Consensually defined facets of personality as prospective predictors of change in depression symptoms.

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Consensually Defined Facets of Personality as Prospective Predictors of Change in Depression Symptoms Kristin Naragon-Gainey and David Watson Assessment published online 26 March 2014 DOI: 10.1177/1073191114528030 The online version of this article can be found at: http://asm.sagepub.com/content/early/2014/03/24/1073191114528030

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ASMXXX10.1177/1073191114528030AssessmentNaragon-Gainey and Watson

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Consensually Defined Facets of Personality as Prospective Predictors of Change in Depression Symptoms

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Kristin Naragon-Gainey1 and David Watson2

Abstract Depression has robust associations with personality, showing a strong relation with neuroticism and more moderate associations with extraversion and conscientiousness. In addition, each Big Five domain can be decomposed into narrower facets. However, we currently lack consensus as to the contents of Big Five facets, with idiosyncrasies across instruments; moreover, few studies have examined associations with depression. In the current study, community participants completed six omnibus personality inventories; self-reported depressive symptoms were assessed at baseline and 5 years later. Exploratory factor analyses suggested three to five facets in each domain, and these facets served as prospective predictors of depression in hierarchical regressions, after accounting for baseline and trait depression. In these analyses, high anger (from neuroticism), low positive emotionality (extraversion), low conventionality (conscientiousness), and low culture (openness to experiences) were significant prospective predictors of depression. Results are discussed in regard to personality structure and assessment, as well as personality–psychopathology associations. Keywords personality scales and inventories, personality structure, personality assessment, facets, Big Five, depression A large body of research has amassed over the past several decades regarding the association between depression and personality traits. This association is of theoretical and clinical interest for numerous reasons: personality can inform case conceptualization and treatment for depression, traits may serve as an intermediate phenotype for depression or a source of comorbidity between depression and other disorders, and extreme trait levels can identify individuals who may be at risk for developing depression (see Klein, Kotov, & Bufferd, 2011). Importantly, personality and depression have common genetic variance, establishing the existence of shared etiological sources (e.g., Kendler, Gatz, Gardner, & Pedersen, 2006; Kendler & Myers, 2010). A brief summary of this literature is provided below, but note that there are a number of extensive reviews that provide greater detail, particularly regarding domain-level trait associations with depression (e.g., Klein et al., 2011; Kotov, Gamez, Schmidt, & Watson, 2010; Lahey, 2009; Malouff, Thorsteinsson, & Schutte, 2005).

Review of Previous Evidence Cross-Sectional Data Higher Order Relations. Kotov et al’s. (2010) meta-analysis found several robust associations between depression and the Big Five—one of the most widely used personality

taxonomies consisting of the broad, higher order traits of neuroticism, extraversion, conscientiousness, agreeableness, and openness to experience. Specifically, as compared with normal controls, individuals with major depressive disorder (MDD) had elevated levels of neuroticism (Cohen’s d = 1.33) and lower levels of conscientiousness (d = −0.90). Extraversion was not strongly related to depression (d = −0.62), with only some studies showing a significant effect. This stands in contrast to a moderate association found in numerous correlational studies (e.g., Brown, Chorpita, & Barlow, 1998; Watson, Clark, & Carey, 1988; Watson, Gamez, & Simms, 2005), but it is consistent with the absence of a significant genetic correlation between extraversion and depression (Kendler & Myers, 2010). Levels of agreeableness and openness to experience were not related to MDD status in the Kotov et al. meta-analysis. The effect sizes were even greater in magnitude when comparing those with dysthymic disorder to normal controls, as one would 1

University at Buffalo, State University of New York, Buffalo, NY, USA University of Notre Dame, Notre Dame, IN, USA

2

Corresponding Author: Kristin Naragon-Gainey, Department of Psychology, University at Buffalo, State University of New York, Park Hall 216, Buffalo, NY 14260, USA. Email: [email protected].


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expect given the conceptualization of dysthymic disorder as a chronic, “trait-like” condition. It is important to note that the above pattern of associated personality traits (i.e., high neuroticism, low conscientiousness, low extraversion) was common to most disorders examined (e.g., anxiety disorders, substance-use disorders), although some studies have found higher levels of neuroticism and lower levels of extraversion in depression relative to most other emotional disorders (e.g., Watson et al., 2005). Lower Order Relations. Personality traits are hierarchical, in that each of the Big Five can be further broken down into specific components (i.e., facets). For example, extraversion may be broken down into related but distinguishable facets, such as positive emotionality, sociability, ascendance, and fun seeking (Naragon-Gainey, Watson, & Markon, 2009). Several researchers have stressed the importance of identifying facets that may mediate the association between broad traits and individual disorders, as more precise and specific conclusions then can be drawn regarding personality–psychopathology relations and etiological sources (Klein et al., 2011; Kotov et al., 2010; NaragonGainey et al., 2009; Rector, Bagby, Huta, & Ayearst, 2012). Facet-level analyses can clarify which component of a trait is responsible for domain-level associations with depression, or they may reveal a substantial facet-level association that was masked by nonsignificant associations at the domain level (see Paunonen & Ashton, 2001, for a discussion of narrow vs. broadband traits). For example, crosssectional multivariate analyses indicate that depression’s association with extraversion is mediated via the positive emotionality facet only, rather than the other components of the trait (Naragon-Gainey et al., 2009). Thus, the inconsistent results regarding depression’s association with extraversion may be due, in part, to the use of different measures of extraversion that emphasize this positive emotionality facet to a greater or lesser extent (Klein et al., 2011; Kotov et al., 2010). Relatively few studies have examined facet-level associations between the Big Five and depression, and nearly all of them measured facet traits with the commonly used NEO Personality Inventory-Revised (NEO PI-R, Costa & McCrae, 1992). Depression was associated with most or all facets of neuroticism at the zero-order level, in both currently depressed patients and those in remission from depression (Bagby et al., 1996; Bagby et al., 1997; Bagby, Joffe, Parker, Kalemba, & Harkness, 1995; Bienvenu et al., 2004; Chopra et al., 2005; Harkness, Bagby, Joffe, & Levitt, 2002; Rector et al., 2012; Rector, Hood, Richter, & Bagby, 2002) as well as in a large community study with multiple personality inventories (Grucza & Goldberg, 2007). However, multivariate analyses that account for shared variance among the neuroticism facets have found that the NEO PI-R Depression facet (a scale assessing trait levels of

sadness, depression, and guilt) is most strongly and specifically associated with depression. There is some evidence that the Anxiety and Hostility facets may also have a specific association with depression, beyond higher order neuroticism (Chioqueta & Stiles, 2005; Costa, Bagby, Herbst, & McCrae, 2005), but other studies have not found this association (Uliaszek et al., 2009). In interpreting these findings, it is important to note that there is substantial item overlap between depression measures and the Depression facet, with the primary difference being the time frame assessed (i.e., current feelings of depression or “state” depression vs. feelings of depression in general or “trait” depression, respectively; e.g., Klein et al., 2011; Ormel, Rosmalen, & Farmer, 2004; Uliaszek et al., 2009). Thus, the Depression facet may be best thought of as a measure of general depression levels, rather than an independent construct, particularly when both measures are based on selfreported, retrospective responses. Within the extraversion domain, low levels of the facet Positive Emotions (i.e., a tendency to experience positive moods such as joy and enthusiasm) were linked to depression (current and remitted), with some studies also finding links to low levels of Assertiveness and Warmth (Bagby et al., 1995; Bagby et al., 1997; Bienvenu et al., 2004; Chioqueta & Stiles, 2005; Chopra et al., 2005; Costa et al., 2005; Harkness et al., 2002; Rector et al., 2002; Rector et al., 2012). Results were inconsistent regarding facets within openness to experience, conscientiousness, and agreeableness, although several studies reported negative associations with Self-Discipline within the conscientiousness domain and Actions within the openness domain (Bagby et al., 1995; Bienvenu et al., 2004; Chioqueta & Stiles, 2005; Costa et al., 2005; Rector et al., 2012). Overall, the Depression and Positive Emotions facets appear to have the strongest and most specific connections to depression, but conclusions should be tentative given the small number of studies and inconsistent results. Lack of a Consensual Faceted Structure. The above literature is limited in that the studies used the same personality measure (viz., the NEO PI-R) that represents only one possible conceptualization of personality facets within the Big Five. Other measures assess somewhat different sets of traits within each domain, including some that are not captured by the NEO PI-R (e.g., No Somatic Complaints in the Hogan Personality Inventory; Harm Avoidance in the Multidimensional Personality Questionnaire; Seriousness within the Six-Factor Personality Questionnaire). Currently, there is not a consensus as to the contents of the facets within each of the Big Five domains; rather, each instrument has its own specific model, with shared and unique features across instruments (Costa & McCrae, 1998; Naragon-Gainey & Watson, 2011; Roberts, Chernyshenko, Stark, & Goldberg, 2005; Saucier & Ostendorf, 1999). Thus, until a consensual faceted structure is identified, findings regarding the associations between personality facets and


