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JEAN-PIERRE ROLLAND1* AND FILIP DE FRUYT2. 1STAPS Department, University of Paris 10, 200 avenue de la rй publique, 92201 Nanterre, France.
European Journal of Personality Eur. J. Pers. 17: S101–S121 (2003) Published online in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/per.485

The Validity of FFM Personality Dimensions and Maladaptive Traits to Predict Negative Affects at Work: A Six Month Prospective Study in a Military Sample JEAN-PIERRE ROLLAND1* AND FILIP DE FRUYT2 1

STAPS Department, University of Paris 10, 200 avenue de la re´publique, 92201 Nanterre, France 2 Department of Psychology, Ghent University, H. Dunantlaan 2, B-9000 Ghent, Belgium

Abstract The present work explores what the domain of maladaptive traits has to offer to the industrial and organizational (I/O) field investigating the incremental validity of maladaptive traits from DSM Axis II to predict negative emotions experienced at work, beyond Five-Factor Model dimensions. This study was designed to examine the validity of adaptive and maladaptive traits to predict four negative affects (Anger, Fear, Sadness, and Shame) experienced at work in military personnel. The design was longitudinal, including two measurement moments, i.e. prior to and immediately after returning from a peace mission in a foreign country. The four negative affects were largely stable across a six month interval. FFM dimensions substantially explained negative affects experienced six months later, although the variance accounted for varied strongly across affects. In line with previous research, emotional stability was a consistent negative predictor of negative affects at both measurement moments. Two maladaptive traits derived from DSM Axis II (i.e. Borderline and Avoidant) were consistently related to specific negative affects experienced at work. Finally, maladaptive traits did not predict negative affect variance beyond FFM traits. These results are in line with robust findings suggesting that maladaptive trait patterns could be integrated in the five-factor space, and as a consequence have little or no incremental utility over FFM dimensions. Copyright # 2003 John Wiley & Sons, Ltd. In as much as his feeling and emotions are inherent aspects of himself, he carries them with him, so to speak, into every situation he enters (Fisher and Hana, 1931).

INTRODUCTION ‘Psychology first discovered behaviour, then embraced cognition, and finally in the 1980s, recognized the central importance of affect in human experience’ (Watson & Clark, 1997a, *Correspondence to: J.-P. Rolland, STAPS Department, University of Paris 10, 200 avenue de la re´publique, 92201 Nanterre, France. E-mail: [email protected]

Copyright # 2003 John Wiley & Sons, Ltd.

Received 30 April 2002 Accepted 15 October 2002

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p. 267). This observation is also true for industrial and organizational (I/O) psychology, but the interest in emotions in the workplace has increased rapidly during the last decade (Brief & Weiss, 2002; Fisher, & Ashkanasy, 2000; Weiss, 2002; Weiss & Brief, 2001). Understanding the antecedents, characteristics, and consequences of emotions in the workplace and the role of personality dimensions in emotions at work have become major themes on the current research and professional agenda of I/O psychologists. There are many examples in the recent I/O literature illustrating this shift of attention and providing evidence of the importance of emotions. It has been recently demonstrated that negative emotions, such as anger and anxiety, not only have important consequences for health (Suinn, 2001, Williams et al., 2001), but also affect work behaviour. Caspi, Elder, and Bem (1987), Douglas and Martinko (2001), Lee and Allen (2002), and Fox and Spector (1999) for example showed that anger (or hostility) is antecedent of counterproductive and aggressive behaviour at work. However, the role of ‘affect related’ personality traits, such as Negative Affectivity or Angry temperament, in emotions experienced at work, and their potential consequences on counterproductive behaviours, remains unclear (Douglas & Martinko, 2001; Penney & Spector, 2002; Skarlicki et al., 1999). Other outcomes, such as subjective well-being and work stress, have been studied intensively and related to emotions. Negative and positive emotions are now considered as core components and/or powerful indicators of subjective well-being (Diener, Suh, Lucas, & Smith, 1999); conversely, emotions and stress are considered as interdependent and inseparable (Lazarus & Cohen-Charash, 2001). Understanding the role of emotions is therefore central to understand well-being and stress at work. Finally, Weiss and Cropanzano (1996) discussed the impact of affective states and traits at work, suggesting that mood and emotions have important consequences for workplace behaviour. In this respect, Brief and Weiss (2002) reviewed consequences of affects on employee performance ratings, creative problem solving, helping behaviour, general performance, and negotiating and withdrawal behaviours. The previous examples suggest that affective dispositions or affective styles, as well as affects and emotions, play an important role in explaining a range of major I/O outcomes such as job performance, counterproductive behaviour, stress, and subjective well-being. To enhance our understanding of these affective processes in applied settings, research on the antecedents of emotional states at work is necessary. Therefore, the present study aims at examining the relationships between dispositional antecedents such as affective traits, and adaptive and maladaptive personality dimensions on the one hand and emotions experienced at work on the other hand. Before introducing the empirical study, we will first define affects and emotions, describe adaptive and maladaptive trait models, and discuss their interrelationships. Emotions, affects, states, and traits Affect researchers define emotions as ‘episodic, relatively short-term, biologically based patterns of perception, experience, physiology, action, and communication that occur in response to specific physical and social challenges and opportunities’ (Keltner & Gross, 1999). Emotions are conceptually distinguished from moods: moods are longer lasting, and more generalized affective states. According to Russell and Feldman-Barrett (1999, p. 806), ‘Core affect can be seen as the elemental feelings included within prototypical episodes ( . . . ) We define mood as prolonged core affect without an object or with a quasiobject’. ‘Core affect is assessed by asking how one is feeling right now. When extended Copyright # 2003 John Wiley & Sons, Ltd.

