connor. and Mehlsen (2019) 259 NEO 12 D 44 DK c FA Jibeen (2014) 251 NEO 12 D 39 30 PK c FA Johnson (2003) 140 NEO 4S p p US c FA Jokela, Bleidorn, Lamb, Gosling, and Rentfrow (2015) 56,019 BFI 9 15 63 33 GB c l'A Jones, Hill, and Henn (2015) 207 Other 12 w 59 ZA c FA Joshanloo and Afshari (2011) 235 BFI 9 D 74 21 IR c l'A Jovanovic (2011) 225 Other 10 D 56 24 RS c l'A Jovanovic (2014) 380 Other 10 D p p 59 22 RS c CA Jovanovic (2019) 500 BFI 9 D 68 17 RS c l'A Kahlbaugh and Huffman (2017) 49 BFI 9 p p 65 74 US c l'A Kahn and Hessling (2001) 278 NEO 12 p p 52 20 US c FA Kämpfe and Parriaux (2010) Sample 1 467 NEO 12 15 56 26 DE c FA Kämpfe and Parriaux (2010) Sample 3 679 NEO 12 D p 1' 69 28 DU c 1 A Kaynak (2018) Older 61 Other 15 p p 48 78 TR c FA Kaynak (2018) Younger 64 Other 15 p 1' 52 21 TR c FA Kirkland, Gruber, and Cunningham (2015) Sample 1 Students 352 BFAS 20 p p 61 19 US c l'A Kirkland et al. (2015) Sample 2 MTurk 459 BFAS 20 p I' 62 33 US c l'A Kirkland et al. (2015) Sample 3 MTurk 178 BFAS 20 p I' 58 34 US c FA Kjell, Nima, Sikström, Archer, and Garcia (2013) Iranian 122 BFI 9 15 p p w 59 15 IR c FA Kjell et al. (2013) Swedish 109 BFI 9 15 p w 65 17 SH c l'A Kluemper (2008) 180 NEO 12 D 42 27 US c l'A PERSONALITY AND WELL-BEING 291 Table 2 (continued) Study A7 Framework Items SWL PA XA PWB F Age Country Core Source Kokinda (2011) 108 Adjectives 8 D 73 38 liS C FA Kong, Wang, Hu, and Liu (2015) 274 NEO 24 D 54 CN C CA Kong, Zhao, You, and Xiang (2019) 136 NEO 12 D 40 CN c CA Kovacs (2007) 450 NEO 12 D 57 22 US c FA Koydemir and Schütz (2012) German 101 BFI 9 D P P 68 24 DE c FA Koydemir and Schütz (2012) Turkey 86 BFI 9 D P P 55 22 TR c FA Krick and Felfe (2019) 259 NEO 12 P P 21 26 DL c CA Kwan, Bond, and Singelis (1997) American 184 NEO 12 C) 71 22 US c FA Kwan et al. (1997) Hong Kong 194 NEO 12 () 55 22 HK c FA Lang, Lüdtke, and Asendorpf (2001) 480 BFI 9 P P 56 DE c FA Langvik, Hjemdal, and Nordahl (2016) 372 NEO 12 P P 76 22 NO c FA Lee, Sudom, and Zamorski (2013) 1,584 BFI 9 P 0 26 CA c FA Letrzing (2019) 206 BFI 9 D P P w 68 39 LS c DA Letzring (2015) 152 IPIP 10 D P P 64 25 US c DA Lightsey et al. (2013) 199 BFI 9 P P 69 24 US c FA Lodewyk (2018) 300 HEXACO 16 P 51 CA c FA Lönnqvist and große Deters (2016) Study 1 153 BFI 9 D P P 61 20 US c FA Lönnqvist and große Deters (2016) Study 2 187 BFI 9 D 79 24 Dli c FA Lopez et al. (2015) 1,643 NEO 12 P P 55 55 NL c AD Lounsbury, Tatum, Chambers, Owens, and Gibson (1999) 249 NEO 12 O 67 22 US c HM Lucas and Fujita (2000) Study 2 142 NEO 36 P 73 US c FA Lucas and Fujita (2000) Study 3 212 NEO 12 P 62 us c FA Lucas and Fujita (2000) Study 5 221 NEO 36 P 61 us c FA MacCann, Lipnevich, Burrus, and Roberts (2012) 354 IPIP 24 O 52 16 us c FA Maclnnis, Busseri, Choma, and Hodson (2013) 245 HEXACO 10 o P P 88 20 CA c FA Mangino (2018) 220 IPIP 20 D 56 us c FA Marcionetti and Rossier (2016) 437 NEO 12 D 47 13 CH c FA Margolis, Schwitzgebel, Ozer, and Lyubomirsky (2019) Study 1 504 BFI 12 D P P w 51 35 c CA Margolis et al. (2019) Study 2 303 BFI 12 D P P w 45 32 I c CA Margolis and Lyubomirsky (2019) 129 BFI 12 D C) o 69 19 US c CA Marrero Quevedo and Carballeira Abella (2011) 554 NEO 48 D P p 64 28 FS c FA Marrero (2019) 1,673 NEO 48 D P p w 52 39 ES c FA Marshall et al. (1992) Sample 1 346 NEO 12 P p 0 20 US c FA Marshall et al. (1992) Sample 2 543 NEO 12 P p 0 19 US c FA Martin. Xejad. Colmar, and Liem (2013) 969 Other 8 D 48 14 AL- c FA McCrae and Costa (1991) 364 NEO 36 C) 0 o 47 US c FA McCullough et al. (2002) Study 2 1,179 Adjectives 8 D 84 45 I c HM McKay (2017) Big 5 127 IPIP 24 D p p 61 22 US c FA McKay (2017) HEXACO 127 HEXACO 10 D p p 61 22 us c FA Melendez, Satorres, Cujino, and Reyes (2019) 618 NEO 12 D p p w 64 70 CO c FA Mellor, Cummins, Karlinski, and Storer (2003) 45 NEO 12 O 96 45 AU c FA Michel and Clark (2013) 380 IPIP 10 p p 54 36 US c FA Miciuk, Jankowski, and Oles (2016) 130 NEO 12 D 62 25 PL c FA Miciuk, Jankowski, Laskowska, et al. (2016) 200 NEO 12 D 50 23 PL c FA Mongrain, Barnes, Barnhart, and Zalan (2018) 648 BFI 9 D 67 32 I c FA Morris, Burns, Periard, and Shoda (2015) 337 NEO 48 D p p 66 20 US c FA Morrison (1997) 307 NEO 12 D 12 US c FA Murray (2002) 7,133 IPIP 10 D 50 52 AL c HM Musek (2007) 301 BFI 9 D p p 40 37 SI c FA Navarro-Prados, Serrate-Gonzalez, Munoz- Rodri'guez, and Di'az-Orueta (2018) 342 NEO 12 D 66 68 LS c FA Neff, Rude, and Kirkpatrick (2007) 177 NEO 12 D p p 71 20 LS c FA Ng, Russell Kua, and Kang (2019) 507 IPIP 10 O O o 51 43 SG c FA Novak et al. (2017) 117 BFI 9 p p 43 57 US c FA Novakov and Popovic-Petrovic (2017) 40 BFI 9 p p 100 55 RS c FA Novoa and Barra (2015) 353 BFI 9 D 53 20 CL c FA (table continues) 292 ANGLIM, HORWOOD, SMILLIE, MARRERO, AND WOOD Table 2 (continued) Study A7 Framework Items SWL PA XA PWB F Age Country Core Source O'Rourke (2004) 192 NEO 12 D 100 61 I C CA O'Rourke (2005) 208 NEO 12 D () 0 54 64 CA C FA Odaci and Cikrik?i (2018) 620 Bl I 9 D 74 21 TR C PA Oken et al. (2017) 134 NEO 12 P P 80 60 US c CA Olesen, Thomsen, and O'Toole (2015) 1,181 NEO 12 D P p 59 22 DK c FA Osma et al. (2018) 428 NEO 12 P p ES c CA Panaccio and Vandenberghe (2012) 181 BFI 9 P p 52 36 CA c l'A Parker, Martin, and Marsh (2008) 523 NEO 12 D 70 22 AU c FA Paulson and Leuty (2016) 270 IPIP 10 P p 42 33 US c FA Pavani et al. (2017) 78 NFO 60 O 0 62 45 FR c FA Pazda and Thorstenson (2018) 262 NEO 12 I' P 68 US c FA Petrides, Pita, and Kokkinaki (2007) 274 Other 40 D 66 26 GR c FA Kandler et al. (2017) 576 NEO 48 D 58 37 US c AD Plopa, Plopa, and Skuzinska (2017) 359 NEO 12 D 81 39 PL c FA Pollock, Noser, Holden, and Zeigler-Hill (2016) 149 HEXACO 10 D P p 47 34 US c FA Pratt (2006) 305 IPIP 10 P p 62 36 US c FA Purvis, Howell, and Iyer (2011) Sample 1 1,858 Adjectives 8 D P p 73 29 US c l'A Purvis et al. (2011) Sample 2 1,065 BFI 9 D 56 41 I c FA Pychyl and Little (1998) 81 NEO 36 D () 0 56 35 CA c FA Qing-Guo, O'Shea, Bajpai, Bajpai, and Yu-Bo (2011) 818 BFI 9 O 44 34 CX c FA Ramanaiah, Detwiler, and Byravan (1995) 245 NEO 36 D 55 23 US c HM Ro (2011) Study 1 429 BFI 9 D w 65 25 US c FA Ro (2011) Study 2 181 BFI 9 w 75 41 US c FA Robinson, Goetz, Wilkowski, and Hoffman (2006) Study 1 246 IPIP 10 P p 74 US c FA Robinson et al. (2006) Study 2 68 IPIP 10 P p 72 US c FA Romero, Luengo, Gomez-Fraguela, and Sobral (2002) 324 NEO 48 P p 36 16 ES c FA Romero, Villar, Luengo, and Gomez- Fraguela (2009) 405 NEO 48 D I' p 61 32 ES c FA Romero, Gomez-Fraguela, and Villar (2012) 583 NEO 48 D P p 72 35 ES c FA Romero, Villar, and Lopez-Romero (2015) 876 HEXACO 16 D P p w 57 41 ES c FA R0ysamb, Nes, Czajkowski, and Vassend (2018) 1,516 NEO 48 D 65 57 NO c FA Ryan and Frederick (1997) Study 3 102 NEO 36 1' p 59 21 US c FA Rzeszutek, Gruszczynska, and Firlag- Burkacka (2019) 530 NEO 12 D 1' p 16 40 PL c FA Sadikovic, Smederevac, Mitrovic, and Milovanovic (2019) Dizygotic 122 NFO 48 D 63 25 RS c FA Sadikovic et al. (2019) Monozygotic 242 NEO 48 D 76 25 RS c FA Saeed Abbasi, Rattan, Kousar, and Khalifa Elsayed (2018) 819 BFI 9 p 62 27 US c FA Saklofske, Austin, Mastoras, Beaton, and Osborne (2012) 216 Adjectives 8 D P 1' 78 20 GB c FA Salter, Smith, and Ethans (2013) Control 36 NEO 48 P p US c FA Salter et al. (2013) Spinal Cord Injury 36 NEO 48 P p US c FA Schimmack, Oishi, Furr, and Funder (2004) Study 1 136 NEO 48 D 74 20 US c FA Schimmack et al. (2004) Study 2 124 NEO 60 D 71 21 US c FA Schimmack et al. (2004) Study 3 143 NEO 48 D US c FA Schimmack et al. (2004) Study 4 344 BFI 9 D 74 CA c FA Schmutte and Ryff (1997) Sample 1 215 NFO 12 () 0 w 53 54 US c FA Schmutte and Ryff (1997) Sample 2 139 NEO 12 w 47 US c FA Schneider, Rench, Lyons, and Riffle (2012) 152 IPIP 10 P p 72 20 US c FA Schwartz, Michael, Zhang, Rapkin, and Sprangers (2018) 541 NEO 12 w 76 44 US c CA Seines, Marthinsen, and Vitters© (2004) 131 NEO 12 D o O w 52 44 NO c FA Sheu, Mejia, Rigali-Oiler, Prim6, and Chong (2016) 849 Adjectives 10 D 58 20 US c FA Sheu, Liu, and Li (2017) 757 Adjectives 10 D 70 21 CX c l'A Shi, Luo, Liu, and Yang (2019) Study 2 208 IPIP 10 D 54 20 CX c FA Shulman and Hemenover (2006) 112 NFO 12 w 47 19 US c l'A Sibley (2011) Study 3 148 HEXACO 10 O 64 20 xz c FA CD tU ■S & PERSONALITY AND WELL-BEING 293 Table 2 (continued) Study JV Framework Items SWL PA NA PWB F Age Country Core Source Simsek (2011) Study 4 106 BI-'I 9 D P P 45 22 TR C FA §im§ek and Koydemir (2013) 721 BI-'I 9 D P P 66 29 TR C CA Sjmsek and Kocayoriik (2013) Study 4 SWB 99 BI'I 9 D P P 54 19 TR C FA Singh and Shejwal (2017) Females 98 NEO 12 P P 100 IS IN C CA Singh and Shejwal (2017) Males 102 NEO 12 P P 0 IS IX C CA Sirianni Molnar (2011) 111 773 Adjectives 8 D P P 93 49 US c FA Sirianni Molnar (2011) Student 538 Adjectives 8 D P P 78 22 US c l-'A Skomorovsky and Sudom (2011) 200 Other 15 D 19 CA c FA Sliter, Withrow, and Jex (2015) 708 IPIP 10 P P 72 21 US c l-'A Sobocko and Zelenski (2015) Study 1 154 BI'I 9 D P P 68 22 CA c CA Sobocko and Zelenski (2015) Study 2 118 BI'I 9 P P 63 20 CA c CA Sorondo (2017) Public Services 25 BI'I 9 P P 62 45 US c FA Sorondo (2017) Technical Services 21 BI-'I 9 P P 62 45 US c FA Soto and John (2017) Study 3 179 BI'I 12 w us c FA Soubelet and Salthouse (2011) 1,175 IPIP 10 D P P 63 c FA Spörrle, Strobel, and Tumasjan (2010) 200 NEO 12 D 50 28 DE c FA Stamatopoulou, Galanis, and Prezerakos (2016) 602 Other 15 D 62 34 GR c l-'A Stanton, Rozek, Stasik-O'Brien, Ellickson- Larew, and Watson (2016) Big 5 293 NEO 48 D 71 46 US c CA Stanton et al. (2016) HEXACO 293 HEXACO 16 D 71 46 US c CA Stanton, Gruber, and Watson (2017) Students 381 BI'I 9 D P P 67 19 us c CA Steca. Capanna, Mecaroni, and Delle Fratte (2005) Females 549 Other 12 D 100 43 IT c FA Steca et al. (2005) Males 601 Other 12 D 0 45 IT c FA Stimson (2010) 89 BI-'I 9 D 79 IS us c l-'A Stolarski (2016) 265 NEO 12 D 54 23 PL c FA Suh, Diener, and Fujita (1996) 115 NEO 24 D O () 63 22 US c l-'A Sulaiman et al. (2013) 315 NEO 12 D P P 41 19 MY c l-'A Suldo, Minch, and Hearon (2015) 624 Other 23 O 63 16 US c l-'A Sun, Stevenson, Rabbani, Richardson, and Smillie (2017) 205 BFAS 20 P 48 35 US c l-'A Sun, Kaufman, and Smillie (2018) 706 BFAS 20 D O O w 54 36 US c FA Szczesniak, Sopiriska, and Kroplewski (2019) 213 NEO 12 D 72 32 PL c FA Tan, Sheffield, Khoo, Byrne, and Pachana (2018) 330 NEO 12 D 100 69 AU c l-'A Tanksale (2015) 183 NEO 12 D p P 51 35 IX c FA Teachman, Siedlecki, and Magee (2007) 325 IPIP 10 p p 64 US c CA Terracciano (2003) 575 NEO 48 p p 63 28 IT c l-'A Tett, Fox, and Wang (2005) 152 Adjectives 8 D p p 66 22 US c l-'A Thingujam (2011) 300 NEO 12 D p p 49 23 IX c l-'A Thomas (2011) 176 IPIP 10 p p 54 31 us c l-'A Thoresen (2000) 440 NEO 12 D p p 39 40 us c FA Thorpe (2015) 197 BI'I 9 O 58 34 us c FA Tov (2012) Study 1 206 IPIP 10 O () O 59 22 SG c FA Tov (2012) Study 2 139 