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Naragon-Gainey and Watson constructs such as depression will be difficult to synthesize due to idiosyncrasies of the particular personality measure (and its implied structural model of personality).

Prospective Data Higher Order Relations. Although cross-sectional studies are useful in beginning to characterize the association between depression and personality, they are susceptible to the influence of current mood state (i.e., mood-state distortion; e.g., Naragon-Gainey, Gallagher, & Brown, 2013) and are unable to address the direction of the association (i.e., whether personality affects depression, depression affects personality, or the associations are bidirectional). Numerous longitudinal studies have prospectively predicted the first onset of major depression from premorbid personality traits, consistently finding a positive association between neuroticism and a future major depressive episode (e.g., Clayton, Ernst, & Angst, 1994; de Graaf, Bijl, Ravelli, Smit, & Vollenbergh, 2002; Fanous, Neale, Aggen, & Kendler, 2007; Duggan, Lee, & Murray, 1990; Kendler et al., 2006; Kendler, Neale, Kessler, Heath, & Eaves, 1993; Ormel, Oldehinkel, & Vollebergh, 2004). Evidence for extraversion as a vulnerability factor for depression is more mixed, with some studies indicating that low levels of extraversion are a significant but weak predictor of the onset of MDD (Kendler et al., 2006; Rorsman, Grasbeck, Hagnell, Isberg, & Otterbeck, 1993) and others failing to find a significant association (Fanous et al., 2007, Hirschfeld et al., 1989, Kendler et al., 1993). Again, these discrepant findings may be due to different associations at the facet level and the specific trait measures that were used. To our knowledge, researchers have not examined the prospective prediction of the onset of major depression from baseline levels of conscientiousness, openness to experience, or agreeableness. Other studies have taken a dimensional approach, assessing change in depressive symptoms rather than the onset of major depressive episodes. Consistent with the above findings, high neuroticism and low extraversion predicted change in depression symptoms 1 year later among those with mood and anxiety disorders (Spinhoven et al., 2011). In addition, low conscientiousness predicted an increase in depression 6 months later among a group of patients with MDD (Anderson & McLean, 1997). Lower Order Relations. Very little information is available regarding the prospective prediction of depression symptoms from facet-level traits. However, among a group of depressed individuals receiving treatment, two NEO PI-R extraversion facets (low Excitement Seeking and low Positive Emotions), as well as several openness facets (low Fantasy, Aesthetics, Actions, and Values), were significant predictors of severity of follow-up depression (Bagby et al., 2008).

The Current Study The current study is the first to use a comprehensive model of Big Five facets as prospective predictors of depressive symptoms, assessing change in these symptoms over the course of 3 years in a large community sample.1 We have two primary aims: (a) to identify “consensual” Big Five facets that recur across instruments and (b) to clarify which components of each broad trait are specifically predictive of depression symptoms, including individual facets that may be obscured by nonsignificant associations at the domain level. This is particularly important for the extraversion domain, where domain-level results have been inconsistent across studies and depression is known to be differentially related to individual facets (Naragon-Gainey et al., 2009). An examination of the depression measure used in the current study (see Method section) revealed that 10 out of 24 items overlap with content in Depression facet measures, consistent with previous evidence of substantial shared content between the two constructs (e.g., Ormel, Rosmalen, et al., 2004). Thus, we decided to treat the Depression facet as an alternative measure of depressive symptoms, holding it constant in facet-level regression analyses. This approach allows us to determine which facets are incrementally predictive of change in depressive symptoms, above and beyond a general tendency to feel depressed. Regarding the first aim, few studies have conducted multi-inventory facet-level structural analyses. However, based on two prior studies that have identified consensually defined facets, we expected to find a four-faceted model of extraversion (i.e., sociability, ascendance, positive emotionality, and fun seeking; Naragon-Gainey et al., 2009) and at least the three “core” facets of conscientiousness identified by Roberts et al. (2005; i.e., industriousness, order, and selfcontrol). Regarding the second aim, based on prior crosssectional and prospective data, we hypothesized that low positive emotionality in the extraversion domain would be associated with change in depression, whereas the other extraversion facets would not. Given very few facet-level analyses in the past (with only one longitudinal study) and conflicting results, we did not have strong a priori expectations for associations with facets from the other domains. We use a “bottom-up” approach, specifying facets for each domain by conducting exploratory analyses of the scales included in several omnibus personality inventories. Multiple inventories were included that are based on distinct structural models of personality (including the Big Three, Big Five, and Six-Factor models) in order to provide thorough coverage of potential facets within each domain, thereby yielding a comprehensive structure that does not simply reflect the unique, idiosyncratic features of a single measure. In addition, examining a nonclinical sample has the advantage in this context of capturing a broad range of symptom severity and being more


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representative of the general population than a typical clinical sample.

Method Participants and Procedure Permission was obtained to conduct secondary data analyses on the Eugene–Springfield Community Sample (Goldberg, 2008).2 This sample of over 1,000 individuals was initially recruited in 1993 from lists of home owners who were contacted and volunteered to complete questionnaires for at least 5 to 10 years; sample sizes in the current study range from 398 to 598 depending on the analysis and measures included. At the time of recruitment, ages ranged from 18 to 85 years. Participants were paid for each survey that they returned over the course of the study. For the period from 1993 to 2003, retention rates were high: 88% of the initial sample completed at least two of the most recent four surveys. The initial sample was 56.9% female and almost exclusively Caucasian (98.4%; see Goldberg, 2008 for further detail regarding this sample). Of the measures used in the current study, the depression outcome measure was administered in 2002 and all personality measures were administered prior to this (i.e., between 1993 and 1999; see Measures subsection for specific administration dates), such that depression symptoms were predicted prospectively from the personality traits.