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over moderate lengths of time, core affect becomes a mood and is assessed by asking how one generally felt during that period’ (1999, p. 815). Another distinction is between state emotion and trait emotion. Affective traits or styles are defined as the general tendency to experience a given emotion state or mood over time and across situations (Davidson, 2000; Gray & Watson, 2001). According to Lerner and Keltner (2001, p. 146), ‘Trait emotions are enduring tendencies (dispositions) to experience particular emotions. State emotions are momentary experiences of an emotion’. Watson et al. (1999, p. 829) suggest that ‘Trait measures of Negative Affectivity reflect stable individual differences in the tendency to experience aversive emotional states, such as fear, guilt, sadness, and anger, whereas Positive Affectivity scales assess characteristic differences in the experience of positive states such as enthusiasm, confidence, and cheerfulness’. These definitions clearly provide a bridge to go from ‘state emotion’ to ‘trait emotion’, from ‘trait emotion’ to ‘trait affect’ (or affective style), and from ‘trait affect’ to ‘personality traits’ as suggested by Gray and Watson (2001). Affects and adaptive personality dimensions Affects or emotions were most frequently associated with two major dimensions of personality, i.e. Extraversion and Neuroticism. These two dimensions are core domains of the so-called Five-Factor Model (FFM) of personality, including three additional dimensions usually labelled as Agreeableness, Conscientiousness, and Openness to Experience. They are considered as major dimensions underlying adaptive personality functioning applicable from childhood to late adulthood (De Fruyt, Mervielde, & Van Leeuwen, 2002). Extraversion (or Positive Affectivity) and Neuroticism (or Negative Affectivity) are considered as basic temperament traits that influence behaviours and feelings (Eysenck, 1992; Gray & Watson, 2001; Tellegen, 1985). Clark (2000, p. 173) defines these factors as follows: ‘Negative Affect (NA) is a general dimension of subjective distress, encompassing a number of specific emotional states, including fear, sadness, anger, guilt, contempt and disgust ( . . . ). Positive Affect (PA), by contrast, reflects the co-occurrence among a wide variety of positive mood states, including joy, interest, attentiveness, excitement, enthusiasm, and pride’. Numerous studies have consistently shown that these dimensions are related to affects. Neuroticism is related to ‘unpleasant’ affects and Extraversion to ‘pleasant’ affects (Allik & Realo, 1997; Canli et al., 2001; Costa & McCrae, 1980; Izard, Libero, Putnam, & Haynes, 1993; Lucas & Fujita, 2000; McCrae & Costa, 1991; Watson & Clark, 1992, 1997b; Watson et al., 1999; Yik & Russell, 2001). In addition to these correlational results, Larsen and Ketelaar (1989, 1991) and Rusting and Larsen (1997) have demonstrated, in experimental studies, that these traits actually represent predispositions to experience positive versus negative affects. A smaller number of studies have examined the relations between the remaining dimensions of the FFM and emotions. Positive correlations were found between Openness to Experience and positive affective states (Costa & McCrae, 1984; McCrae & Costa, 1991; Watson & Clark, 1992); whereas Agreeableness and Conscientiousness were found to correlate positively with positive affective states, but negatively with negative affective states (McCrae & Costa, 1991; Watson & Clark, 1992). Moskowitz and Cote´ (1995) and Suls, Martin, and David (1998) further found that people high in agreeableness reported more unpleasant affect and distress when they engaged in quarrelsome behaviours or interpersonal conflicts than did less agreeable individuals. Tobin, Graziano, Vanman, & Tassinary (2000) found that Agreeableness is also related to the experience of negative Copyright # 2003 John Wiley & Sons, Ltd.

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emotions and to efforts to control their expression in the presence of others. These results suggest that associations between Agreeableness and affects are more sensitive to situational influences than the relationship between emotions and Neuroticism and Extraversion. In sum, the most consistent patterns of correlations between affects and personality traits have been found for PA and Extraversion, and NA and Neuroticism. After an examination of the relations between the full FFM and momentary affects and moods, Yik and Russell (2001, p. 273), concluded ‘Extraversion and Neuroticism are the two dimensions most predictive of momentary affect. The FFM adds Agreeableness, Conscientiousness and Openness to the E-and-N model. Doing so provides a small but significant improvement in the ability to predict affect’. These associations between NA and Neuroticism and PA and Extraversion are strong, robust, and in line with the claim of Tellegen (1985), who had already suggested in 1985 that Neuroticism and Extraversion should be relabelled as Negative and Positive Emotionality respectively. Studies on the stability of affects and emotions show a substantial long-term stability over periods as long as 7 years (Izard et al., 1993; Watson & Slack, 1993; Watson & Walker, 1996) and cross-situational consistency (Riemann, Angleitner, Borkenau, & Eid, 1998). In this last study, 600 participants rated their current mood in five different situations. Correlations across situations range from 0.43 to 0.66 for positive mood and from 0.47 to 0.63 for negative mood (fearful, irritable, depressed), demonstrating a ‘high and stable consistency of moods across situations’ (Riemann et al., 1998, p. 353). These results suggest that emotions and affects share defining characteristics with personality traits, the latter described as ‘Individual differences in the tendency to behave, think, feel, in certain consistent ways’ (Caspi, 1998, p. 312; emphasis added). Therefore, some authors (Clark, 2000; Tellegen, 1985; Lucas et al., 2000, Lucas & Diener, 2001; Watson & Clark, 1984, 1997b; Watson et al., 1999) (i) consider negative and positive emotionality as the core constitutive elements of Neuroticism and Extraversion respectively, (ii) define personality as the disposition to experience emotional states (Larsen, 1989; Watson & Clark, 1984), or (iii) consider emotions as central components in the structural development of traits (Izard et al., 1993). A study aimed at predicting Negative Affect using personality dimensions may therefore be considered as somewhat tautological. However, there are only a few studies examining the predictive validity of the full FFM and affects (De Fruyt & Denollet, 2002; McCrae & Costa, 1991; Riemann et al., 1998; Yik & Russell, 2001; Watson & Clark, 1992), and there is no research on personality and affect conducted in work settings. The studies by Moskowitz and Cote´ (1995), Suls et al. (1998) and Tobin et al. (2000) have demonstrated that these relationships may be context and situation dependent, suggesting that these relationships may be different in work settings. Affects and maladaptive traits The majority of the research on affect–trait relationships has examined trait constructs describing the normal range of individual differences, as exemplified in the FFM (DeNeve & Cooper, 1998). However, the FFM framework is not the only approach towards personality description. The clinical and psychopathological field has developed a categorical conceptualization, distinguishing three clusters and ten more specific maladaptive trait and symptom patterns, described as ten specific personality disorders. These disorders are described on Axis II of the Diagnostic and statistical manual of mental disorders, DSM-IV (APA, 1994). These two different approaches are no longer considered as mutually exclusive (Lynam & Widiger, 2001; Widiger & Costa, 1994; Widiger, Costa, Copyright # 2003 John Wiley & Sons, Ltd.