IPIP 10 D O O 66 21 SG c FA Trankle and Haw (2009) 157 BI-'I 9 p p 83 22 AU c 1 A Tuce and Fako (2014) Boys 225 Other 10 O 0 18 BA c l-'A Tuce and Fako (2014) Girls 200 Other 10 O 100 IS BA c l-'A van Allen and Zelenski (2018) 221 IPIP 24 D p p w 75 22 CA c DA Vilhena et al. (2014) 729 NEO 48 O 71 42 PT c l-'A Villieux, Sovet, Jung, and Guilbert (2016) 403 BI'I 9 D p p 86 23 FR c l-'A Vitters0 (2001) 264 Other 12 D o o 19 NO c l-'A Vorkapic and Loncaric (2013) 290 BI'I 9 D 99 37 HR c FA Wahl, Heyl, and Schilling (2012) Hearing Impaired 116 NEO 12 p p 42 S3 DE c FA Wahl et al. (2012) Sensory Unimpaired 150 NEO 12 p p 49 S2 DE c FA Wahl et al. (2012) Visually Impaired 121 NEO 12 p p 59 S3 DE c FA Watson and Clark (1992) Sample 1 532 Adjectives 16 p p US c FA Watson and Clark (1992) Sample 2 236 Adjectives 16 p p US c FA Watson and Clark (1992) Sample 3 224 NEO 36 p p US c l-'A Watson and Clark (1992) Sample 4 325 NEO 12 p p US c l-'A (table continues) 294 ANGLIM, HORWOOD, SMILLIE, MARRERO, AND WOOD Table 2 (continued) ■a ja 3 T3 'S s 1) (U Study N Framework Items SWL PA NA PWB F Age Country Core Source Watson, Hubbard, and Wiese (2000) Dating females 136 NEO 12 D 100 US C HM Watson et al. (2000) Dating males 136 NEO 12 D 0 US C HM Watson et al. (2000) Friends 558 BFI 9 D P P US c CA Watson, Suls, and Haig (2002) Study 2 287 BFI 9 P P 51 US c FA Watson et al. (2002) Study 3 346 NEO 48 P P 61 US c l'A Watson et al. (2004) 576 BFI 9 P P 50 28 US c CA Watson et al. (2007) Study 2 370 BFI 9 P P 67 39 US C CA Watson et al. (2007) Study 3 Patients 329 BFI 9 P P 68 42 US C CA Watson et al. (2007) Study 3 Students 306 BFI 9 P P 63 US C CA Watson, Stasik, Chmielewski, and Naragon-Gainey (2015) Community 372 BFI 9 P P 74 37 US c: CA Watson et al. (2015) Iowa 554 BFI 9 P P 67 19 US c: CA Watson et al. (2015) Notre Dame 493 BFI 9 P P 60 19 US c CA Watson. Stanton, and Clark (2017) 448 BFI 12 P P 53 36 US C CA Webb et al. (2013) 65 NEO 48 P P 49 30 US C FA Weber and Huebner (2015) 344 Other 23 () 55 12 US c l'A West (2007) 148 Other 23 () us c FA White (2011) Dating 262 BFI 9 P P 63 19 us c l'A White (2011) Married 202 BFI 9 P P 50 39 us c l'A Williams and Wiebe (2000) 140 NEO 48 P 55 21 us C l'A Williams and Simms (2018) 336 NEO 24 13 68 40 us C l'A Wilt, Grubbs, Exline, and Pargament (2016) Community 965 BFI 9 D 62 35 us c: l'A Wilt et al. (2016) University Student 418 BFI 9 D 70 us c: l'A Shyh Shin, Boon Ooi, Ang, Oei, and Aik Kwang (2009) Australian 189 Adjectives 8 D 69 19 AU C l'A Shyh Shin et al. (2009) Singaporean 243 Adjectives 8 D 66 18 SG C l'A Wong et al. (2015) 401 NEO 12 P 58 44 CN c l'A Wood, Nye, and Saucier (2010) 259 BFI 9 13 US c l'A Woyciekoski, Natividade, and Hutz (2014) 274 Other 25 13 P P 69 27 BR c l'A Wu, Liu, Guo, Cai, and Zhou (2019) Husband 587 BFI 9 13 0 42 CN C l'A Wu et al. (2019) Wife 587 BFI 9 13 100 41 CN C FA Xu et al. (2017) 2.357 Other 8 0 58 16 CN c FA Yeo (2015) 260 IPIP 10 13 W 51 37 ID c FA Yilmaz and Kafadar (2019) 100 Other 9 P P 59 20 TR c: DA Zeidner and Olnick-Shemesh (2010) 203 Other 12 13 58 16 IL c FA Zellars. Perrewé. Hochwarter. and Anderson (2006) 188 NEO 12 P P 90 40 US c l'A Zhai, O'Shea, Mike, and Yang (2010) 413 BFI 9 0 59 31 CN c FA Zhai, Willis, O'Shea, Zhai, and Yang (2013) 818 BFI 9 () 56 34 CN c l'A Zhang, Mandl, and Wang (2010) 139 BFI 9 13 52 25 DE C l'A Zhang and Howell (2011) 754 Adjectives 8 13 70 25 US C l'A Zhang and Tsingan (2014) 238 BFI 9 P P 71 19 CN C FA Zhu, Woo, Porter, and Brzezinski (2013) 309 BFI 9 13 58 19 US C FA Agbo and Ngwu (2017) 238 TIPI 2 O O 48 22 NG N FA Aghababaei and Tabik (2013) 256 IPIP 4 13 49 23 IR N l'A Aghababaei (2014) 288 HEXACO 10 0 64 21 IR N l'A Aghababaei and Arji (2014) Big 5 Study 1 183 IPIP 10 0 68 21 IR X FA Aghababaei and Arji (2014) HEXACO Study 1 183 HEXACO 10 () 68 21 IR N FA Aghababaei and Arji (2014) Study 2 109 HEXACO 10 () 59 20 IR N FA Antunes, Caetano, Pina, and Cunha (2017) Sample 1 542 IPIP 4 P P 56 33 PT N l'A Balgiu (2018) 496 BFI 2 13 O O w 39 19 RO N l'A Blatný et al. (2018) 2,229 BFI 2 13 43 42 CZ N l'A Brailovskaia and Margraf (2016) Facebook non-users 155 BFI 2 13 64 25 DE N FA Brailovskaia and Margraf (2016) Facebook users 790 BFI 2 13 71 23 DE X l'A Brailovskaia and Margraf (2018) 633 BFI 2 13 66 22 DE X AD Brailovskaia, Bierhoff, and Margraf (2019) 438 BFI 2 D 66 22 DE X CA Carciofo and Song (2019) 767 BFI 2 () P P 20 CN X CA Chopik and Lucas (2019) Men 2,578 BFI 3 0 0 51 DE X l'A PERSONALITY AND WELL-BEING 295 Table 2 (continued) Study N Framework Items SWL PA NA PWB F Age Country Core Source Chopik and Lucas (2019) Women 2,578 BFI 3 O 100 51 DE N FA Cikrikci (2019) 292 TIPI 2 13 66 20 TR N FA Correa, Hinsley, and de Züniga (2010) 959 TIPI 2 O 33 46 US N FA Csarny (1998) 386 NEO 12 o 58 52 US N FA Datu (2014) 210 TIPI 2 D 63 18 PH N FA Datu, Yuen, and Chen (2018) 356 TIPI 2 () O o 67 14 PH N FA Denovan (2018) 306 TIPI 2 D P p 82 20 GB X FA Devcntcr. I.üdtke. Xagy. Retelsdorf, and Wagner (2019) 896 BFI 9 () 29 18 DE X 1-A Dijkstra and Barelds (2009) 3,626 Adjectives 2 D P p 100 46 NL N FA Duckworth, Weir, Tsukayama, and Kwok (2012) 9,649 Other 6 D o o 58 68 US X FA Eakman and Eklund (2012) 224 TIPI 2 D 54 28 US X FA Ebner, Thiele, Spurk, and Kauffeld (2018) Study 2 322 BFI 4 O 67 30 DE X FA Freund and Baltes (1998) 200 NEO 6 p 51 84 DE N FA Furier, Gomez, and Grob (2013) Men 1,608 BFI 2 () 0 52 CH N FA Furier et al. (2013) Women 1,608 BFI 2 () 100 19 CH X FA Gibson (2007) Study 1 240 TIPI 2 D 73 US X DA Glidden, Billings, and Jobe (2006) 295 NEO 12 O 62 43 US X DA Goldstein and Flett (2009) 138 TIPI 2 p p 70 19 CA N 1-A Gore et al. (2014) Study 1 2,566 Other 5 p p 70 US X FA Goswami (2014) 893 IPIP 5 () 61 12 GB N FA Grevenstein and Bluemke (2015) 1,842 BFI 5 D 86 28 DE X FA Grevenstein, Aguilar-Raab, and Bluemke (2018) 1,033 BFI 3 D 75 42 DE N FA Halama, Martos, and Adamovoväü (2010) Hungarian 249 Adjectives 6 13 62 22 HU X FA Halama et al. (2010) Slovak 274 Adjectives 6 13 53 22 SK N FA Hengartner, Kawohl, Haker, Rössler, and Ajdacic-Gross (2016) 1,125 BFI 3 p p 50 30 CH X CA Jennings (2004) 794 Adjectives 7 13 p p 30 72 US X FA Joshanloo and Nosratabadi (2009) 227 BFI 9 () w 49 23 IR X 1-A Kashdan and Steger (2007) 97 Other 5 13 66 20 US X 1-A Kim, Schimmack, Cheng, Webster, and Spectre (2016) American 174 BFI 9 () 80 19 US X CA Kim et al. (2016) Hong Kong 97 BFI 9 () 76 20 HK X CA Knöpfli, Morselli, and Perrig-Chiello (2016) 2,508 BFI 2 13 58 60 CH X DA Lai (2018) 13,424 Adjectives 6 O 47 44 AU X 1-A Augusto Landa, Martos, and Lopez-Zafra (2010) 228 NEO 12 w 84 21 ES N FA Leffel et al. (2018) 499 NEO 3 13 45 US X 1-A Levinson and Rodebaugh (2011) 323 IPIP 4 p 68 19 US N 1-A Lönnqvist and Itkonen (2014) 4,701 Adjectives 6 13 66 33 FI X FA Losoncz (2007) 10,512 Adjectives 6 O 53 44 AU X 1-A Luhmann, Hawkley, and Cacioppo (2014) 414 BFI 2 13 p p 64 35 US X FA Margolis et al. (2019) Study 3 407 BFI 3 () o o 62 36 I X CA Martinez-Molina and Arias (2018) 278 IPIP 4 13 p p 71 22 ES X AD McMahan, Renken, Kehn, and Nitkova (2013) 464 TIPI 2 13 p p w 65 21 US N 1-A Montasem, Brown, and Harris (2013) 218 TIPI 2 13 p p 58 22 GB X 1-A Morsunbul (2014) 793 Other 6 13 64 18 TR N 1-A Naukkarinen, Karkkola, Kuittinen, and Räty (2016) 187 TIPI 2 13 FI X 1-A Ng (2015) 1,972 BFI 2 () 55 42 SG X 1-A Nishimura and Suzuki (2016) 463 Other 5 13 36 19 JP X FA Oishi, Krochik. Roth, and Sherman (2012) African American 33 Other 5 13 o o 76 US X 1-A Oishi et al. (2012) Asian American 46 Other 5 13 o o 76 US X FA Oishi et al. (2012) European American 41 Other 5 13 o o 76 US X FA Oishi, Kohlbacher, and Choi (2018) 1,546 BFI 2 () 52 61 JP X CA Pavot, Diener, and Suh (1998) Study 3 66 NEO 12 () 61 79 US X 1-A Rammstedt, Lechner, and Danner (2018) 1,338 BFI 6 () 50 43 DE X 1-A Reich, Sangiorgio, and Young (2019) 223 TIPI 2 13 77 21 US X 1-A Rigby and Huebner (2005) 211 Other 5 O 51 16 US N FA (table continues) 296 ANGLIM, HORWOOD, SMILLIE, MARRERO, AND WOOD Table 2 {continued) u cd 1) OJ Study N Framework Items SWL PA NA PWB F Age Country Core Source Robinson, Demetre, and Corney (2010) Approaching Retirement 86 TIPI 2 D 54 61 GB N FA Robinson et al. (2010) In Retirement 279 TIPI 2 D 54 64 GB N FA Rodgers et al. (2018) 244 TIPI 2 D W 77 25 I N CA Ryan, Newton, Chauhan, and Chopik (2017) 716 Other 6 P P 55 62 US N FA Saeki, Oishi, Maeno, and Gilbert (2014) 404 BFI 4 O O O 43 20 JP N FA Saiz, Alvaro, and Martinez (2011) 655 Other 12 O ES N CA Schimmack, Schupp, and Wagner (2008) 1,053 BFI 3 O DE N FA Schoeps, Gonzalez, and Montoya-Castilla (2016) Female 182 BFI 2 D 100 42 ES N FA Schoeps et al. (2016) Male 182 BFI 2 D 0 44 ES N FA Seder and Oishi (2012) Study 1 48 Other 1 D 58 US N FA Seder and Oishi (2012) Study 2 36 Other 1 D 64 US N FA Selvarajan, Singh, and Cloninger (2016) 1,130 Adjectives 7 P 51 50 US N FA Sibley et al. (2011) 21,219 IPIP 4 O 59 47 NZ N CA Sodermans and Matthijs (2014) 506 BFI 9 O 49 18 BE N FA Soto and Luhmann (2013) BHPS 13,825 BFI 3 O 55 48 GB N CA Tartaglia, Miglietta, and Gattino (2017) 600 Other 1 D 40 22 IT N FA Tian and Zheng (2007) 1,151 Other 5 O 48 CN N FA Vollmann, Pukrop, and Salewski (2016) 158 BFI 2 O 68 56 DE N FA Wang, Hu, Li, and Tao (2019) 545 IPIP 4 D 28 20 CN N CA Whisman, Uebelacker, Tolejko, Chatav, and McKelvie (2006) Female 416 NEO 12 O 100 68 US N FA Whisman et al. (2006) Male 416 NEO 12 O 0 72 US N FA Wicker (2016) 183 TIPI 2 D 80 US N FA Wigert (2002) 125 NEO 12 O 57 53 US N FA Note. Items = the rounded mean number of items per personality factor; SWL = whether life satisfaction was measured using either D = Diener's Satisfaction with Life Scale or O = other measure; PA and NA = whether the positive and negative affect measures were measured with either P = PANAS or O = other measure; PWB = psychological well-being, and is W when PWB was measured in the study. A blank cell for SWL, PA, NA, or PWB indicates that the construct was not measured in the study in a way that met inclusion criteria for this meta-analysis. F = the percentage of females in the sample. Age is the mean age of the sample. Country is the two-digit ISO country code, and "I" indicates a multi-country English-speaking Internet sample. Core is coded C = core and N = noncore, where core studies included at least one correlation involving a personality scale with at least eight items per factor and a well-being measure with at least five items. Source = the source of the correlations using the following codes: FA = from article; AD = accompanying dataset; CA = correlations provided following contact with the author; DA = data were provided following contact with the author; HM = otherwise unpublished correlations taken from the Heller, Watson, and Hies (2004) meta-analysis. Further details about the nature of the sample in each