Measures Indicators for the Big Five facets were selected based on a review of factor-analytic studies that related the facets of one or more omnibus personality inventories to the Big Five. We selected a range of inventories from different traditions in order to have broad coverage of scales that may be relevant to a faceted model of personality. Based on empirical results and theory (i.e., Byravan & Ramanaiah, 1995; Cattell, 1995; Chernyshenko, Stark, & Chan, 2001; Church, 1994; Detwiler & Ramanaiah, 1996; Doster et al., 2000; Jackson, Ashton, & Tomes, 1996; Johnson, 1994; Johnson, 2000; Markon, Krueger, & Watson, 2005; Paunonen & Jackson, 1996; Roberts et al., 2005; Rossier, Meyer de Stadelhofen, & Berthoud, 2004), we selected a priori potential indicators for facets within each of the Big Five domains as shown in Table 1, ranging from 16 to 28 scales per domain. In interpreting these placements, it should be emphasized that the name of the scale does not always correspond clearly to its content or empirical correlates; for example, the Hogan Personality Inventory (HPI) Empathy scale primarily reflects low irritability, and the Jackson Personality Inventory–Revised (JPI-R) Responsibility scale focuses on having a strong conscience and a sense of duty toward others (rather than other meanings of “responsibility,” such as dependability or reliability). Note also that Roberts

et al. (2005) previously conducted analyses in this sample to examine the lower order structure of conscientiousness, but they selected a slightly different set of instruments (i.e., four of six instruments used in their study and the current study are overlapping). For those four common instruments, we used the same markers of conscientiousness facets that they did. On examination of the personality scales, only two scales (NEO PI-R Positive Emotions and Multidimensional Personality Questionnaire [MPQ] Wellbeing) in the data set appeared to be strong markers of positive emotionality, and three or more indicators per factor are desirable for exploratory factor analysis (e.g., MacCallum, Widaman, Zhang, & Hong, 1999). Given our hypothesis that positive emotionality will be associated with depression, we created two additional positive emotionality scales from personality adjectives available in the data set (see below) to allow a potential positive emotionality factor to emerge in analyses. Center for Epidemiologic Studies–Depression Scale (CES-D; Radloff, 1977). Depression was assessed using Goldberg’s revision of the CES-D Scale, a commonly used measure of depression. In this revision, four depression items were added to the 20 original CES-D items to increase coverage (Goldberg, 2008). Each item was rated on a 5-point Likerttype scale (1 = not at all in the past week to 5 = most or all of the time in the past week). The scale was administered in 1997 and in 2002 in this sample, with the first administration serving as the baseline measure and the later administration serving as the outcome for the current study. Sixteen Personality Factor Questionnaire (16PF; Conn & Rieke, 1994). The 16PF is one of the oldest omnibus personality inventories that is still frequently used. The fifth edition includes 185 items with a 3-point response format. There are 16 primary scales, as well as five higher order “global” factors. The internal consistency of the 16PF scales ranges from .66 to .86, and the scales have strong convergent and discriminant validity with the NEO PI-R and other Big Five scales (Conn & Rieke, 1994). The 16PF was administered in this sample in 1996. Hogan Personality Inventory (HPI; Hogan & Hogan, 1995). The HPI has 206 true/false items that measure seven higher level traits (i.e., Ambition, Sociability, Likeability, Prudence, Adjustment, Intellectance, and School Success) and 44 facet-level constructs; the latter facets are the focus of the current study. The mean internal consistency for these scales is .80, and they converge well with measures of the Big Five (Hogan & Hogan, 1995). The HPI was administered in 1997. Jackson Personality Inventory–Revised (JPI-R; Jackson, 1994). The JPI-R consists of 300 true/false items that form


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Naragon-Gainey and Watson Table 1. Potential Indicators for Big Five Facets Within Each Domain. Domain Neuroticism

Extraversion

Conscientiousness

Agreeableness

Openness

Indicators NEO PI-R Anxiety, NEO PI-R Hostility, NEO PI-R Depression, NEO PI-R Self-Consciousness, NEO PI-R Impulsiveness, NEO PI-R Vulnerability to Stress, MPQ Stress Reaction, 6FPQ Autonomy,a 6FPQ Individualism,a 6FPQ Self-Reliance,a JPI-R Anxiety, JPI-R Cooperativeness, JPI-R Empathy,a 16PF Emotional Stability, 16PF Apprehension, 16PF Tension,a 16PF Vigilance,a HPI Identity, HPI Not Anxious, HPI No Depression, HPI No Guilt, HPI Even-Temperedness, HPI Empathy, HPI Confidence, HPI Calm, HPI No Somatic Complaints,a HPI Self-Focusa NEO PI-R Warmth, NEO PI-R Gregariousness, NEO PI-R Assertiveness, NEO PI-R Activity, NEO PI-R Excitement Seeking, NEO PI-R Positive Emotion, MPQ Wellbeing, MPQ Social Closeness, MPQ Social Potency,a 6FPQ Affiliation, 6FPQ Exhibition, 6FPQ Self-Reliance, 6FPQ Dominance,a JPI-R Sociability, JPI-R Energy Level,a JPI-R Social Confidence, 16PF Social Boldness, 16PF Liveliness, 16PF Warmth, 16PF Privateness, 16PF Self-Reliance, HPI Exhibitionistic, HPI Likes Parties, HPI Likes Crowds, HPI Leadership, HPI Entertaining, HPI Impulse Control, HPI Thrill Seeking,a HPI Likes Peoplea NEO PI-R Competence, NEO PI-R Order, NEO PI-R Dutifulness, NEO PI-R Achievement-Striving, NEO PI-R Self-Discipline, NEO PI-R Deliberation, MPQ Control, MPQ Achievement, MPQ Tradition, MPQ Harm Avoidance,a 6PFQ Cognitive Structure, 6PFQ Order, 6PFQ Deliberateness, 6PFQ Achievement, 6PFQ Endurance, 6PFQ Seriousness,a JPI-R Organization, JPI-R Responsibility,a 16PF Perfectionism, 16PF Rule-Consciousness,a HPI Moralistic, HPI Virtuous, HPI Not Spontaneous, HPI Avoids Trouble, HPI Not Autonomous,a HPI Mastery,a HPI Competitivea NEO PI-R Trust, NEO PI-R Straightforwardness, NEO PI-R Altruism, NEO PI-R Compliance, NEO PI-R Modesty, NEO PI-R Tender-Mindedness, MPQ Aggression, 6FPQ Dominance, 6FPQ Even-Tempered, 6FPQ Abasement,a JPI-R Risk-Taking,a 16PF Dominance, 6PFQ Good-Natured, 16PF Sensitivity,a HPI Caring, HPI No Hostility, HPI Easy to Live With, HPI Sensitive,a HPI Trustinga NEO PI-R Fantasy, NEO PI-R Aesthetics, NEO PI-R Feelings, NEO PI-R Actions, NEO PI-R Ideas, NEO PI-R Values, MPQ Absorption, MPQ Traditionalism, 6FPQ Change, 6FPQ Understanding, 6FPQ Breadth of Interest, JPI-R Complexity, JPI-R Breadth of Interest, JPI-R Innovation, JPI-R Tolerance,a JPI-R Traditional Values, 16PF Openness to Change, 16PF Abstractedness, 16PF Reasoning,a HPI Reading, HPI Culture, HPI Generates Ideas, HPI Experience Seeking, HPI Good Memory,a HPI Educationa

Note. NEO PI-R = NEO Personality Inventory–Revised; MPQ = Multidimensional Personality Questionnaire; 16PF = Sixteen Personality Factor Questionnaire; 6FPQ = Six Factor Personality Questionnaire; JPI-R = Jackson Personality Inventory–Revised; HPI = Hogan Personality Inventory. a. Indicates that this indicator was dropped based on the results of single instrument factor analyses (see text).

15 content scales. In two studies, median coefficient alphas were .90 and .93, and the scales show good convergent and discriminant validity with other personality measures (Jackson, 1994). This measure was administered in 1999.

order personality measures (Costa & McCrae, 1992). This measure was administered in 1994.

Multidimensional Personality Questionnaire (MPQ; Tellegen, in press). The MPQ includes 276 true/false items that stem from the Big Three personality tradition. The instrument contains 11 content scales. The MPQ scales have good internal consistency as well as strong patterns of convergent and discriminant validity (Tellegen, in press). The MPQ was administered in 1999.

Six-Factor Personality Questionnaire (6FPQ; Jackson, Paunonen, & Tremblay, 2000). The 6FPQ is a 108-item inventory that includes four of the Big Five domains (i.e., extraversion, agreeableness, openness to experiences, and low neuroticism [referred to as Independence]). Conscientiousness is broken into two domains in this measure, consisting of Methodicalness and Industriousness. Each of the six domains has three facets within it, and responses are made on a five-point scale. The 6FPQ was administered in 1999.

