Enhancing emotion perception, a fundamental component of emotional intelligence: Using multiple-group SEM to evaluate a training program

Personality and Individual Differences 95 (2016) 11–19

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Enhancing emotion perception, a fundamental component of emotional intelligence: Using multiple-group SEM to evaluate a training program Sarah Herpertz a, Astrid Schütz a,⁎, John Nezlek b,c a b c

Department of Psychology, University of Bamberg, Germany Department of Psychology, College of William and Mary, United States SWPS University of Social Sciences and Humanities, Faculty in Poznań, Poland

a r t i c l e

i n f o

Article history: Received 26 October 2015 Received in revised form 3 February 2016 Accepted 5 February 2016 Available online xxxx Keywords: Emotional intelligence Ability to perceive emotions in others Emotional intelligence intervention Multiple-group SEM

a b s t r a c t This study evaluated a training program designed to improve the ability to perceive emotions in others, a component of ability-based emotional intelligence (EI). Participants, 105 students of business administration and management, were randomly assigned to a training group or a control group (time management training). The training lasted one day and was followed by 4 weeks of online training. Participants completed the MSCEIT before training and 1 month and 6 months after training. Multiple-group SEM analyses of latent means found that the ability to perceive emotions in others using the faces task of the MSCEIT improved in the training group but did not improve in the control group. Latent moderated SEM analyses found that participants who were high in agreeableness benefitted more from the intervention than those low in agreeableness, and a similar moderating effect was found for conscientiousness. Training effects were stable after 6 months. Training did not change scores on the MSCEIT pictures task. These results suggest that the ability to perceive emotions in others can be improved through training, but that personality traits moderate the effectiveness. Potential applications for such training are discussed. © 2016 Elsevier Ltd. All rights reserved.

1. Introduction “The ability to perceive discrete emotions such as anger, disgust, fear, sadness, and so forth in other people is a fundamental part of social life. Without this ability, people lack empathy for loved ones, make poor social judgments in the boardroom and classroom, and have difficulty avoiding those who mean them harm.” (Lindquist, Gendron, Barrett, & Dickerson, 2014, p. 375). The ability to perceive emotions in others serves various functions including enhanced workplace performance (Elfenbein, Foo, White, Tan, & Aik, 2007), relationship quality, and mental health (Hall, Andrzejewski, & Yopchick, 2009). According to the ability-based model of emotional intelligence (EI; Salovey & Mayer, 1990), emotion perception is “the ability to identify emotions in oneself and others, as well as in other stimuli” (Brackett, Rivers, Shiffman, Lerner, & Salovey, 2006, p. 781). According to this approach, the ability to perceive emotions is the most fundamental ability in the four hierarchically interrelated abilities of perceiving, using, understanding, and regulating emotions in the self and others (Joseph & Newman, 2010). Research in developing emotional abilities has attracted increasing attention, and it has been shown that it is possible to develop emotional ⁎ Corresponding author at: Department of Psychology, University of Bamberg, Markusplatz 3, D-96047 Bamberg, Germany. E-mail address: [email protected] (A. Schütz).

http://dx.doi.org/10.1016/j.paid.2016.02.015 0191-8869/© 2016 Elsevier Ltd. All rights reserved.

abilities through training (e.g., Nelis, Quoidbach, Mikolajczac, & Hansenne, 2009). Although there has been genuine progress in understanding EI interventions, various questions remain. For example, few intervention studies have been conducted as experimental intervention designs with a random assignment to an intervention and an active control group, few have assessed short-term as well as longterm intervention effects, and few have measured emotional abilities with a performance-based test (Schutte, Malouff, & Thorsteinsson, 2013). Furthermore, based on their meta-analytic review, Schutte et al. (2013, p. 62) point out that more “focus on specific aspects of training, such as the impact of training on different aspects of emotional intelligence” is necessary and that very little attention has been paid to the questions about which individuals benefit more than others from EI interventions. Last but not least, with regard to the ability to perceive emotions in others, in their meta-analysis, Blanch-Hartigan, Andrzejewski, and Hill (2012, p. 493) indicated that “research on training effectiveness for emotion recognition in nonclinical adult populations” is warranted. Given the importance of the ability to perceive emotions in others, it is surprising that there are no randomized control-group design interventions aimed to train healthy adults in basic emotion identification (at least to our knowledge). It is also important to note that researchers in this field have used repeated measures analysis of variance (ANOVA), multivariate analysis of variance (MANOVA), or regression analysis to compare observed mean values prior and after interventions (Thompson & Green, 2006).