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& McCrae, 2002), and a large amount of research has demonstrated consistent relationships between the FFM and the personality disorders described in DSM Axis II (Dyce & O’Connor, 1998; McCrae et al., 2001; O’Connor & Dyce, 2001, 2002; Ostendorf, 2000, 2002; Reynolds & Clark, 2001; Salgado, Lado, & Moscoso, 2002; Trull, Widiger, & Burr, 2001). These findings provide support for a spectrum model conceptualization of personality, where maladaptive individual differences are considered as extreme variants of adaptive traits. Differences between personality disorder psychopathology and the normal range of individual differences are thus more quantitative and gradual than reflecting qualitatively different underlying systems. This is not really surprising, because definitions of personality disorders are indeed largely based on traits. The World Health Organization defines personality disorders as ‘ . . . deeply ingrained and enduring behaviour patterns, manifesting themselves as inflexible responses to a broad range of personal and social situations. They represent either extreme or significant deviations from the way the average individual in a given culture perceives, thinks, feels, and, particularly relates to others. Such behaviour patterns tend to be stable and to encompass multiple domains of behaviour and psychological functioning’ (WHO, 1993; emphasis added). This definition is consistent with those offered in the psychological literature on the normal range of personality differences (see Caspi, 1998) and also includes ‘feelings’, hereby explicitly referring to emotions, affects, and moods. For several reasons moods, affects, and emotions are central components of many personality disorders. First, affectivity is considered part of the defining nature of traits and personality disorders. Second, for several personality disorders the primary defining characteristic is emotion based. Finally, almost all of the Axis II disorders contain at least one specific criterion that is emotion related. In a recent paper, Clark (2000, Table 4, p. 186) suggests various associations between personality disorders and specific affects. Clark explicitly argues that Anger–Irritability is related to Paranoid, Antisocial, and Borderline personality disorders, and that Fear–Anxiety is related to Schizotypal, Avoidant, and Dependent personality disorders. However, since no study has empirically tested relations between affects and personality disorders, these associations—though theoretically consistent—remain speculative. Moreover no study has tested these hypotheses in a work context. The growing evidence for a spectrum model operationalization of personality should direct the attention of I/O psychologists to both adaptive and maladaptive forms of personality functioning. Hogan and Hogan (2001) were among the first to draw the attention of I/O researchers and professionals to this maladaptive range of individual differences. Hogan and colleagues demonstrated that maladaptive traits were predictors of poor leadership (Hogan & Hogan, 2001) and Fleming and Holland (2002) showed lower performance ratings. It thus appears that DSM Axis II constructs, and the maladaptive personality field in general, have potential and may introduce a new perspective in the study of personality in I/O psychology. However, the previous pioneering studies only included maladaptive traits. Although there is evidence that adaptive trait models such as the FFM and maladaptive traits show substantial and considerable relationships (O’Connor & Dyce, 1998, 2001, 2002; Ostendorf, 2000, 2002; Salgado, Lado, & Moscoso, 2002), and both are related to (specific) emotions, the question remains of whether adaptive and maladaptive traits show incremental validity in predicting emotions. Provided our interest in emotions at work, the question is whether it is useful to assess adaptive and maladaptive traits to predict emotions in work settings. Copyright # 2003 John Wiley & Sons, Ltd.

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Objective The objective of this study is to examine the incremental validity of affects, FFM personality dimensions, and maladaptive DSM Axis II traits to predict future negative affects experienced at work, including Anger, Fear, Sadness, and Shame. These relationships will be examined in a prospective design, including two measurement moments with a sixmonth interval. Affects experienced at work, FFM traits, and maladaptive traits were examined at time 1 (T1), whereas affects were assessed a second time after six month at time 2 (T2). The following hypotheses were derived from the literature. (i) Affects experienced at work will demonstrate substantial stability. Given the longterm stability of affects, we hypothesize that negative affects experienced at work at T2 will show temporal stability over a six-month period and be best predicted by the same affect assessed at T1. (ii) FFM traits and maladaptive traits will predict negative affects at both T1 and T2. All four negative affects experienced at work (Anger, Fear, Shame, and Sadness) will be best predicted by Emotional Stability at both T1 and T2 (Watson et al., 1999). Hypotheses regarding the maladaptive trait–affect relationship were derived from Clark (2000) and Lynam and Widiger (2001). Lynam and Widiger asked experts to rate the prototypic case for each personality disorder by using all 30 facets of the NEO-PI-R. The NEO PI-R (Costa & McCrae, 1992) assesses the lower order traits of Anxiety, Angry-Hostility, and Depression, and we relied upon these trait disorder relationships to derive hypotheses for the ‘Fear–Anxiety’, ‘Anger–Irritability’, and ‘Sadness’ affects respectively. Based both on Clark’s suggestions (Clark, 2000, p. 186, Table 4), and on experts’ ratings (Lynam & Widiger, 2001, p. 404, Table 1), we hypothesized that Anger would be predicted by Antisocial, Borderline, and Paranoid maladaptive traits, and that Fear (Anxiety) would be predicted by Avoidant, Dependent, and Schizotypal traits. On the basis of the results of Lynam and Widiger (2001) we further hypothesized that Fear–Anxiety would be predicted by Compulsive and Borderline (positive sign), and Antisocial (negative sign) traits, that Anger would be predicted by the Narcissistic trait, and finally that Sadness would be predicted by the Borderline maladaptive trait. (iii) FFM traits will predict affect assessed at T2 over and beyond affect assessed at T1. This hypothesis was based on the claim by Yik and Russell (2001) that Agreeableness, Conscientiousness, and Openness add to the E-and-N- model and provide a small but significant improvement in the ability to predict affect. (iv) Maladaptive traits will prove incrementally valid in predicting affect at time 2 over FFM traits. This hypothesis relies on the idea that the maladaptive trait field explains additional variance in I/O criteria beyond adaptive traits (Hogan & Hogan, 2001). Method Procedure The results presented here are part of a larger project on morale at work of a French military sample enrolled in a six month peace mission abroad. Due to the military character of this operation, we cannot provide much information on sample and conditions. The research design is prospective–longitudinal with a six month interval. FFM dimensions and maladaptive DSM Axis II traits were measured at time 1 (before Copyright # 2003 John Wiley & Sons, Ltd.