NEO Personality Inventory–Revised (NEO PI-R; Costa & McCrae, 1992). The NEO PI-R measures personality domains and facets in the five-factor model. Respondents rate themselves using a 5-point Likert-type scale. Each of the five domains has six facets, each of which contains eight items. The facets have acceptable internal consistency (αs = .56 to .81) and the domains have good long-term retest reliability (r = .63 to .83 after 6 years). In addition, the facets show good convergent and discriminant validity with other lower

Person-Descriptive Adjectives. The Eugene–Springfield data set includes two large sets of single-word adjectives that were rated on a Likert-type scale and were administered in 1993 and 1995. For this study, adjectives were selected that were likely to be good markers of positive emotionality, given a shortage of available established measures of positive emotionality in this data set. Using the Positive Affect and Joviality scales from the Expanded Form of the Positive and Negative Affect Schedule (PANAS-X; Watson & Clark,


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1999) as models, two scales were formed (one from each administration). The first composite (Positive Emotionality composite) consists of the terms active, alert, cheerful, confident, energetic, vigorous, and animated, while the second (Joviality composite) consists of happy, joyful, delighted, cheerful, excited, enthusiastic, lively, and energetic. Coefficients alpha were indicative of strong internal consistency (.81 and .87, respectively).

Data Analysis All analyses were run in SAS 9.3. Although scales were selected a priori as potential indicators of facets within each of the Big Five domains (see Table 1), we used principal factor analysis to examine these markers empirically to ensure that only strong, specific indicators of each facet were included in subsequent analyses. We proceeded in two steps: (a) For each instrument, a separate exploratory factor analysis was conducted on its scales to assess whether the facets conform to the Big Five domains in the hypothesized manner in this sample. If a scale was not a strong and specific marker of the expected domain, it was omitted from subsequent analyses (see below for more detail). (b) Next, all the remaining indicators across inventories were factor analyzed by domain (i.e., all the neuroticism scales, all the extraversion scales, etc.) to determine the structure of the facets for each domain. The personality factors were expected to be correlated, so oblique rotations (“promax”) were used for all factor analyses. The number of factors extracted was determined using the minimum average partial (MAP; Velicer, 1976) test, which is based on the examination of residual correlation matrices. This method is more objective than the examination of scree plots or other rules of thumb for factor extraction (e.g., O’Connor, 2000). After establishing the faceted structure of each of the Big Five domains with the above factor analyses, regressionbased factors scores were extracted. Time 2 depression was then regressed onto these factor scores, one domain at a time, while holding baseline depressive symptoms and trait depression constant. Because of the large-scale nature of this study that included data collection over the course of 7 years, not all participants completed all measures and sample sizes vary by analysis.

Results Personality Structure Factor Analyses of Each Instrument. Initial analyses examined each instrument separately in order to determine whether scales were good markers of one of the Big Five domains, as hypothesized in Table 1. Thus, “gold standard” markers of the Big Five were needed to serve as common reference points across instruments. The NEO PI-R was

chosen as the template to which other inventories were anchored because of its robust factor structure (see Costa & McCrae, 1992), its widespread influence and use as a faceted measure of the five-factor model, and its desirable characteristic of providing even and thorough coverage (i.e., six scales) for each of the five domains. To this end, the NEO PI-R facets were included along with the scales for each of the other instruments in five separate factor analyses with promax rotations (e.g., a factor analysis with all the NEO PI-R facets and all the MPQ scales, a second with all the NEO PI-R facets and all the HPQ scales, etc.). Five factors (one for each Big Five domain) were extracted in each of these analyses. Scales that (a) had a primary standardized loading on their expected domain of less than |.40| or (b) had one or more secondary loadings within about |.10| of the primary loading were dropped from subsequent analyses. These criteria were not applied to the NEO PI-R scales because they were intended to establish the structure against which the other scales could be evaluated. As marked by the NEO PI-R facets, five factors corresponding to the Big Five were recovered for the separate factor analyses of the MPQ, HPI, 6FPQ, 16PF, and JPI-R. After applying the above criteria, eight neuroticism indicators (16PF Tension, 16PF Vigilance, 6FPQ Autonomy, 6FPQ Individualism, 6FPQ Self-Reliance, JPI-R Empathy, HPI No Somatic Complaints, HPI Self-Focus) were omitted from subsequent analyses, as well as five extraversion indicators (JPI-R Energy Level, MPQ Social Potency, 6FPQ Dominance, HPI Likes People, HPI Thrill Seeking), seven conscientiousness indicators (16PF Rule-Consciousness, 6FPQ Seriousness, HPI Not Autonomous, HPI Mastery, HPI Competitive, MPQ Harm Avoidance, JPI-R Responsibility), five agreeableness indicators (16PF Sensitivity, 6FPQ Abasement, JPI-R Risk-Taking, HPI Sensitive, HPI Trusting), and four openness indicators (JPI-R Tolerance, 16PF Reasoning, HPI Good Memory, HPI Education). After omitting the above scales, a range of 14 indicators (agreeableness) to 24 indicators (extraversion) remained for the subsequent domain-level analyses. Full results are available from the first author on request. Factor Analyses of Facets Within Each Domain. Next, all the remaining indicators across inventories were submitted to exploratory factor analyses—one analysis for each domain— to further refine the structural model (see Tables 2 through 6). In all the analyses, each factor had at least three strong indicators (i.e., factor loadings of .50 to .80). MAP indicated that three factors should be extracted for the neuroticism domain (Table 2). Based on the content of the scales that had primary loadings on each factor, they were labeled Anxiety (e.g., fear, worry, tension), Anger (e.g., loses temper easily, hostile, irritable), and Depression (e.g., feeling down, guilty, low self-confidence). For the extraversion factor analysis, MAP indicated that four factors should be extracted: Sociability (e.g., warmth, gregariousness,


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Naragon-Gainey and Watson Table 2. Standardized Loadings From Exploratory Factor Analysis of Facets in the Neuroticism Domain.

16PF Apprehension JPI-R Cooperativeness NEO PI-R Anxiety JPI-R Anxiety NEO PI-R Self-Consciousness HPI Not Anxious MPQ Stress Reaction NEO PI-R Vulnerability NEO PI-R Hostility HPI Even-Temperedness HPI Empathy HPI Calm NEO PI-R Impulsiveness HPI No Depression HPI Identity HPI Confidence NEO PI-R Depression HPI No Guilt 16PF Emotional Stability

Anxiety

Anger

Depression

.69 .69 .67 .64 .57 −.57 .53 .41 –.03 .18 .00 –.23 −.05 .10 .12 −.31 .29 −.09 −.15

−.06 −.16 .15 .38 −.14 −.29 .36 .10 .78 −.74 −.73 –.46 .37 −.12 −.09 .26 .10 −.18 −.25

.15 −.11 .09 −.09 .33 .03 .10 .34 .02 −.11 .06 –.04 .28 −.80 −.65 −.59 .56 −.51 −.50

Note. N = 510. Loadings greater than or equal to |.30| are shown in bold. NEO PI-R = NEO Personality Inventory–Revised; MPQ = Multidimensional Personality Questionnaire; 16PF = Sixteen Personality Factor Questionnaire; 6FPQ = Six-Factor Personality Questionnaire; JPI-R = Jackson Personality Inventory–Revised; HPI = Hogan Personality Inventory. The first three eigenvalues were 8.45, 1.14, and 0.80.