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Although useful, these methods have some potentially important limitations such as assuming measurement invariance across groups and time (i.e., observed measures are assumed to reflect a latent construct similarly across group and time; Crayen, Geiser, Scheithauer, & Eid, 2011). To address these gaps and to respond to some of the issues mentioned above we evaluated the effects of an intervention program that was intended to improve the ability to perceive emotions in others. We examined the short- and long-term effects of this intervention in a sample of healthy adults using a pre–post–post design, with a control group. The outcome measures were performance-based tests of EI, and differences between the intervention and control groups were examined using multiple-group SEM. Moreover, we examined relationships between individual differences in the impact of the training and individual differences in the Big Five personality traits. Thus, the current study extends previous research in three ways. First, we focus on one specific component of EI. Most studies have focused on interventions that were intended to improve EI overall, and such a lack of specificity makes it difficult to understand exactly how an intervention works. Second, we used an experimental design with random assignment to an intervention or control group to examine short-term and longterm intervention effects. The randomized control group is important for the examination of causal effects of an intervention and for examining effect sizes (Brunwasser, Gillham, & Kim, 2009). Third, to our knowledge the present study is the first to use multiplegroup SEM to examine the effect of an ability-based EI intervention program. The benefits of using multiple-group SEM compared to the OLS techniques that have been used previously include better control over the influence of correlated errors on results and the ability to take measurement invariance into account. 1.1. The ability to perceive emotions in others as personal resource in the workplace The ability to perceive others' emotions helps individuals to identify others' feelings and to get important information about others' intentions and goals (e.g., Van Kleef (2009)). Emotion perception plays a crucial role in daily life and is highly relevant in meeting various requirements at the workplace. For example, in a recent study with 300 teachers, Nizielski, Hallum, Schütz, and Lopes (2013) found that the ability to perceive emotions plays an important role in burnout protection. Further, Herpertz, Nizielski, Hock, and Schütz (2016), in a study of applicants who wanted to be flight attendants, found a positive relationship between applicants' ability to perceive emotions in others and the aptitude ratings they received in an assessment center. Finally, Momm et al. (2015) found that individuals high in the ability to perceive emotions in others were better in handling interpersonal aspects in the workplace and had higher salaries. 1.2. Improving the ability to perceive emotions through training In a meta-analytic review, Blanch-Hartigan et al. (2012) identified 30 studies that attempted to improve person perception accuracy; however, no study aimed at training healthy adults in basic emotion identification. Only two of the EI intervention studies used randomized control trial and performance-based outcome measures. Crombie, Lombard, and Noakes (2011) and Reuben, Sapienza, and Zingales (2009) evaluated the intervention effect on emotional abilities (i.e., the ability to perceive, facilitate, understand, and manage emotions) by administering the performance-based Mayer–Salovey–Caruso Emotional Intelligence Test (MSCEIT; Mayer, Salovey, & Caruso, 2002) before and after the intervention. Our intervention study extends the work of those studies by using an active (i.e., trained) control group, by examining short-term effects (right after the intervention) and long-term effects (6 months after the intervention). Like these studies,