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mission). Measures of affects experienced at work were collected both at time 1 and at time 2 (after the soldiers returned from their mission abroad). At time 1, participants were preparing for their peace mission between antagonist national groups in their unit in France. They were asked to describe ‘emotions they had experienced during the last month’, which means during training in their unit in France. The assessment at time 2 was scheduled during the first week after returning from their mission abroad. They were requested to describe ‘emotions they had experienced during the last month’, thus referring to their experiences abroad. Persons were free to participate; anonymity was strictly guaranteed through a personal code and by a written declaration of the researchers. Questionnaires were administered on the work site during work time to groups of 15–25 persons. Participants completed additional questionnaires on cognitive and affective components of morale that are not included in the present analyses. Participants Participants were all French male military. The total sample at time 1 (before mission) included 460 persons (mean age ¼ 26.34; SD ¼ 6.55; range ¼ 18–51) but was reduced to 160 (mean age ¼ 27.45; SD ¼ 7.18; range ¼ 18–51) at time 2. A large number of participants were transferred to other military units immediately after their return, and 300 participants could not be traced at time 2. Moreover, after exclusions for missing data, this sample was reduced to 130 (mean age ¼ 27.99; SD ¼ 7.34; range ¼ 18–51). Questionnaires The three main groups of variables were assessed using different questionnaires. Adaptive traits. FFM dimensions were assessed using a Big Five Adjective self-report list (Rolland, 1993; Rolland & Mogenet, 2001), including 55 adjectives, grouped in five sets of 11 trait terms. Dimensions are Emotional Stability, Introversion, Openness, Agreeableness, and Conscientiousness. Respondents were asked to describe themselves as they usually behave and feel using a six point Likert-type scale with anchors labelled ‘Does not describe me at all’ (scored 1) to ‘Describes me quite well’ (scored 6). Sample items per domain are calm, serene (Emotional Stability), reserved, solitary (Introversion), creative, cultured (Openness), agreeable, accommodating (Agreeableness), and careful, conscientious (Conscientiousness). The factor structure has been replicated using either multiple groups centroid factor analysis or confirmatory analyses. Scales have satisfactory psychometric characteristics with internal consistency coefficients ranging (n ¼ 2079) from 0.69 to 0.80, and test–retest correlations (n ¼ 118) varying between 0.81 and 0.87 for a four week interval (Rolland & Mogenet, 2001). Cross-validation with the NEO-PI-R (Costa & McCrae, 1992) shows substantial correlations between complementary dimensions (n ¼ 447) ranging from 0.54 (Agreeableness) to 0.73 (Conscientiousness) (Rolland, 1998; Rolland & Mogenet, 2001). Maladaptive traits. These were assessed using the Hogan Development Survey (HDS; Hogan & Hogan, 1997). The HDS was explicitly based on the DSM Axis II personality disorder descriptions, but it was not developed for the assessment of all the DSM disorders. The HDS focuses only on the core construct of each disorder from a dimensional perspective (Hogan & Hogan, 2001, p. 41). For example, there are no anxiety items in the Paranoid (Skeptical)1 scale, whereas the DSM-IV considers anxiety as part of the paranoid 1 Hogan and Hogan have derived HDS scales from DSM-IV, but gave their scales different labels (Borderline, Excitable; Paranoid, Skeptical; Avoidant, Cautious etc). In the present paper we used DSM-IV original labels.

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personality disorder (Hogan & Hogan, 2001). An overview of the item selection guidelines can be found in Hogan and Hogan (2001). The survey includes 154 items, scored for 11 scales, each grouping 14 items. Respondents are requested to ‘agree’ or ‘disagree’ with the items. The HDS has been cross-validated with the MMPI personality disorder scales. Correlations (n ¼ 140) range from 0.45 for Antisocial to 0.67 for Borderline (Hogan & Hogan, 2001). Fico, Hogan, and Hogan (2000) report coefficient alphas between 0.50 and 0.70, with an average of 0.64, and test–retest reliabilities (n ¼ 60) over a three month interval ranging from 0.50 to 0.80, with an average of 0.68. There were no mean-level differences between sexes, racial/ethic groups, or younger versus older persons (Hogan & Hogan, 2001). The French adaptation of the HDS uses a four point scale format, ranging from 1 (‘strongly disagree’) to 4 (‘strongly agree’). Its factor structure has been previously examined and confirmed using multiple groups centroid factor analysis. All Cronbach alpha coefficients for the scales are satisfactory, ranging from 0.52 to 0.80 (n ¼ 1304), except for Dependent (0.52). Test–retest reliability (n ¼ 82, interval ¼ 4 weeks) ranges from 0.65 to 0.84 (Rolland, 2000; Hogan, Hogan, & Rolland, 2002). Affects experienced at work. Emotions were assessed using a self-report questionnaire adapted from the questionnaire of Diener, Smith, and Fujita (1995). This inventory assesses six basic affects, i.e. joy, love, anger, fear, sadness, and shame (and surprise).2 Respondents are asked to rate the frequency with which they have experienced each affect during the last month. In the present study, they were asked to rate the frequency of affects experienced at work or as a result of work. The answer format was a seven point scale, ranging from 1 ‘Not experienced at all’ to 7 ‘Experienced this affect several times each day’. The French adaptation includes 31 items (representing separate emotions or affects). Scales used in the present study are Anger (four items), Fear (four items), Sadness (five items), and Shame (six items). Items (translated from French) are anger, disgust, irritation, rage (Anger scale), fear, worry, anxiety, nervousness (Fear scale), depression, despair, loneliness, sadness, sorrow (Sadness scale), and confusion, culpability, embarrassment, humiliation, regret, shame (Shame scale). The factor structure of this inventory (examined with confirmatory analysis through SEM) and the internal consistency of the scales are satisfactory (Diener et al., 1995; Smits, De Boeck, Kuppens, & Van Mechelen, 2002). Analyses Latent affects at time 1 and time 2 were computed using AMOS 4 (Arbuckle, 1999), and temporal stability of affects was examined through correlational analysis. The incremental validity of FFM and maladaptive traits to predict affect over and above each other or beyond affect assessed at time 1 was examined through hierarchical regression analysis. In these analyses, affects are measured variables. Provided the large number of significance tests, predictors were only considered significant when p < 0.01. Results Structural validity and reliability of measures The factor structures of the FFM adjective list and the French adaptation of the HDS have been examined, confirmed, and reported previously (Rolland, 1993, 2000; Rolland, & Mogenet, 2001; Hogan et al., 2002). The structure of the Negative Affects inventory was 2