study are provided in the online repository that accompanies this paper. Samples where HEXACO and Big Five were measured are treated as two separate studies for reporting purposes. BFAS = Big Five Aspect Scales; BFI = Big Five Inventory; IPIP = International Personality Item Pool; TIPI = Ten-Item Personality Inventory. openness exhibited similar correlations with well-being to their Big Five analogues. The average correlations with well-being for honesty-humility and HEXACO agreeableness were also similar to the correlation for Big Five agreeableness. Results also showed that the variance in observed correlations was greater for the Big Five than for the HEXACO; this is consistent with the greater variability in questionnaires used to measure the Big Five. To assess which combinations of personality and well-being dimension were uniquely related, we performed a marginalization procedure on the meta-analytic corrected correlation matrix (see the online supplemental materials). Specifically, we reversed negative affect, neuroticism, and emotionality so that all variables were positively aligned with well-being. We then subtracted the overall mean correlation, and the row and column marginal means from the correlation matrix (for further details of the procedure see, Anglim & Grant, 2016). Large residual cross-correlations (e.g., above .10 or .15) highlight the unique profile of the personality-well-being relationship, where positive residuals indicate that the pair of variables is more related than expected, and negative residuals indicate that the pair of variables is less related than expected. Absolute residuals greater than .12 for the Big Five were reversed neuroticism with reversed negative affect (.14), and personal growth ( — .15); openness with personal growth (.22); agreeableness with positive relations (.13) and autonomy ( — .13), and conscientiousness with purpose in life (.13). For HEXACO, these were reversed emotionality with reversed negative affect (.19), positive relations ( — .18), autonomy (.22), and purpose in life ( — .14); agreeableness with autonomy ( — .13); conscientiousness with purpose in life (.18); and openness with autonomy (.12) and personal growth (.15). Table 9 presents the meta-analytic estimate of the correlations between the Big Five and SWB across various moderators (i.e., core and noncore studies, item length, and personality measurement type) and compares results with past meta-analyses. It also reports the mean and standard deviation of correlations after reversing the negative correlations (i.e., N with PA, N with SWL, and E, O, A, C with NA). The mean correlation indexes the extent to which personality is related to well-being. The standard deviation of correlations indexes the degree to which a nuanced profile of personality correlates is provided as opposed to a more homogenous set of correlations. Overall, the pattern of correlations is fairly robust across different types of measures and different item PERSONALITY AND WELL-BEING 297 Table 3 Combined Sample Sizes and Number of Studies Across Study Features Combined Core Noncore Category n k n k n k Total 334,567 462 206,364 370 128,203 92 Personality items Extra Short 1 to 3 47,941 45 47,941 45 Short 4 to 7 75,012 30 75,012 30 Standard 8 to 15 180,646 292 175,396 275 5,250 17 Long 16 or more 30,968 95 30,968 95 Measure type HEXACO 7,146 22 6,566 19 580 3 NEO 64,398 170 61,767 161 2,631 9 IPIP 44,359 43 20,120 35 24,239 8 BFAS 3,442 8 3,442 8 BFI 131,342 125 87,251 93 44,091 32 TIPI 4,847 17 4,847 17 Adjectives 45,290 28 10,580 20 34,710 8 Other 33,743 49 16,638 34 17,105 15 Year Pre-2000 7,256 30 6,604 27 652 3 2000-2004 23,903 49 22,984 47 919 2 2005-2009 30,664 51 12,282 39 18,382 12 2010-2014 106,176 146 42,598 112 63,578 34 2015-2019 166,568 186 121,896 145 44,672 41 Sample size Under 100 2,239 36 1,689 27 550 9 100-199 16,288 111 14,329 99 1,959 12 200-299 23,904 99 19,230 80 4,674 19 300-499 38,454 102 32,344 87 6,110 15 500-999 47,609 70 37,520 56 10,089 14 1000 or more 206,073 44 101,252 21 104,821 23 M age Under 18 13,722 29 10,753 23 2,969 6 18 to 29 65,597 192 49,522 155 16,075 37 30 to 59 213,033 147 127,288 122 85,745 25 60 or over 21,082 29 4,406 18 16,676 11 Note. Correlations between a trait and a well-being variable were classified as core if the personality trait was measured with eight or more items and the well-being variable was measured with five or more items. Studies were classified as core if they had one or more core correlation. BFAS = Big Five Aspect Scales; BFI = Big Five Inventory; IPIP = International Personality Item Pool; TIPI = Ten-Item Personality Inventory. lengths. Nonetheless, consistent with reduced reliability of measurement and potentially validity, noncore studies and extrashort measures had weaker correlations with well-being. In general, there was a high degree of consistency across the different personality frameworks, although the TIPI was notably less consistent. The BFAS had somewhat stronger average correlations and the TIPI had weaker average correlations. The NEO and BFAS had larger standard deviations. To quantify the consistency across frameworks, we created a data frame that had 15 rows for the 15 absolute SWB correlations and seven columns for the seven personality frameworks. We then computed the average correlation each framework had with the other six frameworks. These correlations were .88 (NEO), .88 (IPIP), .90 (BFAS), .87 (BFI), .74 (TIPI), .90 (Adjectives), and .84 (Other). Table 9 also compares meta-analytic correlations of the current study with that of previous meta-analyses. A major conclusion of Steel et al. (2008) was that personality is more strongly related to well-being than was found in the meta-analysis of DeNeve and Cooper (1998). Whereas DeNeve and Cooper (1998) synthesized a mostly pre-Big Five literature, Steel et al. (2008) focused exclu- sively on the NEO framework. The current meta-analysis found meta-analytic correlations between personality and well-being that were slightly larger than Steel et al. (2008). Importantly, the current results indicate that this finding is not limited to the NEO framework, but is shared across a broad range of personality measures that are intended to measure the Big Five. The pattern of correlations in the current meta-analysis was almost identical to that obtained in Steel et al. (2008), but quite different to that of DeNeve and Cooper (1998). To quantify this, we first treated the 15 absolute correlations between Big Five personality and SWB (i.e., SWL, PA, NA) for the three metaanalyses (i.e., current study, Steel et al., and DeNeve & Cooper) as a vector. The correlation between the 15 Big Five-SWB-absolute-correlations was r = .991 (current study with Steel), r = .689 (current study with DeNeve), and r = .679 (DeNeve with Steel). Thus, it seems that categorizing historical measures of personality into Big Five frameworks as was done by necessity in DeNeve and Cooper (1998) only provides an approximation of how Big Five personality actually correlates with well-being. 298 ANGLIM, HORWOOD, SMILLIE, MARRERO, AND WOOD Table 4 Meta-Analytic Correlations of Big Five and HEXACO Personality With SWB and PWB Measure SWL PA NA PR AU EM PG PL SA M NEO Neuroticism -.39 -.34 .56 -.43 -.45 -.58 -.34 -.45 -.60 -.46 Extraversion .32 .44 -.21 .47 .26 .38 .39 .39 .43 .37 Openness .08 .24 -.05 .20 .24 .11 .44 .21 .16 .19 Agreeableness .20 .19 -.25 .39 .10 .28 .31 .28 .28 .25 Conscientiousness .27 .35 -.25 .32 .30 .51 .32 .50 .44 .36 HEXACO Honesty-humility .11 .07 -.15 .20 .19 .20 .21 .18 .14 .16 Emotionality -.09 -.12 .31 .01 -.36 -.19 -.11 -.03 -.24 -.16 Extraversion .43 .55 -.39 .57 .39 .52 .45 .41 .61 .48 Agreeableness .17 .14 -.25 .27 .02 .22 .16 .13 .23 .18 Conscientiousness .22 .32 -.17 .18 .23 .41 .31 .47 .23 .28 Openness .10 .15 -.01 .14 .25 .10 .34 .14 .18 .16 Note. PWB = psychological well-being; SWB = subjective well-being; SWL = satisfaction with life; PA = positive affect; NA = negative affect; PR = positive relations; AU = autonomy; EM = environmental mastery; PG = personal growth; PL = purpose in life; SA = self-acceptance. Absolute correlations above .30 are bolded. M = mean correlation between the personality trait and well-being variables, where the correlation with negative affect (NA) is reversed. u cd 1) OJ Finally, a publication bias analysis was conducted. There are several reasons to expect publication biases to be minimal in this context. First, the majority of primary studies have a high degree of power to detect the main correlations between personality and well-being. For example, a study with n = 200 has 99% statistical power to detect a population correlation of .30 at a .05 significance threshold. Second, many studies measure personality and well-being incidentally as part of broader studies of individual differences and there is no obvious incentive to show a specific pattern of correlations between personality and well-being. Nonetheless, we examined funnel plots for the 99 correlation types (i.e., 11 personality traits by nine well-being variables) and calculated the rank test for funnel asymmetry (Begg & Mazumdar, 1994). After reversing neuroticism, emotionality, and negative affect, none of the correlations examined exhibited significant positive asymmetry. Weil-Being Intercorrelations To contextualize the meta-analytic and facet-level analyses, we present estimates of the intercorrelations between dimensions of well-being. Table 10 presents correlations among the nine well-being scales for the Combined and the NEO Data Sets. Reflecting a general well-being factor, the average correlation between well-being variables was .51 in the Combined Dataset. Consistent with the focus on the scale-level, when factor analysis is performed and two factors are extracted, loadings for the nine scales do not align Table 5 Detailed Meta-Analytic Results for Big Five Domains and Subjective Weil-Being Measure k n r Lower 95% CI r Upper 95% CI r P tp Lower 95% CI p Upper 95% CI p Satisfaction with life Neuroticism 224 158,934 -.39 .10 -.41 -.38 -.46 .13 -.48 -.44 Extraversion 219 158,905 .32 .08 .31 .33 .38 .11 .36 .39 Openness 194 146,668 .08 .08 .07 .10 .10 .11 .08 .12 Agreeableness 188 145,623 .20 .07 .19 .21 .24 .10 .23 .26 Conscientiousness 196 149,681 .27 .07 .26 .28 .31 .09 .30 .33 Positive affect Neuroticism 167 54,816 -.34 .11 -.36 -.32 -.39 .13 -.41 -.36 Extraversion 157 51,731 .44 .10 .42 .46 .51 .13 .49 .53 Openness 123 41,406 .24 .13 .21 .26 .28 .15 .25 .31 Agreeableness 122 40,714 .19 .13 .16 .21 .22 .16 .19 .25 Conscientiousness 128 43,497 .35 .10 .33 .37 .40 .12 .38 .43 Negative affect Neuroticism 172 55,495 .56 .11 .55 .58 .65 .13 .63 .67 Extraversion 152 49,212 -.21 .10 -.22 -.19 -.24 .12 -.26 -.22 Openness 121 39,538 -.05 .08 -.07 -.03 -.06 .10 -.08 -.04 Agreeableness 120 39,023 -.25 .11 -.28 -.23 -.30 .14 -.33 -.28 Conscientiousness 128 42,358 -.25 .11 -.27 -.22 -.29 .14 -.31 -.26 Note. Only core studies using at least eight items per personality factor and at least five items for well-being were included, k is the number of studies. f is mean observed correlation estimated from random-effects model and inverse-variance weighting, p is the equivalent correlation estimated using correlations corrected for measurement error. i> and Tp are the estimated standard deviations of true unadjusted and corrected correlations, respectively. PERSONALITY AND WELL-BEING 299 Table 6 Detailed Meta-Analytic Results for Big Five Domains and Psychological Well-Being u cd 1) OJ Measure k n r Lower 95% CI r Upper 95% CI r P tp Lower 95% CI p Upper 95% CI p Positive relation with others Neuroticism 18 6,440 -.43 .11 -.49 -.37 -.51 .14 -.57 -.44 Extraversion 19 6,840 .47 .12 .41 .53 .56 .15 .49 .63 Openness 17 6,233 .20 .09 .15 .25 .24 .12 .17 .30 Agreeableness 17 6,233 .39 .09 .34 .44 .47 .12 .41 .53 Conscientiousness 18 6,440 .32 .12 .26 .38 .38 .16 .30 .46 Autonomy Neuroticism 17 6,309 -.45 .08 -.50 -.41 -.54 .11 -.60 -.49 Extraversion 17 6,309 .26 .10 .20 .32 .31 .13 .25 .38 Openness 16 6,102 .24 .09 .18 .29 .29 .13 .23 .36 Agreeableness 16 6,102 .10 .11 .04 .16 .13 .14 .05 .20 Conscientiousness 17 6,309 .30 .05 .27 .34 .36 .07 .32 .41 Environmental mastery Neuroticism 16 6,160 -.58 .11 -.64 -.52 -.69 .13 -.76 -.63 Extraversion 16 6,160 .38 .14 .31 .45 .45 .16 .37 .53 Openness 15 5,953 .11 .11 .04 .17 .13 .15 .04 .21 Agreeableness 15 5,953 .28 .10 .22 .34 .35 .13 .27 .42 Conscientiousness 16 6,160 .51 .10 .45 .56 .61 .11 .55 .67 Personal growth Neuroticism 16 5,920 -.34 .11 -.40 -.28 -.41 .15 -.49 -.33 Extraversion 16 5,920 .39 .09 .34 .44 .47 .12 .41 .54 Openness 15 5,713 .44 .10 .39 .50 .55 .12 .48 .61 Agreeableness 15 5,713 .31 .10 .25 .36 .38 .12 .31 .45 Conscientiousness 16 5,920 .32 .06 .28 .36 .40 .08 .35 .44 Purpose in life Neuroticism 15 5,699 -.45 .12 -.51 -.38 -.53 .14 -.61 -.46 Extraversion 15 5,699 .39 .10 .33 .45 .47 .13 .40 .54 Openness 14 5,492 .21 .09 .15 .26 .25 .13 .18 .33 Agreeableness 14 5,492 .28 .06 .24 .32 .35 .09 .29 .40 Conscientiousness 15 5,699 .50 .10 .44 .55 .60 .10 .54 .66 Self-acceptance Neuroticism 14 5,488 -.60 .13 -.67 -.53 -.69 .15 -.77 -.61 Extraversion 14 5,488 .43 .11 .37 .49 .50 .13 .43 .57 Openness 13 5,281 .16 .10 .10 .23 .19 .13 .11 .27 Agreeableness 13 5,281 .28 .06 .24 .32 .35 .09 .29 .41 Conscientiousness 14 5,488 .44 .05 .40 .47 .51 .08 .46 .56 Note, k is the number of studies, f is mean observed correlation estimated from random-effects model and inverse-variance weighting, p is the equivalent correlation estimated using correlations corrected for measurement error. i> and Tp are the estimated standard deviations of true unadjusted and corrected correlations, respectively. with higher-order PWB and SWB dimensions. Life satisfaction shared the greatest overlap with self-acceptance, although correlations were relatively large for most other well-being scales, with the exception of autonomy and personal growth. Facet-Level Correlations We first examined the degree to which the domain correlations between personality and well-being in the facet-level data sets were consistent with the core meta-analytic estimates. In general, there was very strong convergence with the pattern of domain correlations for all the facet-level data sets: NEO (r = .94), IPIP (r = .95), HEXACO (r = .96), Big Five Aspects (r = .89) data sets (see the online supplemental materials for details). Average correlations between personality and well-being were higher (mean difference study and meta-analytic correlations in parentheses) than meta-analytic estimates for the IPIP (M = .06) and Big Five Aspects (M = .12), but similar for HEXACO (M = .03) and NEO (M = -.03). Zero-order correlations between personality facets and well-being are presented for NEO (see Table 11), IPIP NEO (see Table 12), and HEXACO (see Table 13). Domain-level correlations for the NEO and IPIP NEO data sets are reported in the online supplemental materials. Semipartial correlations that involved removing overlap between each facet and the corresponding domain scores are also reported in the online supplemental materials. For the NEO, the strongest average correlations with well-being are seen for depression ( — .46), vulnerability ( — .44), and competence (.41). For the IPIP NEO, semipartial correlations frequently highlighted depression as an incremental predictor over and above the Big Five. Positive emotions was also a prominent incremental predictor in relation to satisfaction with life, positive affect, and self-acceptance. Various other semipartial correlations emerged consistent with the unique profile of the well-being variable (e.g., purpose in life with achievement striving and autonomy with angry hostility [ + ], self-consciousness [ —], and assertiveness [+]). For the HEXACO, social self-esteem and liveliness emerged as the 300 ANGLIM, HORWOOD, SMILLIE, MARRERO, AND WOOD Table 7 Detailed Meta-Analytic Results for HEXACO Domains and Subjective Well-Being Measure k n r Lower 95% CI r Upper 95% CI r P tp Lower 95% CI p Upper 95% CI p Satisfaction with life Honesty-humility 14 4,049 .11 .00 .08 .14 .13 .00 .10 .16 Emotionality 14 4,049 -.09 .07 -.14 -.04 -.11 .09 -.16 -.05 Extraversion 14 4,049 .43 .07 .39 .48 .51 .09 .46 .56 Agreeableness 14 4,049 .17 .06 .13 .22 .21 .08 .15 .26 Conscientiousness 14 4,049 .22 .00 .19 .25 .27 .02 .24 .30 Openness 14 4,049 .10 .12 .03 .17 .11 .14 .03 .19 Positive affect Honesty-humility 8 3,834 .07 .05 .02 .13 .09 .06 .03 .14 Emotionality 8 3,834 -.12 .05 -.17 -.06 -.15 .09 -.22 -.08 Extraversion 8 3,834 .55 .04 .51 .58 .63 .05 .59 .67 Agreeableness 8 3,834 .14 .09 .07 .21 .17 .10 .09 .25 Conscientiousness 8 3,834 .32 .10 .25 .40 .38 .12 .29 .47 Openness 8 3,834 .15 .04 .10 .20 .17 .05 .13 .22 Negative affect Honesty-humility 9 4,134 -.15 .05 -.20 -.11 -.18 .06 -.23 -.13 Emotionality 9 4,134 .31 .09 .24 .37 .36 .11 .28 .44 Extraversion 9 4,134 -.39 .11 -.47 -.32 -.46 .13 -.55 -.37 Agreeableness 9 4,134 -.25 .07 -.31 -.19 -.30 .09 -.36 -.23 Conscientiousness 9 4,134 -.17 .09 -.24 -.10 -.20 .11 -.28 -.12 Openness 9 4,134 -.01 .02 -.04 .03 -.01 .04 -.05 .03 Note, k is the number of studies, f is mean observed correlation estimated from random-effects model and inverse-variance weighting, p is the equivalent correlation estimated using correlations corrected for measurement error. t> and tp are the estimated standard deviations of true unadjusted and corrected correlations, respectively. strongest average predictors of well-being. Differential correlations of emotionality facets highlight why emotionality correlated much less with well-being overall. Specifically, anxiety and to a lesser extent fearfulness had strong negative correlations with well-being whereas dependence and sentimentality did not. Similarly, with regards t conscientiousness, it was mostly diligence that had the stand-out correlations. Incremental Prediction of Facets Over Domains To examine the variance explained by broad and narrow traits across the four data sets, regression models were estimated predicting each well-being variable from either the broad or the narrow traits for the given personality measure. The variance explained by broad and narrow traits (adjusted r-squared) for each measure is shown in Table 14. Two measures of incremental prediction of narrow traits are also provided: raw incremental prediction by narrow over broad traits and proportional increase of narrow traits relative to broad traits. On average, broad traits explained 46% of variance and narrow traits explained 53% for an average proportional increase of facets over domains of 18% (21% if you exclude the Big Five Aspects data). Despite differences in the overall magnitude of prediction (i.e., Big Five Aspects and IPIP NEO explained more than HEXACO and NEO), the general pattern of well-being predicted by domains and facets/aspects was similar across NEO, IPIP NEO, and HEXACO, but distinct for the Big Five Aspects. On average, PWB variables were better predicted by personality than SWB variables. IPIP NEO and HEXACO had larger incremental prediction than the NEO and Big Five Aspects, although the difference for the NEO was reduced when incremental prediction was defined as a proportion, due to the relatively lower levels of prediction in the NEO sample. Overall, the greatest proportional increase in variance explained by facets was seen for life satisfaction, autonomy, self-acceptance, and purpose in life. HEXACO Versus Big Five Comparison To contextualize the meta-analytic finding and frame a comparison of HEXACO and Big Five, Table 15 presents the correlations between HEXACO and Big Five domains using the Combined Dataset. All analogous scales between HEXACO and Big Five correlated greater than .50. Interestingly—though unsurprisingly, given the rotational differences between the two models— honesty-humility correlated more with Big Five agreeableness than did HEXACO agreeableness. Of relevance to understanding correlations with well-being, HEXACO extraversion correlated more with neuroticism than did HEXACO emotionality. Table 16 presents the domain-level correlations for HEXACO and IPIP NEO Domains with well-being dimensions in the Combined Dataset. The pattern of correlations is broadly similar to the meta-analytic findings, albeit the correlations are slightly stronger on average. This may reflect the use of particularly reliable personality and well-being measures in this study. We also computed the HEXACO Neuroticism domain score using the weighted facet-composite described in the Method section. This yielded a pattern of correlations that was very similar to IPIP NEO Neuroticism. To compare the HEXACO and Big Five models of personality in terms of the prediction of well-being dimensions, regression models were estimated (using the Combined Dataset) predicting each well-being variable from various sets of personality predictors: that is, HEXACO Domains, NEO Domains, HEXACO Facets, NEO Facets, and the different combinations of Domains and Facets from both instruments. The variance in well-being ex- PERSONALITY AND WELL-BEING 301 Table 8 Detailed Meta-Analytic Results for HEXACO Domains and Psychological Well-Being Measure Lower 95% CI r Upper 95% CI r Lower 95% CI p Upper 95% CI p Positive relation with others Honesty-humility 5 2,033 .20 .00 .16 Emotionality 5 2,033 .01 .09 -.08 Extraversion 5 2,033 .57 .04 .52 Agreeableness 5 2,033 .27 .04 .21 Conscientiousness 5 2,033 .18 .00 .14 Openness 5 2,033 .14 .00 .10 Autonomy Honesty-humility 5 2,033 .19 .05 .13 Emotionality 5 2,033 -.36 .00 -.40 Extraversion 5 2,033 .39 .00 .36 Agreeableness 5 2,033 .02 .07 -.05 Conscientiousness 5 2,033 .23 .05 .17 Openness 5 2,033 .25 .05 .19 Environmental mastery Honesty-humility 5 2,033 .20 .02 .15 Emotionality 5 2,033 -.19 .09 -.28 Extraversion 5 2,033 .52 .08 .44 Agreeableness 5 2,033 .22 .07 .14 Conscientiousness 5 2,033 .41 .07 .34 Openness 5 2,033 .10 .08 .01 Personal growth Honesty-humility 5 2,033 .21 .07 .13 Emotionality 5 2,033 -.11 .00 -.15 Extraversion 5 2,033 .45 .04 .40 Agreeableness 5 2,033 .16 .04 .10 Conscientiousness 5 2,033 .31 .02 .26 Openness 5 2,033 .34 .05 .28 Purpose in life Honesty-humility 5 2,033 .18 .00 .13 Emotionality 5 2,033 -.03 .04 -.09 Extraversion 5 2,033 .41 .08 .33 Agreeableness 5 2,033 .13 .07 .05 Conscientiousness 5 2,033 .47 .00 .43 Openness 5 2,033 .14 .00 .10 Self-acceptance Honesty-humility 5 2,033 .14 .02 .10 Emotionality 5 2,033 -.24 .00 -.29 Extraversion 5 2,033 .61 .03 .57 Agreeableness 5 2,033 .23 .06 .17 Conscientiousness 5 2,033 .23 .07 .15 Openness 5 2,033 .18 .10 .08 .24 .09 .61 .32 .22 .19 .25 .32 .43 .10 .29 .32 .25 .10 .61 .30 .49 .19 .29 .06 .50 .21 .35 .41 .22 .03 .49 .21 .50 .19 .19 .20 .64 .30 .30 .27 .24 .00 .68 .33 .22 .18 .24 .45 .49 .03 .29 .32 .26 .23 .64 .27 .51 .12 .27 .14 .56 .20 .40 .43 .24 .03 .52 .17 .60 .19 .18 .31 .74 .29 .27 .22 .00 .12 .00 .06 .02 .05 .06 .00 .02 .09 .06 .07 .06 .10 .11 .11 .10 .05 .00 .05 .05 .06 .05 .06 .09 .04 .02 .03 .06 .03 .07 .09 .14 20 11 66 26 17 12 17 48 45 06 22 24 19 33 56 18 41 01 17 20 53 14 35 35 17 10 46 08 55 15 12 37 71 21 18 .28 .12 .70 .40 .27 .25 .31 .41 .53 .12 .36 .39 .32 .13 .72 .37 .61 .23 .37 .07 .59 .26 .46 .52 .31 .04 .59 .27 .64 .24 .23 .24 .78 .37 .36 .35 Note, k is the number of studies, f is mean observed correlation estimated from random-effects model and inverse-variance weighting, p is the equivalent correlation estimated using correlations corrected for measurement error, f and Tp are the estimated standard deviations of true unadjusted and corrected correlations, respectively. plained by each set of predictors, using adjusted r-squared to penalize for overfitting, is shown in Table 17. On average, NEO Domains explained more variance than HEXACO Domains and NEO facets explained more variance than HEXACO facets. HEXACO facets explained about 22% more variance (mean increase of adjusted r-squared of .09) than HEXACO domains, and NEO Facets explained about 18% more variance than NEO domains (mean increase of adjusted r-squared of .12). Satisfaction with life showed the largest relative increase in prediction when moving from domains to facets: 52% for HEXACO and 41% for NEO, although in terms of absolute increase, self-acceptance showed similar increases. Whereas the HEXACO facets improved prediction when added to a model with NEO Domains, adding HEXACO Domains or HEXACO Facets to a model with NEO Facets led to almost no improvement in prediction. Discussion The present study provides a comprehensive examination of the links between self-reported personality and well-being, using both the HEXACO and Big Five frameworks of personality, broad and narrow traits within each of these frameworks, and both evaluative (i.e., SWB) and eudaimonic (i.e., PWB) conceptualizations of well-being. Whereas previous meta-analyses have either relied on pre-Big-Five measures or a single Big Five personality framework, the current study incorporated a broad range of Big Five measures and synthesized the large body of research that has emerged in recent years. Whereas previous meta-analyses have examined the relationship between the Big Five and SWB, none have examined the Big Five in relation to PWB, and none have examined the HEXACO framework at all. 302 ANGLIM, HORWOOD, SMILLIE, MARRERO, AND WOOD Table 9 Meta-Analytic Correlations Between Big Five Personality and Subjective Well-Being by Study Type, Number of Personality Items, Personality Measure Type, and Comparison With Past Meta-Analyses SWL PA NA Personality items N E O A C N E O A C N E O A C M SD Study status Core studies -.39 .32 .08 .20 .27 -.34 .44 .24 .19 .35 .56 -.21 -.05 -.25 -.25 .28 .13 Noncore studies -.32 .24 .09 .18 .21 -.36 .40 .27 .24 .26 .53 -.20 -.08 -.14 -.24 .25 .12 Personality items Extra Short 1 to 3 -.31 .22 .08 .15 .20 -.34 .33 .20 .12 .23 .46 -.20 -.05 -.13 -.21 .22 .11 Short 4 to 7 -.32 .27 .14 .19 .23 -.32 .45 .36 .33 .28 .55 -.18 -.10 -.12 -.23 .27 .12 Standard 8 to 15 -.38 .31 .09 .21 .26 -.34 .43 .25 .22 .36 .57 -.20 -.07 -.27 -.26 .28 .13 Long 16 or more -.42 .33 .06 .18 .29 -.35 .46 .19 .11 .31 .57 -.22 -.01 -.20 -.22 .26 .15 Measure type NEO -.42 .34 .05 .17 .28 -.32 .44 .18 .10 .36 .56 -.20 -.02 -.20 -.21 .26 .15 IPIP -.38 .28 .09 .19 .25 -.36 .38 .20 .23 .33 .54 -.21 -.05 -.23 -.28 .27 .12 BFAS -.43 .37 .06 .14 .31 -.41 .57 .27 .24 .42 .65 -.34 -.12 -.24 -.27 .32 .16 BFI -.34 .27 .09 .20 .23 -.37 .43 .28 .24 .34 .57 -.20 -.06 -.31 -.29 .28 .13 TIPI -.31 .22 .10 .14 .19 -.32 .38 .27 .09 .19 .39 -.26 -.16 -.01 -.22 .22 .11 Adjectives -.35 .26 .06 .21 .23 -.29 .46 .33 .23 .33 .57 -.22 -.10 -.19 -.24 .27 .13 Other -.34 .31 .17 .25 .25 -.34 .46 .31 .26 .27 .58 -.17 -.09 -.15 -.12 .27 .13 Meta-analyses Current (core) -.39 .32 .08 .20 .27 -.34 .44 .24 .19 .35 .56 -.21 -.05 -.25 -.25 .28 .13 DeNeve and Cooper (1998) -.24 .17 .14 .16 .22 -.14 .20 .14 .17 .14 .23 -.07 .05 -.13 -.10 .15 .07 Steel et al. (2008) -.38 .28 .03 .14 .22 -.30 .44 .20 .12 .27 .54 -.18 -.02 -.20 -.20 .23 .14 Heller (2004) -.48 .28 .08 .29 .31 Note. Current (core) k = 120 to 224, n = 39,023 to 158,934; Heller, Watson, and Hies (2004) k= \9,n= 12,092; Steel et al. (2008) k = 22 to 57, n = 6,040 to 16,764; DeNeve and Cooper (1998) k = 38 to 102, n is a subset of 42,171. M and SD is the mean and standard deviation of correlation after reversing N with PA, N with SWL, and E, O, A, C with NA. NA = negative affect; PA = positive affect; SWL = satisfaction with life; BFAS = Big Five Aspect Scales; BFI = Big Five Inventory; IPIP = International Personality Item Pool; TIPI = Ten-Item Personality Inventory. u cd d c I % 1) OJ The study also provides the first robust assessment of incremental prediction by facets across both SWB and PWB and two major personality frameworks. Several important findings emerged from this investigation. First, the research confirms that the overlap between basic personality traits and well-being dimensions is substantial. Second, whereas (lower) neuroticism is the strongest correlate of well-being within the Big Five framework, extraversion is the strongest correlate within the HEXACO framework. Conversely, conscientiousness—which previous research has rarely highlighted in relation to well-being—is a notable correlate within both frame- works. Third, correlations with personality mirror the unique characteristics of different dimensions of well-being. For example, notably strong correlations were observed between openness and personal growth, between conscientiousness and purpose in life, and between neuroticism and negative affect. Fourth, examination of facet-level correlates highlighted the unique importance of particular facets (e.g., depression and positive emotions in the Big Five framework and social self-esteem in the HEXACO framework) as well as explaining differences between the HEXACO and Big Five frameworks. Fifth, facets provided moderate levels of incremental prediction over and above domains when predicting Table 10 Correlation Among Weil-Being Scales for Combined Dataset (Lower Diagonal) and NEO Dataset (Upper Diagonal) Variable 1 2 3 4 5 6 7 8 9 SWB 1. Life satisfaction .36 -.29 .41 .25 .51 .27 .52 .65 2. Positive affect .52 -.09 .31 .23 .40 .32 .37 .36 3. Negative affect -.44 -.39 -.32 -.29 -.43 -.21 -.33 -.40 PWB 4. Positive relations .49 .53 -.41 .45 .57 .53 .58 .63 5. Autonomy .16 .26 -.42 .25 .55 .46 .48 .56 6. Environmental mastery .58 .60 -.59 .61 .42 .47 .72 .74 7. Personal growth .36 .51 -.38 .53 .44 .58 .53 .49 8. Purpose in life .55 .60 -.49 .53 .38 .76 .69 .73 9. Self-acceptance .74 .63 -.58 .60 .44 .77 .60 .77 Note. N = 903 for Combined Dataset; subjective well-being. N = 1,673 for NEO Dataset; PWB = psychological well-being; SWB ■ PERSONALITY AND WELL-BEING 303 Table 11 Correlations ofNEO Facets With Well-Being Measures in NEO Dataset Variable SWL PA NA PR AU EM PG PL SA M Nl. Anxiety -.28 -.16 .31 -.21 -.28 -.34 -.06 -.15 -.38 -.23 N2. Angry hostility -.23 -.14 .35 -.39 -.28 -.39 -.20 -.29 -.39 -.29 N3. Depression -.48 -.32 .41 -.46 -.41 -.57 -.27 -.49 -.66 -.46 N4. Self-consciousness -.31 -.27 .26 -.40 -.41 -.43 -.22 -.34 -.50 -.36 N5. Impulsiveness -.15 -.07 .19 -.05 -.14 -.23 .04 -.15 -.21 -.12 N6. Vulnerability -.39 -.35 .36 -.36 -.44 -.60 -.28 -.48 -.59 -.44 El. Warmth .22 .27 -.13 .59 .24 .32 .35 .31 .32 .33 E2. Gregariousness .19 .17 -.07 .40 .04 .14 .24 .18 .18 .19 E3. Assertiveness .23 .28 -.04 .31 .23 .28 .22 .23 .32 .26 E4. Activity .18 .29 .02 .22 .19 .25 .23 .30 .25 .24 E5. Excitement seeking .00 .12 .05 .07 -.05 -.06 .25 -.07 -.03 .03 E6. Positive emotions .34 .31 -.14 .49 .22 .36 .42 .34 .40 .36 Ol. Fantasy -.02 .07 .06 .09 .03 -.05 .30 .01 .00 .05 02. Aesthetics .00 .10 .06 .10 .02 -.02 .30 .01 -.03 .06 03. Feelings .07 .17 .04 .25 .14 .13 .41 .18 .12 .18 04. Actions .08 .13 -.03 .19 .12 .07 .43 .08 .12 .15 05. Ideas .01 .19 -.01 .09 .14 .08 .37 .09 .07 .13 O6. Values .02 .06 -.11 .25 .23 .12 .40 .16 .13 .17 Al. Trust .22 .16 -.15 .41 .12 .25 .17 .24 .27 .23 A2. Straightforwardness .02 -.05 -.15 .11 .13 .08 .05 .11 .07 .07 A3. Altruism .18 .14 -.16 .43 .22 .28 .24 .30 .26 .26 A4. Compliance .05 -.04 -.15 .11 -.06 .07 -.03 .04 .08 .03 A5. Modesty -.09 -.13 -.04 .05 .03 -.06 .02 .00 -.09 -.03 A6. Tender-mindedness .07 .05 -.11 .27 .22 .17 .27 .23 .18 .18 CI. Competence .37 .33 -.24 .35 .35 .55 .28 .54 .51 .41 C2. Order .15 .14 -.04 .06 .11 .30 .09 .30 .17 .17 C3. Dutifulness .17 .16 -.15 .17 .31 .41 .17 .39 .28 .26 C4. Achievement striving .24 .33 -.02 .18 .24 .39 .23 .46 .31 .30 C5. Self-discipline .28 .29 -.19 .26 .34 .55 .19 .52 .43 .36 C6. Deliberation .15 .11 -.14 .04 .09 .24 -.04 .26 .18 .13 U cd d c 1) OJ Note. N = 1,673; SWL = satisfaction with life; PA = positive affect; NA = negative affect; PR = positive relations; AU = autonomy; EM = environmental mastery; PG = personal growth; PL = purpose in life; SA = self-acceptance; Correlations .30 or above are in bold. Correlations equal to or larger than .05, .07, and .09 are significant at .05, .01, and .001, respectively. well-being. Across multiple measures of the Big Five and HEXACO frameworks there were moderate levels of consistency in the degree of incremental prediction by facets. These findings have fundamental implications for understanding well-being, in terms of the role that both broad and narrow