we examine the effects of our intervention by administering the performance-based MSCEIT (Mayer et al., 2002). In addition, we sought to extend previous EI intervention studies by examining the effects of training in emotion perception separately for the MSCEIT faces tasks and pictures tasks. Such a distinction is necessary because previous research has shown that emotion perception measures using different items such as pictures and faces form separate factors and not one emotion perception factor (Brannick, Wahi, & Goldin, 2011; Keele & Bell, 2008). Moreover, in a study on the effect on intranasal oxytocin on perceiving emotion Cardoso, Ellenbogen, and Linnen (2014) found different results for the MSCEIT emotion perception subscales. The authors found a significant effect of intranasal oxytocin on the ratings of emotion in the MSCEIT faces task, but not for ratings of nonsocial stimuli (i.e., the MSCEIT pictures task). Given this results, we examined analyses separately for the MSCEIT faces and pictures task. In addition to examining short-term and long-term intervention effects, we also examined relationships between individual differences in personality and the effects of the intervention. Although there are various models of personality, we chose to examine relationships between training effectiveness and the “Big Five” factors. Despite considerable research, little is known about which individuals benefit more than others from EI interventions. In a meta-analytic review of 89 studies, Blume, Ford, Baldwin, and Huang (2010) concluded that conscientiousness (.28) and neuroticism (.19) were moderately related to training success, defined as increases in knowledge, skills, and transfer of these results into the daily work environment. In contrast, they found close to zero correlations between success and openness to experience (.08), extraversion (.04), and agreeableness (−.03). In another review, Burke and Hutchins (2007) found only mixed support for the positive relationship between conscientiousness and the effectiveness of training and reported only minimal or no relationships between effectiveness and the other Big Five personality traits. In a review of research on relationships between personality and job performance and training effectiveness that was not limited to EI, Hurtz and Donovan (2000) concluded that conscientiousness, agreeableness, and emotional stability were positively related to “interpersonal facilitation”, a construct that involved emotional skills (Van Scotter & Motowidlo, 1996). Moreover, they concluded that agreeableness was positively related to training effectiveness, whereas conscientiousness was positively related to job performance per se. Taken together, the available research led us to expect that agreeableness would be positively related to training effectiveness. We examined relationships between training outcomes and other factors on a somewhat exploratory basis. 1.3. Multiple-group SEM Approach to examine intervention effects As mentioned above, most studies of EI interventions have examined intervention effects using ANOVA or MANOVA. Although such analyses can be useful for evaluating treatment effects, they have limitations. First, they “do not explicitly address the issue of measurement error in the outcome variables, as these methods focus on observed rather than latent variables” (Crayen et al., 2011, p. 498). Second, they do not allow testing for measurement invariance across time and groups (Cheung & Rensvold, 2002). See Supplementary material, Section 1 for a detailed description of the advantages of multiplegroup SEM. 1.4. The present research The available research suggests that it is possible to improve the ability to perceive emotions in others, an ability that is a fundamental component of EI (Mayer & Salovey, 1997). In the present study participants were randomly assigned to a training group or a control group. All individuals participated in a 1-day program followed by 1-month online follow-up. The EI training focused on the ability to perceive emotions