Positive affects (joy and love at work) and surprise have also been assessed, but are not presented in this paper.

Copyright # 2003 John Wiley & Sons, Ltd.

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evaluated through structural equation modelling analysis (maximum likelihood). Negative Affects are assumed to form a single Negative Affect dimension (Tellegen, Watson, & Clark, 1999; Watson & Tellegen, 1985), but Sadness and Fear–Anxiety may be considered as having different adaptive functions (Carver, 2001; Lazarus & Cohen-Charash, 2001), whereas Fear and Anger may have opposite effects (Lerner & Keltner, 2001). In order to test predictions at the level of specific affects (Anger, Fear, Sadness, and Shame), we decided to test a hierarchical affect model with an NA latent component on top and four specific latent NA facets. Results (for affects at time 2) are presented in Figure 1. MacCallum and Austin (2000, p. 219) strongly recommend using the RMSEA index to examine model fit. Hu and Bentler (1999) suggest that the cut-off value should be approximately 0.06 for RMSEA and 0.95 for the TLI and CFI indices. The indices presented in Figure 1 (RMSEA ¼ 0.06; TLI ¼ 0.94; CFI ¼ 0.954) indicate a good fit for this hierarchical model. Figure 2 shows correlations between specific affects considered as latent variables. Correlations between variables are quite high, ranging from 0.70 to 0.91, suggesting that one should group all emotions in a global Negative valence affect

Figure 1. Confirmatory analysis of Negative Affects Inventory (sample at time 2). Copyright # 2003 John Wiley & Sons, Ltd.

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Figure 2. Correlations between latent variables (sample at time 2).

construct. However, affects with a similar valence (such as Anger, Fear–Anxiety, Sadness, and Shame–Guilt) may be considered as having different adaptive functions (Lazarus & Cohen-Charash, 2001; Martinko, Gundlach, & Douglas, 2002), and research has shown that these affects are not equal and may have different and even opposite effects (Bodenhausen, Sheppard, & Kramer, 1994; Lee & Allen, 2002; Lerner & Keltner, 2001; Raghunatan & Pham, 1999; Tiedens & Linton, 2001). Moreover, personality disorders are hypothesized to show a different correlational pattern with specific affects such as Fear and Anger (Clark, 2000). Consequently, we decided to examine our hypotheses at the level of specific affects. Internal consistency coefficients for the FFM and HDS scales were good, ranging between 0.67 (Agreeableness) and 0.82 (Conscientiousness) for the FFM, and between 0.62 (Antisocial) and 0.76 (Narcissistic) for the HDS scales, with two scales having lower reliabilities (i.e. Dependent (0.43) and Passive–Aggressive (0.56)). Reliabilities for the affect scales were satisfactory at both measurement moments, ranging between 0.78 and 0.80. The intercorrelations among FFM and maladaptive traits and affects are presented in Table 1. Copyright # 2003 John Wiley & Sons, Ltd.

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Copyright # 2003 John Wiley & Sons, Ltd.

0.74 0.23 0.19 0.28 0.06 0.31 0.29 0.06 0.06 0.10 0.06 0.00 0.26 0.08 0.16 0.02

0.67 0.24 0.35 0.27 0.21 0.33 0.03 0.13 0.11 0.18 0.02 0.17 0.08 0.13 0.05

0.82 0.19 0.79 0.48 0.23 0.080.09 0.310.57 0.290.35 0.150.05 0.170.09 0.120.13 0.10 0.00 0.060.18 0.20 0.16 0.51 0.09 0.04 0.03

Intro Agre Cons EmSt

SCZ BDL

AVD

0.21 0.20 0.01 0.44 0.07 0.14 0.37 0.22 0.09 0.39 0.29 0.03 0.19 0.00 0.02 0.28 0.01 0.10 0.32 0.04 0.06 0.30 0.04 0.02