personality traits may play in human flourishing. Personality and Weil-Being According to effect size guidelines in individual differences research (e.g., Gignac & Szodorai, 2016), the relationship between personality and well-being is strong. The average correlation between personality domains and well-being was r = .28, considerably higher than the average correlation in individual differences research as a whole (i.e., r ~ .20). The strongest average correlations with well-being were —.46 for Big Five neuroticism and .48 for HEXACO extraversion. Regression models indicated that about half the observed variance in well-being scales can be explained by personality domains (46%) and facets (53%). The domain-level correlations between Big Five personality and SWB were very similar to those reported in the meta-analysis by Steel et al. (2008) and larger and more nuanced than those reported in the meta-analysis by DeNeve and Cooper (1998). There are several reasons for this. First, DeNeve and Cooper (1998) included many studies that predated the Big Five and also used a mixture of different well-being measures. In contrast, Steel et al. (2008) focused on a small number of high-quality personality questionnaires such as the NEO and a limited set of reliable measures of SWB. Similar to Steel et al. (2008), we focused the core metaanalysis on a limited set of reliable personality and well-being measures. Our research extends that of Steel et al. (2008) by showing that the magnitude and pattern of correlations observed in Steel et al. (2008) is not limited to the NEO. A broadly similar magnitude and pattern of well-being correlations was found across a diverse range of Big Five measures. Second, the HEXACO and the Big Five frameworks have a strong focus on affect, well-being, and psychological functioning. In general, it seems likely that measures based on the Big Five and related lexical approaches, such as the HEXACO, will generally exhibit strong correlations with well-being. Broad and Narrow Personality Traits of the Big Five and HEXACO Overall, both the HEXACO and Big Five models are similarly effective in predicting well-being. For the Big Five model, neuroticism is a very strong predictor, extraversion and conscientiousness are fairly strong, and openness and agreeableness are more moderate. For the HEXACO model, extraversion is a very strong predictor (even stronger than Big Five neuroticism), conscientious- 304 ANGLIM, HORWOOD, SMILLIE, MARRERO, AND WOOD Table 12 Correlations Between IPIP NEO Facets and Well-Being Measures in Combined Dataset u cd d c I % U OJ Variable SWL PA NA PR AU EM PG PL SA M Nl. Anxiety -.38 -.38 .59 -.33 -.43 -.56 -.31 -.36 -.53 -.43 N2. Angry hostility -.32 -.35 .54 -.32 -.29 -.45 -.30 -.33 -.43 -.37 N3. Depression -.65 -.58 .70 -.59 -.45 -.76 -.50 -.69 -.83 -.64 N4. Self-consciousness -.36 -.43 .49 -.45 -.56 -.56 -.42 -.44 -.55 -.47 N5. Impulsiveness -.20 -.22 .36 -.13 -.34 -.36 -.14 -.27 -.31 -.26 N6. Vulnerability -.41 -.43 .62 -.36 -.53 -.65 -.42 -.49 -.57 -.50 El. Warmth .42 .50 -.40 .69 .25 .52 .44 .47 .53 .47 E2. Gregariousness .30 .36 -.24 .46 .07 .33 .25 .24 .33 .29 E3. Assertiveness .34 .44 -.30 .42 .42 .47 .44 .46 .47 .42 E4. Activity .28 .41 -.22 .29 .25 .49 .38 .51 .38 .36 E5. Excitement seeking .14 .23 -.03 .17 .03 .09 .20 .04 .12 .12 E6. Positive emotions .50 .53 -.37 .59 .23 .48 .49 .47 .55 .47 Ol. Fantasy .00 .11 .08 .09 .06 -.06 .21 .03 .01 .04 02. Aesthetics .08 .24 -.06 .23 .15 .11 .42 .22 .16 .19 03. Feelings .01 .09 .19 .19 .02 -.04 .35 .20 .05 .08 04. Actions .20 .30 -.26 .27 .29 .29 .54 .32 .32 .31 05. Ideas .12 .28 -.17 .20 .41 .29 .48 .35 .26 .28 O6. Values -.04 -.04 .02 .01 .06 -.08 .17 -.04 -.01 .00 Al. Trust .35 .32 -.37 .54 .10 .40 .34 .37 .42 .36 A2. Straightforwardness .08 .09 -.25 .22 .15 .22 .21 .27 .17 .18 A3. Altruism .26 .36 -.25 .52 .15 .34 .47 .43 .34 .35 A4. Compliance .13 .11 -.21 .19 -.04 .12 .17 .17 .15 .13 A5. Modesty -.30 -.26 .16 -.22 -.18 -.27 -.17 -.26 -.39 -.25 A6. Tender-mindedness .10 .15 -.07 .31 .07 .07 .33 .22 .14 .16 CI. Competence .41 .47 -.48 .42 .52 .66 .56 .68 .60 .53 C2. Order .10 .15 -.14 .02 .12 .25 .10 .28 .13 .14 C3. Dutifulness .21 .23 -.34 .27 .30 .40 .34 .43 .32 .32 C4. Achievement striving .34 .45 -.27 .29 .34 .54 .49 .67 .45 .43 C5. Self-discipline .34 .42 -.37 .26 .33 .61 .33 .58 .45 .41 C6. Deliberation .09 .06 -.26 .09 .21 .26 .11 .30 .17 .17 Note. N = 903; SWL = satisfaction with life; PA = positive affect; NA = negative affect; PR = positive relations; AU = autonomy; EM = environmental mastery; PG = personal growth; PL = purpose in life; SA = self-acceptance. Correlations .30 or above are in bold. Correlations equal to or larger than .07, .09, and .11 are significant at .05, .01, and .001, respectively. ness is fairly strong, and honesty-humility, emotionality, agreeableness, and openness are more modest. Differences in well-being correlations between the Big Five and HEXACO may largely result from how these models partition personality trait variance (for a review, see Ashton & Lee, 2019; Ashton et al., 2014). These differences can be readily appreciated by examining (a) the correlations between the HEXACO and the Big Five (see Table 15 in the current paper and Table 1 in Gaughan et al, 2012), (b) the item content of relevant HEXACO and Big Five scales, and (c) the correlations between personality and well-being at the facet-level for HEXACO and the Big Five. For instance, HEXACO extraversion (a) correlates at —.65 with IPIP NEO neuroticism, (b) has many (reversed) items that relate to low self-esteem and depression (e.g., "I sometimes feel that I am a worthless person"), and (c) shows correlations with well-being most prominently for the facets of social self-esteem and liveliness. In contrast, HEXACO emotionality (a) correlated only .56 with IPIP NEO neuroticism, and (b) combines traditional neuroticism facet scales such as fearfulness and anxiety (which correlate negatively with well-being) with more neutral emotional tendencies such as dependence (which is relatively uncorrelated with well-being) and prosocial tendencies such as sentimentality (which correlate positively with some aspects of well-being). HEXACO honesty-humility and HEXACO agreeableness both correlate most strongly with Big Five agreeableness, although HEXACO honesty-humility has a secondary correlation with Big Five conscientiousness, whereas HEXACO agreeableness has a secondary correlation with neuroticism, reflecting its content related to lower anger and hostility. Although organized differently across the Big Five and HEXACO frameworks, the tendency to experience low levels of negative emotions and high levels of positive emotions accounts for much of the effect of personality on well-being. In the Big Five model, neuroticism captures the broad set of tendencies to experience negative emotions, whereas facets related to positive emotions form only part of extraversion. Facets such as depression, positive emotions, and social self-esteem are particularly strong predictors of well-being. It is not surprising that these characteristic ways of experiencing the world—viewing life through a more negative lens, ruminating on negative experiences, and emphasizing what's wrong rather than what's right with the world—translate into lower levels of well-being. On the other hand, Big Five extraversion may operate both through the tendency to experience positive emotion as well as the more instrumental pathways paved by the behavioral components of extraversion, such as facilitating positive social connections and actively engaging with environmental rewards (Smillie, Cooper, Wilt, & Revelle, 2012; Smillie, Wilt, Kabbani, Garratt, & Revelle, 2015; Sun, Stevenson, Kabbani, Richardson, & Smillie, 2017). PERSONALITY AND WELL-BEING 305 Table 13 Correlations Between HEXACO Facets and Well-Being Measures in HEXACO Dataset Variable SWL PA NA PR AU EM PG PL SA M HI: Sincerity .14 .10 -.25 .21 .27 .24 .23 .19 .21 .20 H2: Fairness .19 .21 -.22 .25 .16 .21 .18 .25 .23 .21 H3: Greed-avoidance .08 .04 -.14 .11 .23 .03 .15 .07 .10 .11 H4: Modesty -.05 .00 -.09 .11 .03 .01 .10 .01 -.06 .03 El: Fearfulness -.04 -.16 .19 -.15 -.37 -.27 -.22 -.14 -.17 -.19 E2: Anxiety -.26 -.22 .47 -.23 -.35 -.43 -.23 -.26 -.40 -.32 E3: Dependence .09 .05 .25 .17 -.30 -.19 .01 -.08 -.05 -.06 E4: Sentimentality .13 .17 .11 .25 -.14 .04 .22 .18 .07 .09 XI: Social self-esteem .57 .56 -.55 .62 .37 .70 .50 .62 .75 .58 X2: Social boldness .27 .35 -.27 .39 .44 .38 .40 .38 .40 .36 X3: Sociability .27 .33 -.20 .51 .09 .32 .30 .24 .31 .29 X4: Liveliness .52 .59 -.46 .60 .29 .66 .50 .58 .64 .54 Al: Forgiveness .21 .21 -.18 .29 .09 .21 .19 .15 .23 .20 A2: Gentleness .17 .17 -.15 .18 .06 .10 .13 .07 .13 .13 A3: Flexibility .14 .14 -.19 .23 -.02 .16 .14 .10 .17 .14 A4: Patience .22 .27 -.34 .20 .16 .27 .20 .19 .27 .24 CI: Organization .11 .19 -.12 .07 .16 .33 .14 .31 .18 .18 C2: Diligence .26 .44 -.29 .24 .36 .52 .44 .62 .41 .40 C3: Perfectionism -.02 .13 -.03 .02 .16 .15 .20 .27 .10 .12 C4: Prudence .17 .24 -.35 .15 .27 .34 .17 .33 .27 .25 Ol: Aesthetic appreciation .09 .20 -.06 .12 .22 .11 .33 .16 .13 .16 02: Inquisitiveness .06 .21 -.16 .10 .29 .21 .30 .16 .16 .18 03: Creativity .05 .23 -.06 .08 .25 .08 .28 .13 .17 .15 04: Unconventionality .00 .14 .05 .02 .22 -.04 .25 .05 .07 .07 I: Altruism .14 .21 -.06 .28 .00 .12 .32 .25 .18 .17 Note. N = 465; SWL = satisfaction with life; PA = positive affect; NA = negative affect; PR = positive relations; AU = autonomy; EM = environmental mastery; PG = personal growth; PL = purpose in life; SA = self-acceptance. Correlations .30 or above are in bold. u cd d c I % Whereas most previous research has emphasized only neuroti-cism/emotionality and extraversion in relation to well-being (e.g., Diener et al, 1999; Schimmack et al., 2004; Smillie, Kern, & Uljarevic, 2018), the present research reveals that conscientiousness is not far behind, and is perhaps even on par with extraversion. For instance, the average correlation for Big Five extraversion was .37 versus .36 for Big Five conscientiousness (.28 for HEXACO conscientiousness). Conscientiousness emerged as particularly important for purpose in life and environmental mastery, although was somewhat less related to negative affect and positive relations. Several processes described by conscientiousness could account for its positive implications for well-being. First, conscientiousness is related to a sense of competence in life, and the competence facet of conscientiousness was a particularly strong predictor of well-being. Second, conscientiousness describes effective self-regulation, as when one forgoes short-term pleasures for the attainment of longer-term goals, whether they be related to family, education, finance, or