S. Herpertz et al. / Personality and Individual Differences 95 (2016) 11–19

in others, and the training in the control group focused on time management skills. Participants completed a series of measures before the intervention, at the end of the intervention, and 6 months later. This design allowed us to examine whether our intervention led to substantial short-term and long-term improvements in the ability to perceive emotions in others. The study was guided by the following hypotheses. Hypothesis 1. The emotion perception intervention will increase participants' ability to perceive emotions in others as measured at the end of the intervention (short-term effect). Following studies that have shown that the MSCEIT faces task and pictures task are not one construct (Brannick et al., 2011; Cardoso et al., 2014; Keele & Bell, 2008), we examined the analyses separately for the MSCEIT faces and pictures task. Given our aim to improve the ability to perceive emotions in others, we expected to find an improvement especially in the ability to perceive emotions in faces, i.e., MSCEIT faces task. Hypothesis 2. We expect that these improvements will not decrease over time, i.e., 6 months after the end of training. Hypothesis 3. Agreeableness will moderate training effects. Participants higher in agreeableness will benefit more from training than those lower in agreeableness. We examined the moderating effects of other factors of the five factor model (FFM) on an exploratory basis. 2. Method 2.1. Procedure Participants were solicited via an announcement posted on a notice board for economic science lectures at a medium size University in Southern Germany. The study was described as an evaluation study of a new soft skill training program that was relevant for career success. Participation was restricted to individuals who were students of economy and business administration and for whom German was their first language. Interested participants were randomly assigned to the training group (TG) or the control group (CG). Participants were then assigned to one of five training sessions. Participants attended a 1-day program. Individuals in the TG received training designed to improve the ability to perceive emotions in others. In contrast, participants in the CG received training that focused on time management. To increase the transfer effect, the 1-day program was followed by a group-specific 1-month online follow-up program. Participants completed an online battery before the intervention (Time 1, baseline), at the end of the intervention (Time 2, i.e., after the 1 month online follow-up), and 6 months later (Time 3). 2.2. Participants A total of 105 individuals participated in the study (54 TG, 51 CG). Due to missing responses, 18 individuals (9 TG, 9 CG) were excluded from the short-term effect analyses (Time 1 to Time 2). In addition, 28 individuals (15 TG, 13 CG) were excluded from the long-term effect analyses (Time 2 to Time 3). Thus, the final sample consisted of 87 individuals (n = 45 in the TG and n = 42 in the CG) who completed Time 1 and Time 2, and 59 individuals (n = 30 in the TG and n = 29 in the CG) who completed all three time-points. The 87 participants had a mean age of 22.08 years (SD = 2.08). The sample consisted of 55 women and 32 men. Individuals were predominantly students of business administration and management (n = 59, 67.8%) or business related studies (n = 26, 32.2%), e.g., economic studies and international business studies. Sixty-seven participants (77%) were undergraduate students; 20 (23%) participants were graduate students. Chi-squared tests revealed that there were no significant differences between the TG and

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the CG regarding gender (χ2 = 1.19, ns), field of study (business or business related fields; χ2 = 1.34, ns), and status (undergraduate or graduate; χ2 = 2.91, ns). A univariate analysis of variance found no significant differences between the TG and CG regarding age F(1, 86) = 0.12, ns. 2.3. Intervention design Each training was conducted by two psychologists (one male and female). The procedure was standardized and was based on guidelines that included exercises and structured input from the trainers. The structure of the intervention was based on established methods such as group discussion and role play (Kolb & Boyatzis, 1970), supplemented with theoretically focused feedback from the trainers. In addition, we followed the guidelines of Blanch-Hartigan et al. (2012) for developing effective emotion perception training programs. We used a combination of training approaches (practice, feedback, and instruction) and small training groups (less than 10 participants). 2.3.1. Transfer effect To enhance the transfer effect of our training (in both the emotion training and control groups) we used three methods. First, we focused on action-oriented exercises and on the personal experiences of the participants (Kauffeld, 2010, p. 56). Second, we developed training materials that were reality-based (Burke & Hutchins, 2007, 2008), for example, we focused on typical situations for students such as working in group projects with other individuals, exam periods, and so forth. Third, we utilized the “knot rope” approach (Weidenmann, 2008, p. 131) and instructed participants to develop reminders with the help of perforated file cards, staves, and a rope. In addition, we conducted a longitudinal online follow-up (Matthews, Zeidner, & Roberts, 2007; Zeidner, Roberts, & Matthews, 2008). Once a week, all participants received group-specific e-mails, which included reminders and further material and instructions for transfer exercises. To enable continued practice at home, relevant exercises were introduced during the training. All participants also received a photo documentation and summary of the theoretical input and exercises. 2.3.2. The emotion perception intervention The emotion perception intervention focused on training the ability to perceive emotions in others. During the 1-day training program participants were familiarized with the specific features of basic and complex emotions. The intervention consisted of three modules. During the first module, participants were made aware of the relevance of EI in daily life with a focus on emotion perception in the workplace. In addition, participants were introduced to the four-branch model of EI (perceiving, using, understanding, and regulating emotions; Mayer & Salovey, 1997). The goal of the second module was to increase the ability to perceive emotions in others. Accordingly, we trained emotion perception in terms of facial expressions, body language, and voice. The pictures of the University of California, Davis, Set of Emotion Expressions (UCDSEE; Tracy, Robins, & Schriber, 2009) were used as pre-tested picture material to present characteristic cues of the six basic emotion families (Ekman & Friesen, 1975) and practice perception. Dynamic stimuli of the Geneva Multimodal Expression Corpus for experimental research on emotion perception (GEMEP; Bänziger, Mortillaro, & Scherer, 2012) were used as training material to train the ability to perceive complex emotions (e.g., admiration, despair). Participants were instructed to identify and differentiate characteristic cues based on information from voice (Pell, Paulmann, Dara, Alasseri, & Kotz, 2009), and body action and posture (Dael, Mortillaro, & Scherer, 2012) of the emotions. In the third module, mirrors were used to help participants see how they expressed various emotions. We also conducted role playing exercises. Participants were told to imagine taking part in group project meeting. Based on standardized instructions, each individual played a different role in a project team (e.g., an angry developer). The entire