0.36 0.15 0.27 0.20 0.24 0.15 0.13 0.13

0.68 0.64 0.24 0.42 0.05 0.44

DEP PAR

0.71 0.13 0.43 0.53 0.03 0.42 0.04 0.37 0.05 0.41 0.01 0.12 0.02 0.61 0.02

SZT

0.40 0.14 0.29 0.27 0.23 0.15 0.13 0.11

0.56 0.23 0.41 0.04 0.42

PAG

0.13 0.02 0.12 0.16 0.03 0.02 0.07 0.03

0.68 0.69 0.11 0.43

HIS

0.14 0.06 0.07 0.05 0.02 0.02 0.09 0.09

0.76 0.38 0.52

NAR

0.62

ATS

0.07 0.20 0.10 0.02 0.03 0.06 0.20 0.04 0.07 0.05 0.09 0.01 0.17 0.02 0.20 0.00

0.72 0.25

OBC

0.78 0.59 0.54 0.62 0.47 0.42 0.27 0.39

0.78 0.51 0.60 0.40 0.60 0.37 0.47

0.79 0.75 0.33 0.37 0.39 0.47

0.80 0.36 0.42 0.43 0.54

ANG1 FEAR1 SAD1 SHAM1

0.79 0.70 0.62 0.66

0.83 0.65 0.66

0.78 0.78

0.86

ANG2 FEAR2 SAD2 SHAM2

n ¼ 130. r ¼ 0.18, p < 0.05, r ¼ 0.23, p < 0.01, r ¼ 0.28, p < 0.001. Alphas in diagonal. Intro, Introversion; Agre, Agreeableness, Cons, Conscientiousness; EmSt, Emotional Stability; Open, Openness, SCD, Schizoid; BDL, Borderline; AVD, Avoidant; SZT, Schizotypal; DEP, Dependent; PAR, Paranoid; PAG, Passive–Aggressive (DSM-III); HIS, Histrionic; NAR, Narcissistic; OBC, Obsessive–Compulsive; ATS, Antisocial; ANG1, Anger at T1; FEAR1, Fear at T1; SAD1, Sadness at T1; SHAM1, Shame at T1; ANG2, Anger at T2; FEAR2, Fear at T2; SAD2, Sadness at T2; SHAM2, Shame at T2.

0.61 0.45 0.44 0.40 0.33 0.36 0.29 0.30

0.67 0.25 0.65 0.24 0.39 0.76 0.38 0.12 0.45 0.71 0.00 0.30 0.39 0.02 0.12 0.03 0.10 0.27 0.06 0.39 0.54 0.14 0.03 0.44 0.45 0.03 0.32 0.16 0.05 0.18 0.40 0.04 0.09 0.22 0.29 0.02 0.23 0.23 0.20 0.31 0.24 18

Open

Correlations between adaptive traits (FFM), maladaptive traits, and affects

ANG1 0.31 0.21 0.090.51 0.00 0.12 FEAR1 0.06 0.03 0.110.56 0.20 0.04 SAD1 0.10 0.04 0.140.30 0.07 0.17 SHAM1 0.25 0.01 0.280.33 0.11 0.01 ANG2 0.17 0.01 0.020.32 0.04 0.07 FEAR2 0.15 0.12 0.040.45 0.02 0.09 SAD2 0.02 0.02 0.120.26 0.15 0.06 SHAM2 0.15 0.01 0.190.30 0.15 0.03

Intro Agre Cons EmSt Open SCZ BDL AVD SZT DEP PAR PAG HIS NAR OBC ATS

Table 1.

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Figure 3. Correlations between Fear (latent) at time 1 and Fear (latent) at time 2.

Temporal stability of affects The six month temporal stability for the four negative affects has been examined through SEM, computing the true correlations between the four specific latent NA factors at time 1 and time 2. A visual representation for the latent factors for Fear is presented in Figure 3. The true correlations between latent variables are 0.77, 0.67, 0.63, and 0.45 for Fear, Anger, Shame, and Sadness respectively, and are all significant at p < 0.01. Affects were demonstrated to be largely stable, despite the strongly divergent contexts and circumstances at time 1 and time 2. Part of this substantial stability may be attributed to ‘personal dispositions’ such as affective styles (Davidson, 2000) or personality traits (Clark, 2000; Gray & Watson, 2001). Predictive validity of FFM and maladaptive traits In order to examine the predictive validity of FFM and maladaptive traits, emotions (measured variables) assessed at T1 and T2 were regressed on both sets of predictors to examine the internal replicability of the prediction pattern within this study. The regression results and standardized betas for the predictors are presented in Table 2. Anger was negatively predicted at both T1 and T2 by Emotional Stability ( ¼ 0.47 and 0.34 respectively, both at p < 0.0001). The explained variance for the FFM mounted to 0.29 at T1, and decreased to 0.13 at T2. Fear at both T1 and T2 was predicted by Emotional Stability ( ¼ 0.64 and 0.55 respectively) and Agreeableness ( ¼ 0.28 and Copyright # 2003 John Wiley & Sons, Ltd.

Eur. J. Pers. 17: S101–S121 (2003)

std 

p

Anger_T1

Regression results

std 

p

Anger_T2 std 

Fear_T1 p

Predictive validity of FFM on affects at T1 and T2 Intro 0.15 0.13 0.05 Agre 0.04 0.13 0.28 *** Consc 0.01 0.10 0.00 Em. St. 0.47 *** 0.34 *** 0.64 *** Open 0.11 0.05 0.12 R2 0.29 *** 0.13 ** 0.39 *** Predictive validity of maladaptive traits on affects at T1 and T2 SCZ 0.21 * 0.14 0.14 BDL 0.66 *** 0.28 * 0.42 *** AVD 0.02 0.06 0.27 ** SZP 0.14 0.24 * 0.05 DEP 0.03 0.02 0.02 PAR 0.01 0.17 0.08 PAG 0.23 * 0.17 0.10 HIS 0.07 0.00 0.04 NAR 0.07 0.13 0.08 OBC 0.08 0.05 0.09 ATS 0.08 0.09 0.01 R2 0.44 *** 0.18 * 0.30 ***

Table 2.

Copyright # 2003 John Wiley & Sons, Ltd.