health (Roberts, Lejuez, Krueger, Richards, & Hill, 2014). Third, achievement striving and diligence can connect people with a sense of purpose and meaning, that can facilitate a deeper sense of life satisfaction. However, as a small counterpoint, we note that a desire for order and perfection generally showed much weaker correlations with well-being. Consistent with highlighting the shortcomings of one's achievements relative to demanding expectations, perfectionism showed small negative semipartial correlations with some well-being dimensions after controlling for personality domains (for further discussion of the benefits and costs of perfectionism, see Stoeber & Otto, 2006; Stoeber & Stoeber, 2009). Both the Big Five and HEXACO conceptions of agreeableness, as well as HEXACO honesty-humility, had relatively modest correlations with well-being. Each of these prosocial traits may plausibly improve well-being by reducing interpersonal conflict and helping to foster positive relations with others. Status seeking, manipulativeness, and greed (captured by honesty-humility and some facets of Big Five agreeableness) may also create instability of social networks, with negative consequences for well-being. Although self-interest may bring short-term benefits, excessive self-interest may, in the long term, damage one's reputation, social relationships, and sense of meaning in life. Furthermore, placing substantial value on status symbols and power places more weight on zero-sum aspects of life (Headey & Wearing, 1992). As a counterpoint, we note that the modesty facet in both the Big Five and HEXACO models tended to be unrelated or negatively related to well-being. This may suggest that an inability or unwillingness to compare oneself favorably to others—whether this be in terms of income, wealth, health, physical attractiveness, or even popularity on social media—may have negative implications for well-being. Indeed, it is well-established that most people perceive their lives to be "better than average" (Alicke, Klotz, Breitenbecher, Yurak, & Vredenburg, 1995; Headey & Wearing, 1992), and that this rationalization may promote well-being. Finally, openness to experience was also a modest but nevertheless meaningful predictor of well-being, with correlations approximating the average effect size in individual differences research. Openness comprises such characteristics as intellectual curiosity, an ability to adapt to change, and the tendency to seek novel experiences (Schmutte & Ryff, 1997). Consistent with this, the current study revealed that openness was particularly related to personal growth, autonomy, and positive emotions. Whereas Stephan (2009) found openness to feelings and ideas to be the most important facets in relation to life satisfaction, our current findings 306 ANGLIM, HORWOOD, SMILLIE, MARRERO, AND WOOD Table 14 Variance Explained by Broad and Narrow Traits Across Measures Measure SWL PA NA PR AU EM PG PL SA M Broad: Adjusted R2 NEO .25 .23 .21 .47 .27 .51 .41 .44 .50 .36 IPIP NEO .32 .43 .52 .50 .38 .65 .54 .58 .57 .50 HEXACO .25 .37 .35 .47 .39 .52 .39 .46 .45 .41 Big Five aspects .32 .54 .67 .44 .69 .53 .67 .61 .53 .56 M .29 .39 .44 .47 .43 .56 .50 .52 .51 .46 Narrow: Adj R2 NEO .30 .25 .24 .54 .38 .55 .48 .51 .56 .42 IPIP NEO .47 .48 .58 .59 .52 .71 .62 .70 .74 .60 HEXACO .38 .44 .44 .51 .44 .63 .45 .58 .61 .50 Big Five aspects .39 .59 .69 .52 .73 .55 .72 .65 .55 .60 M .39 .44 .49 .54 .52 .61 .56 .61 .61 .53 Adj R2 change NEO .06 .02 .03 .06 .11 .04 .07 .07 .06 .06 IPIP NEO .15 .05 .06 .10 .14 .06 .07 .12 .17 .10 HEXACO .13 .07 .09 .04 .05 .11 .06 .11 .16 .09 Big Five aspects .07 .06 .03 .08 .03 .02 .05 .04 .02 .04 M .10 .05 .05 .07 .08 .06 .06 .09 .10 .07 Adj R2 prop increase NEO .24 .09 .16 .13 .43 .08 .16 .16 .13 .17 IPIP NEO .47 .12 .11 .19 .37 .09 .14 .21 .30 .22 HEXACO .51 .19 .26 .09 .13 .20 .14 .24 .36 .24 Big Five aspects .21 .11 .04 .18 .05 .03 .07 .06 .03 .09 M .36 .13 .14 .15 .24 .10 .13 .17 .20 .18 Note. SWL = satisfaction with life; PA = positive affect; NA = negative affect; PR = positive relations; AU = autonomy; EM : PG = personal growth; PL = purpose in life; SA = self-acceptance. Mean values are in bold. environmental mastery; u cd varied somewhat across the different data sets. Openness to actions was a salient predictor to emerge in our data, particularly in relation to personal growth. Openness appears to reflect an orientation toward well-being that involves valuing novelty and nonconformity, and viewing life as a process of growth and change. This is reflected in the strong correlation between values and openness for the Big Five (Parks-Leduc, Feldman, & Bardi, 2015) and the HEXACO (Anglim, Knowles, Dunlop, & Marty, 2017), whereby people who are high on openness tend to value self-direction, stimulation, and universalist values and are less interested in power and conformity. Given that openness is relatively unrelated to life satisfaction, it may provide an example of a personality trait that influences not just the experience of well- being, but the process through which a person achieves the good life. For those high on openness to experience, variety and growth are important, for those low in openness to experience, stability, safety and maintaining tradition may be more critical. Weil-Being Dimensions One of the main insights revealed by the present study concerns the differential patterns of correlations between personality and well-being as one shifts between SWB and PWB. Whereas SWB focuses on the evaluation of the good life, PWB is more strongly reflective of Eudaimonic perspectives. It is important to note, however, that this distinction is theoretical and conceptual, Table 15 Correlations Among HEXACO and IPIP NEO Personality Domains From Combined Dataset Variable 1 2 3 4 5 6 7 8 9 10 HEXACO 1. Honesty-humility 2. Emotionality .06 3. Extraversion .01 -.21 4. Agreeableness .37 -.18 .31 5. Conscientiousness .31 -.11 .21 .22 6. Openness .13 -.18 .19 .19 .17 IPIP NEO 7. Neuroticism -.19 .56 -.65 -.46 -.36 -.26 8. Extraversion -.09 -.08 .83 .17 .11 .13 -.49 9. Agreeableness .67 .22 .12 .53 .26 .05 -.17 .08 10. Conscientiousness .32 -.14 .28 .19 .84 .09 -.48 .19 .32 11. Openness .16 .06 .23 .14 .15 .71 -.19 .30 .20 .14 Note. N = 465. Cross-correlations between personality measures greater than .50 are shown in bold. PERSONALITY AND WELL-BEING 307 Table 16 Correlations Between HEXACO and IPIP NEO Domains and Well-Being Measures for Combined Dataset Measure SWL PA NA PR AU EM PG PL SA M IPIP NEO Neuroticism -.45 -.52 .69 -.46 -.55 -.70 -.47 -.56 -.68 -.56 Extraversion .42 .55 -.30 .63 .30 .53 .53 .49 .52 .47 Openness .09 .32 -.04 .26 .28 .15 .57 .31 .24 .25 Agreeableness .15 .20 -.21 .35 .04 .19 .29 .24 .19 .21 Conscientiousness .27 .39 -.37 .26 .39 .59 .38 .61 .45 .41 HEXACO Honesty-humility .12 .12 -.23 .22 .23 .16 .21 .17 .16 .18 Emotionality -.03 -.07 .37 .00 -.41 -.31 -.09 -.12 -.20 -.18 Extraversion .49 .56 -.45 .64 .37 .62 .52 .55 .64 .54 Agreeableness .24 .26 -.28 .29 .10 .24 .21 .17 .26 .23 Conscientiousness .17 .33 -.26 .16 .30 .44 .31 .50 .31 .31 Openness .07 .25 -.08 .11 .31 .12 .37 .17 .17 .18 HEXACO Neuroticism -.48 -.49 .64 -.48 -.45 -.70 -.44 -.55 -.68 -.55 Note. N = 465; SWL = satisfaction with life; PA = positive affect; NA = negative affect; PR = positive relations; AU = autonomy; EM = environmental mastery; PG = personal growth; PL = purpose in life; SA = self-acceptance. Correlations equal to or larger than .10, .12, and .16 are significant at .05, .01, and .001, respectively. M = mean correlation between the personality trait and well-being variables, where the correlation with negative affect (NA) is reversed. Correlations .30 or above in bold. whereas the empirical differences between these models are less clear cut. All nine dimensions of well-being are positively inter-correlated (after reversing negative affect), despite each capturing important unique variance. Additionally, the nine scales do not segregate into distinct SWB and PWB factors. Thus, it is important to consider both the broad and the scale-specific patterns of personality correlates. First, and in line with recent research (e.g., Anglim & Grant, 2016), many PWB scales showed a much stronger overlap with personality compared with SWB scales. In the meta-analysis, correlations were larger for environmental mastery, personal growth, and self-acceptance, and smaller for life satisfaction, although the PWB scale of autonomy also had smaller correlations. In the domain- and facet-level regression models this pattern was also observed, although positive and negative affect were also predicted somewhat less well. These differences may partially be methodological. PWB is often measured with a 14-item per scale format whereas the standard life satisfaction measure (Diener et al., 1985) involves only five items. Nonetheless, as we discuss below, there are several theoretical reasons why some PWB scales overlap more with particular personality traits. Second, of the three components of SWB, life satisfaction was less well predicted by personality compared with positive and negative affect. This is perhaps unsurprising given that the tendency to experience positive and negative emotions is part of the core content of personality scales (Pytlik Zillig, Hemenover, & Dienstbier, 2002). In contrast, life satisfaction is a cognitive appraisal, influenced both by expectations and evaluations, and the individual's choice of what factors are relevant to that judgment. It is therefore a step removed from summaries of a person's typical behavior and experience. Such factors may help explain why life satisfaction shows a much more modest overlap with personality compared to other dimensions of well-being. Interestingly, the facets of modesty and perfectionism showed negative semipartial correlations with life satisfaction. Thus, whether through objective circumstance, arrogance, or pleasant self-deception, very high life satisfaction is often related to seeing oneself and one's life as superior to those around you. Furthermore, perfectionism may lead people to focus on ways that their life could conceivably be better. At a more general level, it was apparent that each well-being dimension was characterized by a coherent pattern of personality correlates. Specifically, positive affect, unsurprisingly, was well- Table 17 Adjusted R Squared for Regression Models Predicting Weil-Being Measures in Combined Dataset Predictors k SWL PA NA PR AU EM PG PL SA M HEXACO domains 6 .25 .37 .35 .47 .39 .52 .39 .46 .45 .41 NEO domains 5 .26 .43 .49 .50 .36 .63 .55 .55 .53 .48 HEXACO facets 25 .38 .44 .44 .51 .44 .63 .45 .58 .61 .50 NEO domains + HEXACO domains 11 .31 .45 .50 .53 .45 .64 .55 .57 .57 .51 NEO domains + HEXACO facets 30 .41 .50 .52 .59 .49 .70 .57 .64 .67 .57 NEO facets 30 .44 .50 .57 .59 .52 .70 .64 .70 .70 .60 HEXACO domains + NEO facets 36 .46 .50 .57 .59 .54 .71 .64 .71 .71 .60 HEXACO facets + NEO facets 55 .48 .52 .56 .61 .56 .72 .64 .70 .73 .61 Note, n = 465; k = number of predictors in regression model; M = the mean adjusted r-squared value averaged over well-being variables; NEO = IPIP NEO; SWL = satisfaction with life; PA = positive affect; NA = negative affect; PR = positive relations; AU = autonomy; EM = environmental mastery; PG = personal growth; PL = purpose in life; SA = self-acceptance, k is number of predictors. Mean represents the average variance explained for the predictor set over the nine well-being measures. 