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process was videotaped. Subsequently, the participants watched and discussed the various features of emotion expressions in the process of the discussion with a focus on each individual. Following the training sessions, participants received an email once a week for the next four weeks. Each email included theoretical reminders (e.g., features of the facial expression of fear) and related practical exercises (e.g., identifying other's emotions in faces and pictures).

2.3.3. The control group intervention The control group intervention aimed at improving time management skills. We used a previously tested time management training concept (Schütz, 2011). Specifically, participants were familiarized with time management techniques such as Pareto analysis (priorities with the 20/80 rule), Eisenhower method (evaluate tasks using the criteria unimportant/important and not urgent/urgent), and the ALPEN method (A: list your tasks and appointments, L: estimate the expected duration of these activities, P: include time for breaks, E: set priorities, etc.). During the four-week online follow-up, participants of the CG received emails to enhance time management skills (e.g., preparation of a weekly plan with help of the ALPEN method).

2.4. Measures 2.4.1. Ability to perceive emotions The ability to perceive emotions in others was assessed with the German version of the Mayer–Salovey–Caruso Emotional Intelligence Test (Steinmayr, Schütz, Hertel, & Schröder-Abé, 2011) prior to the intervention (Time 1), at the end of the intervention (Time 2), and 6 months later (Time 3). Consistent with our focus on improving the ability to perceive emotions in others, we used only the faces and pictures tasks of the MSCEIT. Participants had to rate to which degree five emotions were present in four photographs of faces (faces task) and in six pictures of art or landscape (pictures task). Answers were made on 5-point scale ranging from 1 (no/not at all) to 5 (extreme/very strong). Participants' scores on each task were calculated using the consensus scoring method.

2.4.2. Personality as a moderator To examine the role played by individual differences in personality traits, participants completed the Big Five Inventory (BFI; John, Donahue, & Kentle, 1991; German adaptation by Lang, Lüdtke, & Asendorpf, 2001). Answers were made on 5-point scale ranging from 1 (strongly disagree) to 5 (strongly agree).