0.33 0.42 0.12 0.16 0.05 0.03 0.15 0.07 0.03 0.04 0.07 0.24

0.06 0.32 0.00 0.55 0.02 0.30

Std 

***

** ***

***

***

***

p

Fear_T2

0.01 0.26 0.27 0.08 0.02 0.04 0.19 0.21 0.11 0.13 0.14 0.29

0.00 0.08 0.12 0.31 0.04 0.10

std 

***

* **

*

***

p

Sadness_T1

0.11 0.15 0.28 0.00 0.15 0.00 0.10 0.04 0.04 0.08 0.05 0.17

0.03 0.15 0.06 0.28 0.09 0.10

std 

*

**

*

***

p

Sadness_T2

0.18 0.21 0.34 0.13 0.11 0.13 0.27 0.18 0.06 0.08 0.06 0.32

0.16 0.22 0.25 0.32 0.02 0.22

std 

***

*

***

***

* ** ***

p

Shame_T1

0.08 0.20 0.23 0.06 0.10 0.03 0.07 0.17 0.20 0.06 0.06 0.17

0.09 0.18 0.12 0.29 0.08 0.14

std 

*

*

*

**

*

p

Shame_T2

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0.32 respectively), and the FFM explained 39 and 30% of the variance. Sadness was consistently predicted by Emotional Stability ( ¼ 0.31 at T1, and 0.28 at T2), and the FFM explained 10% of the variance at both measurement moments. Finally, Shame was also predicted at T1 and T2 by Emotional Stability ( ¼ 0.32 and 0.29 respectively), and additionally predicted by Conscientiousness ( ¼ 0.25) at time 1. Explained variances for the FFM were 22 and 14% respectively. Anger was predicted by the Borderline trait pattern at T1 ( ¼ 0.66), but none of the maladaptive traits predicted this affect at T2. The maladaptive trait pattern explained 44% of the variance at T1. Fear was also predicted by the Borderline pattern ( ¼ 0.42 at both T1 and T2), whereas the Avoidant trait pattern additionally explained fear at T1 ( ¼ 0.27) and Schizoid ( ¼ 0.33) predicted fear at T2. Maladaptive traits explained 30 and 24% of the fear variances at T1 and T2 respectively. The Avoidant scale predicted Sadness at T1 ( ¼ 0.27) and T2 ( ¼ 0.28), and the HDS scales explained 29 and 17% of the variance at T1 and T2 respectively. Finally, shame at T1 was predicted by the Avoidant ( ¼ 0.34) trait pattern, but none of the maladaptive traits predicted this affect at T2. The maladaptive trait pattern explained 32% of the variance at T1. Incremental validity of FFM and maladaptive traits over affect Affects at time 2 were regressed over affects assessed at time 1 (step 1), followed by the FFM or maladaptive traits (step 2) to examine the incremental validity of adaptive or maladaptive traits over affects. Affects at Time 1 predicted between 15 (Sadness) and 36% (Fear) of the corresponding affect variances at time 2. Adding the FFM traits only increased the explained variance significantly for Fear (R2 increase from 0.36 to 0.42), with Emotional Stability ( ¼ 0.26) and Agreeableness ( ¼ 0.20) as significant predictors. Similar analyses were conducted for the maladaptive traits, but none of the negative affects was additionally explained by the maladaptive trait patterns. Incremental validity of maladaptive traits over FFM traits Finally, affects at time 2 were regressed on adaptive traits (step 1), followed by maladaptive traits (step 2). The regression results and standardized betas for the predictors are presented in Table 3. The previous analyses have already shown that Emotional Stability was the single significant FFM predictor of affects assessed at time 2, except for Fear, additionally predicted by Agreeableness. Addition of the maladaptive traits in a second step did not lead to a significant increase of the explained variance for any of the four affects, suggesting that maladaptive traits do not tap additional variance in negative affects experienced at work. Discussion This study was designed to examine the validity of adaptive and maladaptive traits to predict affects experienced at work in military personnel adopting a longitudinal perspective including two measurement moments, i.e. prior and immediately after returning from a peace mission in a foreign environment. First, the present findings underscored the temporal stability of affects observed in work settings. In line with previous research (Izard et al., 1993; Watson & Slack, 1993; Watson & Walker, 1996), anger, fear, sadness, and shame were largely stable across a six month interval. Although stability might be considered as a consequence of job and context stability, the circumstances at T1 and T2 cannot be considered as strictly similar and equivalent, and hence stability cannot be interpreted as a simple consequence of situational Copyright # 2003 John Wiley & Sons, Ltd.

Eur. J. Pers. 17: S101–S121 (2003)

Step 1 Intro Agre Consc Em. St. Open Step 2 SCZ BDL AVD SZP DEP PAR PAG HIS NAR OBC ATS R2 2 R change

Table 3.

Copyright # 2003 John Wiley & Sons, Ltd.