308 ANGLIM, HORWOOD, SMILLIE, MARRERO, AND WOOD predicted by extraversion and facets related to the tendency to experience positive emotions. Negative affect was strongly related to neuroticism, and most prominently with the facet of depression. Positive relations showed close connections with agreeableness and to some extent extraversion. Autonomy combined common well-being correlates with a fairly unique set of personality correlates that combine impulsiveness, noncompliance, and low trust, with assertiveness and social boldness. Environmental mastery correlated fairly uniformly across personality traits although it did show some elevation for conscientiousness. Personal growth was characterized most uniquely by openness with some amplification for diligence and achievement striving. Purpose in life was particularly well characterized by conscientiousness and especially diligence and achievement striving. Finally, self-acceptance showed a somewhat similar pattern of correlations to that of life satisfaction albeit at much greater levels. Although self-acceptance and life satisfaction are highly correlated, self-acceptance places relatively less emphasis on the external conditions of life. This emphasis on liking or loathing oneself brings it very close to several dimensions of personality, as seen by the particularly large correlation with the facet of depression. Some of these cross-correlations have already been noted in previous research (e.g., Anglim & Grant, 2016; Grant et al., 2009; Sun et al, 2018), and the current study consolidates these observations through the first comprehensive, large sample assessment. Incremental Prediction by Narrow Traits One of the most critical contributions of the present study concerns estimation of the proportional increase in variance explained by facets above and beyond domains. Average incremental variance explained by facets was 17%, 22%, and 24% for NEO, IPIP NEO, and HEXACO taxonomies, respectively. The amount of incremental prediction showed some systematic variation across these three measures, although much less consistency was observed for the Big Five Aspect Scales. In particular, life satisfaction, autonomy, and self-acceptance showed the greatest incremental prediction. These scales are not obviously broader or narrower than other well-being dimensions. Rather they may exhibit a complexity that means that several facets are important as is the case with autonomy. Equally, there may be a particular facet that aligns very closely, perhaps as can be seen with depression and social self-esteem in relation to self-acceptance. A major focus of the literature on incremental facet prediction has been on life satisfaction (R0ysamb et al., 2018; Schimmack et al., 2004; Steel et al., 2019), and this exhibited somewhat greater increases of between 24% and 51% depending on the personality framework. This estimate is broadly consistent with the largest study to report incremental facet prediction to date, albeit limited to life satisfaction, which obtained 33% incremental prediction (R0ysamb et al, 2018). Steel et al. (2019) reported a 78% increase based on a meta-analytic correlation matrix, but it is important to note that meta-analytic regression is problematic. In particular, estimating a regression model with 30 highly correlated predictors, where facet-level intercorrelations are not provided in the primary studies leads to unreliable and often inflated estimates of variance explained. More generally, we consider the proportional increase of 10% to 50% when using hierarchical instruments as noteworthy. Even though much of the perceived value of narrow traits is attributable to the idea that facets might double prediction, more modest incremental prediction is still of practical and theoretical importance. Facets also provide a richer profile of how and why different domains correlate with relevant criteria, and provide a more nu-anced picture of the personality-well-being interface. Interestingly, the HEXACO model was characterized by larger incremental facet prediction (as a proportion) than the Big Five, both in terms of the NEO and IPIP NEO. This is striking, given that the NEO model has fewer domains and more facets than does the HEXACO model, which should lead the NEO model to have stronger incremental prediction. The IPIP NEO also has more items per facet, which should yield more reliable measurement of the unique aspects of each facet. On the other hand, the HEXACO model incudes the interstitial trait of altruism, which is not used in scoring the domains, whereas all of the items of the Big Five facets/aspects are used to compute the domain scores. Critically, none of the HEXACO domains capture the general tendency to experience negative emotions in the same way as Big Five neuroticism (Gaughan et al., 2012). Rather, the HEXACO model distributes content from Big Five neuroticism over various domains including extraversion (r = —.50), emotionality (r = .52), and agreeableness (r = —.38; Gaughan et al., 2012). The most salient observation regarding incremental facet prediction within the HEXACO concerned the emotionality facet of anxiety and the extraversion facets of social self-esteem and liveliness, all of which seem to capture the most affect-related influences on well-being. Limitations and Future Research Because the current meta-analysis is based on self-report measures of personality and well-being, some care is required when generalizing the findings to the latent constructs. Participants vary in the degree to which social desirability influences their responses, and items and scales vary in their degree of socially desirable content (Anglim, Morse, et al., 2017; McCrae & Costa, 1983; Wiggins, 1968). Person- and item-level variance in socially desirable responding can lead to elevated correlations between personality and well-being. This is particularly evident in the minority of studies using low-paid participant samples where many participants engage in satisficing and semirandom responding. We observed that in such studies, correlations between broad personality traits were often elevated, which presumably translates to elevated correlations between personality and well-being. As a consequence, care is needed when evaluating personality measures in terms of how much variance they explain in self-reported well-being. One measure might predict self-reported well-being better because it has more socially desirable items. This may partially explain why the IPIP NEO predicted well-being better than the HEXACO PI R. Similarly, if one sample has more evaluative variance, then this may lead to elevated correlations between personality and well-being. For example, the greater prediction of well-being in the Big Five Aspects dataset may partially be explained by the use of a Mechanical Turk sample. Although several studies have examined other-reports of personality and well-being (Dobewall, Realo, Allik, Esko, & Metspalu, 2013; Schimmack et al., 2004), more research is needed in this PERSONALITY AND WELL-BEING 309 area, particularly involving large samples, full hierarchical measures of personality, and multidimensional models of well-being. Finally, it is worth considering the degree to which the correlations between personality and well-being are attributable to artifactual measurement overlap (Anglim & Grant, 2016; Schmutte & Ryff, 1997). Theoretically, the concepts of personality and well-being can be distinguished in terms of temporal frame-of-reference, implied stability, and degree of attribution to the person versus the situation. Whereas personality is defined as relatively stable and originating more from the person, well-being captures the experience and appraisal of life at a given moment. Nonetheless, it is unsurprising that an individual's general approach to acting in and experiencing the world (i.e., their personality) predicts his or her momentary emotional experiences and evaluations of life. Importantly, the correlations between personality and well-being index the extent and nature of this relationship. So, for example, to remove negative affect from neuroticism, or positive affect from extraversion is to fundamentally change the nature of these personality traits. However, many important research questions remain regarding the causal processes that relate personality and well-being. Facet-level analysis provides some perspective about which aspects of a given trait are more or less important in predicting different dimensions of well-being. Nonetheless, the literature would benefit from more experimental and experience sampling research exploring these questions (e.g., Jacques-Hamilton, Sun, & Smillie, 2019). Conclusion The current research reaffirms that personality is critical to the experience of well-being. This is consistent with set-point theories of well-being (Cummins, 2015; Headey & Wearing, 1989; Headey & Wearing, 1992), and the idea that well-being is relatively stable despite short-term fluctuations in response to many transient events. However, it is also important to remember that personality traits are not set like plaster but rather are malleable, with a wealth of evidence that traits change across the life span (Ashton & Lee, 2016; McCrae et al, 1999; Soto, John, Gosling, & Potter, 2011), after specific experiences (e.g., Zimmermann & Neyer, 2013) or interventions (e.g., Roberts et al., 2017), and even according to one's trait change-goals (e.g., Hudson & Fraley, 2015). It would therefore be inappropriate to interpret the strong relation between personality and well-being as indicative of the immutability of human happiness. Rather, efforts to improve well-being might target the most critical aspects of one's habitual or characteristic patterns of behavior and experience, as reflected in basic personality traits. In summary, we have provided the most comprehensive assessment yet of the relations between personality traits and dimensions of well-being. 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