enjoys parties), Ascendance (e.g., assertiveness, likes to be a leader, dominant), Positive Emotionality (e.g., feeling cheerful, energetic), and Excitement Seeking (e.g., enjoying adventures, spontaneous; Table 3). Four factors were also extracted from the conscientiousness analysis, labeled Order (e.g., perfectionistic, neat, exacting), Achievement (e.g., disciplined, follows through, does not procrastinate), Deliberation (e.g., plans ahead, careful, thinks things through), and Conventionality (e.g., follows rules, respects authority, would not cheat, controls behavior; Table 4). Three factors were extracted from the agreeableness domain as indicated by MAP (see Table 5): Good-Natured (e.g., calm, can take criticism, avoids confrontation, does not hold grudges), Modesty (e.g., lets other lead, does not challenge them, does not think highly of self), and Empathy (e.g., putting others first, sympathetic, trusting). Last, three factors were extracted from openness to experience: Culture (e.g., intellectual curiosity and complexity, broad interests, appreciation of art/beauty/reading), Creativity (e.g., imaginative, original, open to emotions, and new experiences), and Liberalism (e.g., questioning of values of authority figures, challenge traditional values, liberal moral/political perspectives; Table 6). Regression-based factor scores were extracted from these factor analyses; Table 7 presents the correlations

among the factor scores for the facets. The mean correlation for the facet-level factors within the same domain was .51, whereas the mean correlation for facets across different domains was |.19|, suggesting that the expected hierarchical structure of the facets and domains was present. The neuroticism facets were particularly strongly intercorrelated (rs = .62 to .73; mean r = .67).

Associations Between Facets and Depression Symptoms Descriptive Statistics for Depression Symptoms. Mean scores on the depression symptom measures were 41.18 (SD = 13.55; range = 24-98) at baseline and 44.36 (SD = 12.48; range = 28-110) at Time 2, where scores could range from a possible 24 to 120. The distributions were positively skewed, as expected in a nonclinical sample, but there was a sizeable minority of participants who endorsed significant depressive symptoms (e.g., at both administrations, about 10% of the sample had scores of 60 or higher). Bivariate Correlations. Domain-level scores were calculated as the sum of the six constituent NEO PI-R facets. Neuroticism was strongly associated with both administrations of the depression measure (rs = .48, p < .001). Low extraversion (rs = −.20 and −.22, ps < .001) and low Conscientiousness (rs = −.21 and −.26, p < .001) were more modestly associated with depression while Agreeableness was significantly associated with Time 2 depression only (r = −.14, p < .001). Openness to experience was not correlated with either depression measure (rs = .02). The first two columns of Table 7 show the correlations of baseline and Time 2 depression with each of the personality facets. Both baseline and Time 2 depression symptoms were moderately to strongly associated with all three facets of neuroticism (rs = .43 to .59), with particularly strong links to the Depression facet (rs = .59 and .56, respectively). This is consistent with our conceptualization of the Depression facet as an alternative measure of depression symptoms; note that it was as strongly correlated with baseline and Time 2 depression as were the two administrations of the depression measures with one another (r = .53). In addition, the Positive Emotionality facet of extraversion was moderately inversely associated with both depression assessments (rs = −.32 and −.39),3 and the Deliberation facet of conscientiousness was more weakly associated with depression (rs = −.24 and −.28). Although there were other significant facet-level correlations with depression, the magnitude of the associations was small. Prospective Regressions. We first conducted a domain-level regression (Table 8) to characterize higher order associations between depression and the Big Five. Holding baseline depression constant, high neuroticism was a strong


8

Assessment

Table 3. Standardized Loadings From Exploratory Factor Analysis of Facets in the Extraversion Domain.

MPQ Social Closeness JPI-R Sociability 16PF Self-Reliance 16PF Warmth NEO PI-R Gregariousness 6FPQ Self-Reliance NEO PI-R Warmth 6FPQ Affiliation 16PF Privateness JPI-R Social Confidence NEO PI-R Assertiveness HPI Leadership 16PF Social Boldness 6FPQ Exhibition HPI Exhibitionistic HPI Entertaining Joviality compositea NEO PI-R Positive Emotions MPQ Well-being Positive Emotionality compositea NEO PI-R Activity NEO PI-R Excitement Seeking 16PF Liveliness HPI Likes Crowds HPI Impulse Control HPI Likes Parties

Sociability

Ascendance

.83 .75 −.75 .69 .69 −.67 .54 .54 −.48 .05 −.07 −.20 .26 .19 −.07 −.01 .07 .09 −.05 −.10 –.16 −.12 .29 .16 .25 .28

.02 .03 .17 .09 .00 .21 .05 .29 −.27 .84 .75 .75 .73 .73 .54 .42 −.08 −.08 −.02 .21 .34 .03 .06 −.11 −.17 .15

Positive Emotionality .04 –.08 .04 .05 .00 .11 .40 .12 −.01 .08 .12 –.01 .04 −.10 −.13 −.03 .82 .74 .68 .59 .44 .10 .08 .05 −.02 –.03

Excitement Seeking −.05 .15 −.15 −.19 .23 .07 −.13 .08 .18 −.15 −.05 .07 –.14 .17 .32 .27 .01 .11 .08 .00 .02 .67 .57 .56 −.51 .47

Note. N = 439. Loadings greater than or equal to |.30| are shown in bold. NEO PI-R = NEO Personality Inventory–Revised; MPQ = Multidimensional Personality Questionnaire; 16PF = Sixteen Personality Factor Questionnaire; 6FPQ = Six-Factor Personality Questionnaire; JPI-R = Jackson Personality Inventory–Revised; HPI = Hogan Personality Inventory. The first four eigenvalues were 8.58, 2.42, 1.61, and 1.23. a. The two composites were formed from person-descriptive adjectives included in the data set; see the text for further detail.

predictor of Time 2 depression (β = .22; p < .001). In addition, low extraversion and low agreeableness were each incrementally predictive of change in depression symptoms (βs = −.09, and −.07, respectively, p < .05). In total, these predictors accounted for 36% of the variance in Time 2 depression. We next conducted five hierarchical multiple regressions predicting depression symptoms at Time 2 from factor scores of the personality facets. Specifically, we entered baseline depressive symptoms and the Depression facet in the first block in order to control for state and trait individual differences in symptom levels. In the second block, we entered the factor scores for the facets, conducting separate regressions for each domain. Results are shown in Table 9. As expected, baseline depression symptoms and trait depression made a large, significant contribution in the prediction of Time 2 depression symptoms across domains (R2 = .39 to .42). Several facets were significant unique predictors of Time 2 depression, after accounting for baseline and trait depression, although the effect sizes were small (change in R2 = .01 to .02 across

domains). In the neuroticism domain, Anger contributed additional incremental variance (β = .16, p < .01). Among the extraversion facets, only low Positive Emotionality was a significant predictor of change in depression symptoms (β = −.13, p < .01). Low levels of Conventionality were uniquely associated with change in depression symptoms among the conscientiousness facets (β = −.10, p < .05), whereas none of the agreeableness facets were incrementally predictive of Time 2 depression. Last, only low Culture within the openness to experiences domain was a significant predictor of depression (β = −.11, p < .05).

Discussion Development of a Consensual Facet Structure The current study examined facet-level traits as predictors of subsequent depression symptoms in a large community sample, after accounting for baseline and trait depression. This study was the first to identify a consensual faceted structure for


9

Naragon-Gainey and Watson Table 4. Standardized Loadings From Exploratory Factor Analysis of Facets in the Conscientiousness Domain.