0.13

0.13 0.13 0.10 0.34 0.05

std 

**

***

p

0.06 0.05 0.08 0.18 0.00 0.20 0.21 0.04 0.10 0.09 0.12 0.25 0.12

0.14 0.14 0.20 0.26 0.03

std 

Anger_T2

** ns

*

p

0.30

0.06 0.32 0.00 0.55 0.02

std 

***

***

***

p

0.28 0.21 0.06 0.09 0.02 0.08 0.17 0.08 0.00 0.10 0.07 0.39 0.09

0.03 0.29 0.10 0.44 0.00

std 

Fear_T2

*** ns

**

***

***

p

Regression results FFM at step 1, FFM þ maladaptive traits at step 2

0.10

0.03 0.15 0.06 0.28 0.09

std 

*

**

p

0.12 0.11 0.23 0.02 0.18 0.02 0.10 0.04 0.01 0.14 0.06 0.20 0.11

0.08 0.14 0.05 0.19 0.06

std 

Sadness_T2

* ns

*

*

p

0.14

0.09 0.18 0.12 0.29 0.08

std 

**

**

*

p

0.03 0.04 0.19 0.03 0.12 0.05 0.08 0.16 0.15 0.08 0.06 0.21 0.07

0.05 0.16 0.03 0.24 0.04

std 

Shame_T2

* ns

*

*

*

***

p

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stability. The observed stability may therefore be partly attributed to ‘personal dispositions’ such as affective styles (Davidson, 2000) or personality traits (Clark, 2000; Gray & Watson, 2001). Considering the well established links between negative affects and Neuroticism and assuming that the military peace mission abroad could be considered as an important event in the life of the soldiers, the stability of affective traits observed here is in line with the observation by Costa, Herbst, McCrae, and Siegler (2000) that life events have little influence on personality traits. Second, discussing the dispositional basis of affect brings us to the question which trait model might be best suited to predict affect at work, i.e. adaptive or maladaptive traits, or eventually both combined? The results clearly showed Emotional Stability as a consistent negative predictor of all four negative affects at both time 1 and time 2. These associations are well established, have a long history, and are in line with definitions of neuroticism and/or negative affectivity (Clark, 2000). Agreeableness was demonstrated to be a positive predictor of Fear experienced at time 1 and time 2, showing consistency across time for this affect. Previous studies have reported inconsistent findings regarding the Agreeableness-affect relationship. McCrae and Costa (1991) and Watson and Clark (1992) found negative correlations between Agreeableness and Negative Affects, whereas Riemann et al. (1998) found low positive correlation with positive mood, but no association with negative mood. Yik and Russell (2001) found no association between Agreeableness and pleasant affect. However, when interpersonal conflict situations were taken into account, Moskowitz and Cote´ (1995) and Suls, Martin, and David (1998) found positive relations between Agreeableness and negative affects, leading Suls to hypothesize that ‘Agreeableness may be an important moderator of emotional reactivity to a specific class of life stressor, interpersonal conflict’ (Suls, 2000, p. 403). Complementarily, in an experimental study, Tobin and colleagues (2000) found that Agreeableness was related to the experience and regulation of negative emotions during interpersonal situations. When confronted with aversive stimuli (slides of burn victims, battered woman, . . . ), agreeable individuals experienced more negative emotions and made more efforts to control their expression in the presence of others. Similar mechanisms might partly explain the positive relations between Agreeableness and Fear observed here. Conscientiousness negatively predicted Shame, but only at time 1. Neither Introversion, nor Openness to Experience, were predictors of negative affects. The explained variance by the FFM at time 1 was substantial, although it varied strongly across affects, ranging between 10% for Sadness and 39% for Fear. The explained variance by the FFM for affects experienced six months later varied between 10% for Sadness and 30% for Fear. Adaptive traits thus demonstrated long-term predictive validity of negative affects, despite changing circumstances. The variations in explained variance provide support for our decision to consider the four negative affects as discrete negative emotions, rather than opting for a general latent negative affectivity component. Negative emotions experienced at work were also predicted by maladaptive traits. Anger at T1 and Fear at T1 and T2 were predicted by the Borderline pattern, whereas Fear was additionally predicted by the Avoidant at T1 and the Schizoid pattern at T2. The avoidant scale was the only significant predictor of Sadness across time, and of Shame experienced at T1. The explained variance ranged between 29 (Sadness) and 44% (Anger) at time 1 and between 17 (Sadness) and 24% (Fear) six month later. Only two of the maladaptive traits are thus consistently related to negative affects experienced at work, i.e. the Borderline and the Avoidant trait pattern. These observations are in line with studies describing FFM and Axis II personality disorders. Both the borderline and the avoidant Copyright # 2003 John Wiley & Sons, Ltd.

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disorder share substantial variance with the Neuroticism trait, and it is probably the Neurotic partition of the Borderline and Avoidant symptom patterns that accounts for the relationship with negative affect. In a recent meta-analysis, Ostendorf (2002) reports the highest true correlation estimates between Neuroticism and the Borderline (0.65, p < 0.01) and Avoidant (0.62, p < 0.01) disorders. The previous results only partly confirm our hypotheses, with Anger predicted by Borderline and Fear predicted by the Avoidant trait pattern. None of the other hypotheses derived from the work of Clark (2000) and Lynam and Widiger (2001) was confirmed. Examination of the third and fourth objectives showed that the FFM only significantly adds beyond affect assessed at time 1 for predicting Fear. Fear is additionally predicted by Neuroticism and Agreeableness. None of the maladaptive trait patterns demonstrated incremental validity beyond the corresponding affects assessed previously. Finally, maladaptive traits did not predict affect variance beyond FFM traits. These results must be interpreted in the light of robust research findings, indicating that maladaptive patterns described by DSM Axis II may be integrated in the FFM factor space (Dyce & O’Connor, 1998; McCrae et al., 2001; O’Connor & Dyce, 2001, 2002; Ostendorf, 2000, 2002; Reynolds & Clark, 2001; Trull et al., 2001), and as a consequence have little or no incremental predictive utility over FFM dimensions. The present study has a number of limitations that constrain the generalizability of its results. First, this research exclusively relies on self-reports provided by the same informants on both occasions. Consequently, results are subject to same-method and sameinformant biases inflating the correlational pattern. Second, emotions experienced at work were assessed retrospectively, and refer to a one month period. Consequently, the emotions that are measured here reflect more traitlike affects and this produces an artificial increase of the correlations with adaptive and maladaptive traits. Third, The FFM was only considered at the broad domain level. Finer-grained FFM measures, enabling analyses at the facet level, might produce a more precise view of the relations between personality traits and affects. The NEO-PI-R (Costa & McCrae, 1992), for example, includes specific N facets (Angry-Hostility, Anxiousness, Depression) and it would be interesting to use this inventory in future research. Fourth, the maladaptive patterns derived from DSM Axis II have been assessed with the HDS. The HDS has been designed to capture only the core of each personality disorder and consequently does not assess the entire personality disorder. Future research should be encouraged to use other questionnaires or structured interviews that cover more comprehensively all components of personality disorder symptomatology. A fifth limiting factor is the greatly truncated sample at time 2. Finally, the military sample and its mission during the time interval were also rather specific and might limit the generalizability of results. Therefore, replication across different professional samples and circumstances is strongly recommended. In sum, this study demonstrated that negative affects at work are best predicted by previous similar affects. FFM and distinct maladaptive trait patterns also predicted negative affects, although they only marginally added (in the case of the FFM) or did not contribute (in the case of maladaptive traits) to the prediction beyond previously reported affect. However, it would be premature to conclude from the present research that the maladaptive trait domain has nothing or little to offer to the I/O field, on top of the FFM. Hogan and Hogan (2001) and Fleming and Holland (2002) have demonstrated that maladaptive traits predict highly valuated I/O outcomes such as leadership and job performance. The personality disorder and maladaptive trait domain might have additional potential to predict more deviant I/O behaviours, such as counterproductive behaviour. Copyright # 2003 John Wiley & Sons, Ltd.

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