6FPQ Order 16PF Perfectionism NEO PI-R Orderliness JPI-R Organization MPQ Achievement 6FPQ Endurance 6FPQ Achievement NEO PI-R Achievement Striving NEO PI-R Self-Discipline NEO PI-R Deliberation MPQ Control 6FPQ Deliberation NEO PI-R Competence HPI Not Spontaneous 6FPQ Cognitive Structure NEO PI-R Dutifulness JPI-R Responsibility 16PF Rule-Consciousness HPI Virtuous HPI Moralistic HPI Avoids Trouble

Order

Achievement

Deliberation

Conventionality

.79 .77 .77 .71 .03 .04 −.05 .06 .22 −.02 .27 .16 −.16 .03 .39 .04 −.01 .29 −.12 .17 −.11

.02 .05 .03 .09 .82 .69 .64 .64 .45 .08 −.18 .04 .38 −.19 –−.13 .28 .04 –.09 −.02 .17 −.15

.01 −.04 .10 .13 −.19 −.07 −.06 .14 .32 .69 .68 .64 .58 .44 .41 .36 –.06 .02 .05 −.12 .20

−.03 .08 −.06 −.05 −.02 −.15 .14 −.03 −.01 .06 −.01 −.01 .00 −.01 −.02 .27 .64 .54 .47 .43 .40

Note. N = 493. Loadings greater than or equal to |.30| are shown in bold. NEO PI-R = NEO Personality Inventory–Revised; MPQ = Multidimensional Personality Questionnaire; 16PF = Sixteen Personality Factor Questionnaire; 6FPQ = Six-Factor Personality Questionnaire; JPI-R = Jackson Personality Inventory–Revised; HPI = Hogan Personality Inventory. The first four eigenvalues were 6.49, 1.77, 1.15, and 0.77.

Table 5. Standardized Loadings From Exploratory Factor Analysis of Facets in the Agreeableness Domain.

6FPQ Even-Tempered 6FPQ Good-Natured NEO PI-R Compliance HPI No Hostility MPQ Aggression 6FPQ Dominance NEO PI-R Modesty 16PF Dominance NEO PI-R Straightforwardness NEO PI-R Altruism NEO PI-R Trust HPI Caring NEO PI-R Tender-Mindedness HPI Easy to Live With

Good-Natured

Modesty

Empathy

.82 .63 .57 .52 −.51 −.03 −.12 −.33 .04 .04 .23 −.04 –.12 .39

−.05 −.04 .25 .05 −.13 −.66 .58 −.54 .46 .14 −.14 −.08 .32 −.14

−.10 −.02 .16 .17 −.14 .20 .16 .19 .28 .65 .52 .48 .43 .36

Note. N = 497. Loadings greater than or equal to |.30| are shown in bold. NEO PI-R = NEO Personality Inventory–Revised; MPQ = Multidimensional Personality Questionnaire; 16PF = Sixteen Personality Factor Questionnaire; 6FPQ = Six-Factor Personality Questionnaire; HPI = Hogan Personality Inventory. The first three eigenvalues were 4.08, 1.03, and 0.74.

each of the Big Five domains, drawing from six omnibus inventories to clarify the contents of the lower level of the personality hierarchy. The development of a consensual faceted model of the Big Five (note that we do not claim this to be “the” consensual model; rather, it represents one articulation

based on a specific sample and set of measures) informs personality assessment in several ways. For researchers interested in a faceted model that is broadly representative of shared content across numerous personality inventories, one approach may be to select one or more of the best markers from each of


10

Assessment

Table 6. Standardized Loadings From Exploratory Factor Analysis of Facets in the Openness Domain.

6FPQ Understanding JPI-R Breadth of Interest 6FPQ Breadth of Interest NEO PI-R Aesthetics HPI Culture HPI Reading JPI-R Complexity NEO PI-R Ideas JPI-R Innovation HPI Experience Seeking HPI Generates Ideas 6FPQ Change NEO PI-R Action 16PF Abstractedness 16PF Openness to Change NEO PI-R Fantasy MPQ Absorption NEO PI-R Feeling MPQ Traditionalism JPI-R Traditional Values NEO PI-R Values

Culture

Creativity

Liberalism

.77 .74 .73 .70 .70 .50 .49 .46 .01 −.01 –.10 .04 .15 −.03 .23 .07 .37 .27 −.06 .07 .02

−.08 .14 .11 .12 −.07 −.11 .03 .26 .77 .70 .65 .56 .52 .49 .43 .42 .38 .34 .05 −.06 .04

.10 −.06 .00 −.03 .02 .10 .35 .06 .02 −.05 .05 .06 .06 .19 .24 .23 −.18 –.15 −.87 −.86 .71

Note. N = 492. Loadings greater than or equal to |.30| are shown in bold. NEO PI-R = NEO Personality Inventory–Revised; MPQ = Multidimensional Personality Questionnaire; 16PF = Sixteen Personality Factor Questionnaire; 6FPQ = Six-Factor Personality Questionnaire; JPI-R = Jackson Personality Inventory–Revised; HPI = Hogan Personality Inventory. The first three eigenvalues were 8.00, 1.36, and 1.04.

the factors found in the current study. In this regard, it is noteworthy that our analyses indicate that no single personality inventory contains content from all of the identified consensual facets. For example, even though our analytic design “prioritized” the NEO PI-R to some extent, no NEO PI-R scales had a primary loading on Conventionality (within conscientiousness). All the other inventories provided much more limited coverage, as each failed to provide strong, clear markers of several facets. Thus, from an assessment perspective, the implications of our data are striking: Researchers who are interested in measuring facet-level personality in a truly comprehensive manner need to draw scales from more than one currently available instrument. Conversely, our results also highlight scale content that is relatively unique to a particular instrument (e.g., NEO PI-R Impulsiveness, Dutifulness, and Feelings load weakly on the facet factors in the current study), suggesting that these specific scales would not be well covered by other omnibus inventories and should be added if they are of interest in a given study. Furthermore, our factor analytic results—which clarify how specific trait scales are associated with one another—may prove useful to researchers who are trying to synthesize results of studies that used different inventories of related constructs (e.g., if two scales are markers of the same facet factor, this provides some support for their comparability).

Prospective Associations Between Personality and Depression This faceted model was then used to clarify and extend our understanding of the associations between personality and depression. At the domain level, baseline neuroticism was strongly predictive of change in depression, whereas extraversion and agreeableness were more weakly associated. However, an examination at the facet level revealed a more complex picture: single facets from each of the neuroticism, extraversion, conscientiousness, and openness domains were predictive of Time 2 depression. These results should be interpreted in light of several methodological strengths of the current study. One common criticism of the larger depression-personality literature is that associations may be partly or largely because of overlapping content (Ormel, Rosmalen, et al., 2004) and mood-state distortion (NaragonGainey et al., 2013); by controlling for baseline depression and for the Depression facet (or “trait depression”), the impact of these issues was reduced in the current study, which allowed us to identify robust predictors. Furthermore, our multivariate approach allows us to draw clearer conclusions about the components that uniquely “drive” associations between personality and depression, above and beyond variance shared among facets from the same domain. Although it is important to acknowledge that effect sizes


11

Naragon-Gainey and Watson Table 7. Correlations Among Big Five Facets and Depression Symptoms. 1 1. Baseline depression 2. Time 2 depression .53 3. Anxiety .48 4. Anger .43 5. Depression (trait) .59 6. Sociability −.11 7. Ascendance −.17 8. Positive Emotionality −.32 9. Excitement Seeking −.06 10. Order −.11 11. Achievement −.09 12. Deliberation −.24 13. Conventionality −.18 14. Good-Natured −.15 15. Modesty .11 16. Empathy −.15 17. Culture .01 18. Creativity .04 19. Liberalism .00

2

3

.45 .46 .56 −.12 −.17 −.39 −.02 −.13 −.18 −.28 −.26 −.22 .07 −.23 −.05 .02 .04

.66 .73 .01 −.38 −.36 −.02 .02 −.17 −.21 −.10 −.18 .28 −.14 −.12 −.20 −.14

4

.62 −.10 −.04 −.33 .14 .00 −.09 −.32 −.35 −.66 −.18 −.43 −.05 .04 −.02

5

6

7

8

9

10

11

12

13

14

15

−.17 −.35 .44 −.51 .54 .47 .00 .41 .41 .27 −.18 .01 .01 .07 −.14 −.29 .02 .33 .34 .00 .49 −.43 −.04 −.05 .08 −.32 .68 .39 −.30 .22 −.05 .26 −.27 .39 .31 .51 −.20 .16 −.22 .20 −.23 −.07 −.07 .13 .40 .24 −.02 −.62 −.12 −.37 −.05 .26 .01 .31 .62 −.28 .50 .10 .56 −.06 .06 −.20 .18 .49 .56 .38 −.10 .11 .29 .25 .20 −.18 .18 −.14 −.08 .07 −.06 −.09 .08 .46 .31 .38 −.26 .28 −.34 −.27 −.14 −.29 .00 −.05 .22 .03 .20 −.43 −.09 −.32 −.54 −.06 .18

16

.16 .08 .10

17

18

.70 .54

.55

N = 402 to 497. Correlations greater than or equal to |.35| are shown in bold. Correlations greater than or equal to |.13| are significant at p < .01.

Table 8. Hierarchical Multiple Regressions Predicting Time 2 Depression From Big Five Domains, After Accounting for Baseline Depression.

Block 1 Baseline depression Block 2 Neuroticism Extraversion Conscientiousness Agreeableness Openness to Experience

B

SE

β

.30

.04

.32***

.12 –.05 –.04 –.05 .02

.02 .02 .03 .03 .02

.22*** –.09* –.05 –.07* .04

ΔR2 .29 .06

Note. N = 598. Parameter estimates shown are the final estimates from the hierarchical regressions. *p < .05. **p < .01. ***p < .001.

were small, this fine-grained level of analysis and stringent control of content overlap provides a more nuanced view of how personality is related to depression and which associations previously reported in the literature likely are robust. Neuroticism. Structural analyses revealed three facets within neuroticism: depression, anger, and anxiety. These correspond to the primary negative emotions that have been the focus of trait affectivity research for decades (e.g., Watson & Clark, 1999). It is noteworthy that scales assessing stress reactivity (e.g., MPQ Stress Reaction, NEO PI-R Vulnerability) tended to split across the three neuroticism facets, although they loaded most strongly on anxiety, suggesting

that individuals are likely to experience any of these negative emotions in reaction to stressful circumstances. Holding the depression facet constant, we found that anger (including related content such as irritability, losing one’s temper, and annoyance) made a unique, positive contribution in predicting change in depression symptoms, whereas anxiety did not. This finding suggests that the domain-level association between neuroticism and depression may not simply be due to shared content between the two constructs, as none of the anger items overlapped with items from the depression measure, highlighting the importance of examining facets of neuroticism in relation to depression (see Klein et al., 2011).


12

Assessment

Table 9. Hierarchical Multiple Regressions Predicting Time 2 Depression From Big Five Facets, After Accounting for Baseline Depression and Trait Depression. SE

β

.30 3.89

.04 .78

.32*** .30***

−.40 2.21

.74 .66

B

ΔR2

Neuroticism (N = 463) Block 1 Baseline depression Trait depression Block 2 Anxiety Anger

−.03 .16**

.39 .02

Extraversion (N = 398) Block 1 Baseline depression Trait depression Block 2 Positive Emotionality Sociability Ascendance Excitement Seeking

.32 4.04

.04 .69

−1.85 −.09 .85 .26

.75 .59 .60 .58

.37*** .32***

−.13** .01 .07 .02

.42 .02

Conscientiousness (N = 446) Block 1 Baseline depression Trait depression Block 2 Order Achievement Deliberation Conventionality

.32 3.22

.04 .66

.40 –.39 −.08 −1.49

.70 .58 .76 .62

.35*** .33***

.03 −.03 −.01 −.10*

.40 .01

Agreeableness (N = 445) Block 1 Baseline depression Trait depression Block 2 Good-Natured Modesty Empathy

.32 4.33

.04 .66

−1.37 −.19 −.51

.76 .77 .66

.35*** .34***

−.10 −.01 −.03

.40 .01

Openness to Experience (N = 446) Block 1 Baseline depression Trait depression Block 2 Culture Creativity Liberalism

.32 4.72

.04 .59

−1.40 1.14 .75

.68 .70 .58

Note. Parameter estimates shown are the final estimates from the hierarchical regressions. *p < .05. **p < .01. ***p < .001.


.35*** .36***

−.11* .09 .06

.40 .01

13

Naragon-Gainey and Watson Anger was uniquely predictive of depression symptoms in one facet level multivariate study in a nonclinical sample (Chioqueta & Stiles, 2005), but not in a clinical sample of acutely depressed participants (Bagby et al., 2008) or a nonclinical sample of adolescents oversampled for high rates of neuroticism (Uliaszek et al., 2009). There is evidence that individuals with a history of double depression (i.e., major depression and dysthymic disorder) have higher levels of trait anger than do those with a history of MDD only, even when both groups are in remission (Harkness et al., 2002). Perhaps unselected community samples tend to include more chronic, low levels of depressive symptoms that are associated with higher levels of anger, as opposed to the acute major depression that is more likely to lead one to seek clinical treatment. High levels of anger may contribute to difficulties in interpersonal relationships that reduce social support or trigger episodes of depression (Harkness et al., 2002). Extraversion. In the extraversion domain, four facets were uncovered that are very similar in content to those identified by Naragon-Gainey et al. (2009): sociability, ascendance, positive emotionality, and excitement seeking. At the domain level, extraversion was weakly predictive of change in depression, but facet-level analyses revealed that only the low positive emotionality facet was predictive of subsequent depression. This is consistent with a number of past studies suggesting that depression’s association with extraversion is solely via the positive emotionality facet (e.g., Durbin, Klein, Hayden, Buckley, & Moerk, 2005; NaragonGainey et al., 2009). Those with low levels of positive emotionality may be less likely to engage with others and pursue interests, leading to withdrawal and a narrowing of activities that depletes coping resources (see the broaden-andbuild theory; Fredrickson, 2000). Note, moreover, that these facet-level results help to explain why domain-level associations for extraversion are weaker and more variable across studies. As noted earlier, extraversion measures vary considerably as to their emphasis on the positive emotionality facet. Indeed, of the six omnibus personality inventories included in the present study, only two of them—the NEO PI-R (Positive Emotions) and the MPQ (Well-being)—actually contain clear markers of this facet. Our results therefore underscore the importance of explicitly modeling positive emotional content when examining associations between personality and depression. Given that many popular inventories lack this content, they would need to be supplemented in some way. Conscientiousness. The conscientiousness domain consisted of four facets in this study—order, achievement, deliberation, and conventionality. In this same sample, Roberts et al. (2005) found a six-factor structure for conscientiousness, with three of the factors identical or very similar to ours:

industriousness (corresponding to our achievement facet), order, and self-control (corresponding to our deliberation facet). They identified the remaining three facets (i.e., responsibility, traditionalism, and virtue) as being less central to conscientiousness and sharing variance with other domains; our conventionalism factor contains elements of each of these three facets. Discrepancies between our findings and those of Roberts et al. (2005) are likely due to three differences in our approaches: (a) We examined scales within the larger context of Big Five and dropped a scale if it did not appear to be a specific marker of a single domain, (b) The inventories included in our analyses and in Roberts et al. were not identical, as described earlier, and (c) Some of the scales that Roberts et al. placed in their traditionalism facet within consciousness were placed in the liberalism facet of openness in our analyses. In the current study, low levels of conscientiousness were not a significant predictor of change in depression at the domain level, despite moderate bivariate correlations with each of the depression symptom measures (r = −.21 and −.26) that are consistent with past studies (Kotov et al., 2010). Higher order structural analyses of personality may shed light on this issue, as they have shown that the Big Five domains are not orthogonal, but rather combine to form the “Big Two”: alpha consists of high neuroticism, low agreeableness, and low conscientiousness, and beta consists of high extraversion and openness. Furthermore, the Big Two are themselves correlated (Digman, 1997; Markon et al., 2005). Thus, our results suggest that much of the observed bivariate association between depression and conscientiousness is due to shared variance between conscientiousness and neuroticism (r = −.47 in the current study), and perhaps the depression facet of neuroticism in particular. This hypothesis is supported by post hoc tests at the facet level, as three of the four conscientiousness facets were significant predictors of depression when neuroticism was not held constant, but only one facet (conventionalism) remained predictive after holding scores on the depression facet constant. Low scores on the conventionalism facet describe a tendency to break rules, disrespect authority, and fail to control behavior. The above tendencies are likely to cause problems in work, relationships, and daily activities, perhaps contributing to stressors (e.g., legal problems, breakups, poor work performance) that elevate risk for depression (Klein et al., 2011). Agreeableness. In domain-level analyses, agreeableness was a unique predictor of change in depression symptoms, despite nonsignificant bivariate correlations (rs = .02) that are consistent with those reported by Kotov et al. (2010). This result appears to be a suppressor effect, in which removing shared variance among predictors increases the association between one predictor and the outcome (e.g., Watson, Clark, Chimielewski, & Kotov, 2013); this finding


14

Assessment

should be replicated to determine whether it is sample-specific or generalizable, particularly since none of the agreeableness facets were uniquely predictive of depression. Openness. Last, although openness to experience was not a significant predictor of depression at the domain level, facet analyses revealed that low levels of culture (e.g., appreciation of arts and intellectual pursuits) was associated with greater depression symptoms beyond baseline and trait depression. Some previous faceted examinations of the openness domain had found associations between NEO PI-R Actions and depression (e.g., Chioqueta & Stiles, 2005), but in the current study, this scale loaded on the creativity facet (e.g., imaginative, often daydreams), which was not related to depression. As a result of the small effect size and the absence of significant bivariate correlations, these results should be interpreted with caution unless replicated. Explanatory Models. There are numerous explanatory models for how personality and depression are related (see Klein et al., 2011 for a review). Given the design of the current study, our results are most consistent with models that view personality and depression as having similar causal sources, or that personality is a risk factor for the subsequent development of depression. Specifically, it may be the case that these facets are picking up on early, low-level symptoms that later develop into depression (precursor model), that personality is distinct from depression but acts as a vulnerability in combination with other risk factors (predisposition model), or that personality and depression are continuous phenomena with shared etiologies (common cause or spectrum model). Although we cannot differentiate among these models in the current study, it is important to note that (a) personality was assessed 3 years or more before Time 2 depression and that (b) our analyses controlled for baseline and trait depression, reducing the likelihood that mood-state distortion or content overlap accounts for these findings. Given that personality is malleable, rates of subsequent depression could be reduced if individuals with problematic levels of these traits (and perhaps with other environmental or familial risk factors) were identified early and offered interventions (e.g., Cuijpers et al., 2008). Furthermore, for those who have already developed a depressive disorder, specific personality trait levels can inform case conceptualization, treatment selection, and treatment response (Zinbarg et al., 2008).

Other Potential Applications of the Consensual Structure The current study illustrates one possible application of a faceted Big Five model, but such a model may be fruitfully

applied to other behaviors and disorders where a narrowbandwidth approach is desired (see Paunonen & Ashton, 2001). Turning to other forms of psychopathology, the Kotov et al. (2010) meta-analysis found striking similarities in domain-level associations across numerous different disorders. However, they acknowledged that this higher order analysis may miss finer-grained distinctions. Providing support for this hypothesis, Samuel and Widiger (2008) conducted a facet-level meta-analysis of associations between personality disorder and five-factor model facets, highlighting numerous examples where facets, but not domains, distinguished between disorders. Finally, a facetlevel meta-analysis examining personality and substanceuse disorders (Ruiz, Pincus, & Schinka, 2008) found specific associations between a single facet within agreeableness (i.e., trust), as well as opposing associations with two facets of extraversion (i.e., a positive association with warmth and a negative association with excitement seeking) that canceled each other out at the domain level. Overall, it is clear that much work remains to be done in understanding finer-grained distinctions among psychological disorders; the comprehensive faceted model reported here provides a framework that would facilitate comparisons across studies by incorporating numerous instruments and by aiding researchers in systematically assessing Big Five facets.

Limitations and Conclusion There are several limitations that should be considered when interpreting these results. First, all data were selfreported via questionnaires. Although the continuous measurement of depression is a strength in that we include the full spectrum of symptoms, clinical interviews are necessary to make conclusive determinations about diagnoses and disorder onset. Second, the personality measures were collected at different times over the course of several years, which may have influenced our identified facet structure. However, there is evidence that personality as assessed by many of these measures is quite stable over a period of years (e.g., Costa & McCrae, 1992); moreover, the facets that emerged in our analyses are broadly congruent with those found in previous analyses (e.g., Naragon-Gainey et al., 2009; Roberts et al., 2005). Third, the design of this study did not allow us to tease apart different explanatory models for the association between personality and depression, as described above, or to consider possible moderating or mediating factors such as stressful life events. In particular, having three or more assessment points of depressive symptoms would provide greater information about the trajectory of symptoms and their association with traits. Last, all participants were homeowners, as this was the method of recruitment; lower income individuals therefore


15

Naragon-Gainey and Watson were underrepresented in this sample, potentially limiting the generalizability of the findings. Notwithstanding these limitations, the current study identified a “consensual” model of Big Five facets, drawn from multiple popular personality inventories. These facets were able to account prospectively for variance in depressive symptoms several years later, above and beyond baseline depression symptoms and trait depression, in a large community sample. Given the few facet level studies that have been conducted in this area, these results inform our understanding of the specific components that drive the associations between the Big Five and depression. Acknowledgments We are grateful to Lewis Goldberg for his generosity in allowing us to analyze data from the Eugene–Springfield Community Sample as well as to Maureen Barker for helping us obtain the data.

Declaration of Conflicting Interests The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding The authors received no financial support for the research, authorship, and/or publication of this article.

Notes 1. As described in the “Participants and Procedures” and “Measures” sections, personality was assessed at multiple time points; note, however, that all personality scales were administered at least 3 years prior to the depression outcome measure. 2. The association between depression (as assessed by the CESD) and personality traits have been analyzed in this data set in two other published studies. Grucza and Goldberg (2007) examined several of the strongest correlations of the 1997 administration of the CES-D (among other clinical outcomes) and individual facet-level personality scales administered prior to 1997, whereas Langan-Fox and Canty (2010) looked at the associations among affiliation motive congruence, perfectionism, and depression. A third study (LanganFox, Sankey, & Canty, 2009) examined self-directedness, locus of control, and self-disclosure as moderators between depression (assessed by the Depression facet of the NEOPI-R) and achievement motives. However, none of these studies looked at depression within a comprehensive, multiinventory-faceted Big Five model; nor did they account for baseline depression or trait depression in their analyses. 3. We also considered whether the Positive Emotionality facet should be treated as an alternative measure of depression (similar to the Depression facet) because of overlapping content with the depressive symptoms measure. However, only 2 of 24 depression items (“felt happy” and “enjoyed life”) overlapped with content in the Positive Emotionality facet, and

correlations between the depression measure and the Positive Emotionality facet were not strong in magnitude (i.e., −.32 with baseline depression and −.39 with Time 2 depression). Despite this evidence of only minor item overlap, we tested whether our findings were affected by criterion contamination by rerunning all facet-level regression analyses reported in Table 9 with the Positive Emotionality facet included in the first block. The magnitudes of parameter estimates and patterns of our findings did not change substantially after controlling for Positive Emotionality (i.e., any changes in parameter estimates were less than one standard error).

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