3. Results 3.1. Overview of analyses We tested our hypotheses with multiple-group SEM using Mplus, Version 7.2 (Muthén & Muthén, 1998-2012). With this type of model it is possible to examine (1) latent mean differences across groups and time (dmean), (2) latent mean changes across time in each group (dchange), and (3) latent moderation effects. Due to the fact that not all participants provided data at each time point we tested separate models to assess short-term intervention effects (Time 1 to Time 2, referred to as the pre–post model) and long-term intervention effects (Time 2 to Time 3, referred to as the post–post model). We performed a multiple-group SEM where Time 2 was regressed on Time 1, and Time 3 was regressed on Time 2, respectively (see Fig. B1), and in these models we allowed the error terms to covary from Time 1 to Time 2, and from Time 2 to Time 3 (see Kline, 2012, p. 115). See Supplementary material, Section 2 for a detailed description of testing measurement invariance and examination of latent mean differences, mean changes, and moderation effects. For all models, we used item parcels as indicators of the latent factors instead of single item indicators. We identified the model by constraining one parcel of each latent variable to 1.0. Accordingly, for each MSCEIT task we created four parcels using the item-to-construct balance approach by Little, Cunningham, Shahar, and Widaman (2002). Seven items of the MSCEIT faces task and two items of the MSCEIT pictures task had to be excluded from the analyses because of negative discriminating power (see Kline, 2012, p. 115, for similar results). Final internal consistencies of the MSCEIT faces task were .82 (Time 1), .88 (Time 2), and .87 (Time 3); and of the MSCEIT pictures task .87 (Time 1), .89 (Time 2), and .91 (Time 3). Each of the five subscales of the BFI (Lang et al., 2001) was modeled using three parcels. Items were randomly assigned to parcels. Five items had to be excluded from the analyses (two items each from the conscientiousness and openness scale, and one item from the neuroticism scale) because of low item-total correlation. Final internal consistencies of the scales were .85 (extraversion), .75 (conscientiousness), .77 (agreeableness), .81 (neuroticism), and .84 (openness).

3.2. Descriptive statistics Means and standard deviations for each variable and each group are reported separately for participants in the TG and CG at Time 1, Time 2, and Time 3 (see Table A1). Intercorrelations for all study variables may be found in Table A2.

Fig. B1. Multiple-group SEM.

S. Herpertz et al. / Personality and Individual Differences 95 (2016) 11–19

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Table A1 Means (and standard deviations) for each variable and each group. Variable

TG

CG

TG

CG

TG

CG

n = 45

n = 42

n = 45

n = 42

n = 30

n = 29

M (SD)

M (SD)

M (SD)

M (SD)

M (SD)

M (SD)

48.33 (11.78) 40.09 (9.95) 3.67 (0.78) 3.65 (0.58) 4.01 (0.59) 2.91 (0.76) 3.72 (0.69)

56.14 (8.53) 44.04 (9.23)

48.17 (13.14) 38.61 (11.47)

54.47 (11.08) 42.75 (10.58)

Time 1 MSCEIT faces task MSCEIT pictures task BFI extraversion BFI agreeableness BFI conscientiousness BFI neuroticism BFI openness

Time 2

49.05 (11.57) 42.75 (9.36) 3.81 (0.62) 3.74 (0.59) 3.83 (0.51) 2.87 (0.72) 3.78 (0.74)

Time 3 49.64 (12.37) 38.66 (12.67)

Note. TG = emotion perception intervention group; CG = control group; MSCEIT = Mayer–Salovey–Caruso Emotional Intelligence Test; BFI = Big Five Inventory. Time 1 = prior the intervention; Time 2 = at the end of the intervention (after the 1 month online follow-up); Time 3 = 6 months following the intervention.

change (i.e., improvement) in the ability to perceive emotions in others in the training group.

3.3. Components of the MSCEIT To determine if the MSCEIT emotion perception task consisted of one or two factors, we conducted confirmatory factor analyses (CFA) on scores from the item parcels. These results suggest that there were two separate factors (i.e., faces task and pictures task factor), and we analyzed the MSCEIT faces and pictures tasks separately (see Supplementary material, Section 3 for results). 3.4. Measurement invariance across groups and time To test for measurement invariance, we analyzed the MSCEIT subscales of faces and pictures tasks and time-points (pre–post, post– post). The results of these analyses, summarized in Supplementary material, Section 4, revealed an acceptable model fit for the 12 invariance models (nonsignificant χ2, RMSEA ≤ .05, CFI ≥ .95, SRMR ≤ .10). All parameters were significant, and all standardized loadings were at least .70 across groups and time. Constraining factor loadings (Model 2) and intercepts (Model 3) to be invariant produced no decrement in fit for both MSCEIT tasks in the pre–post model, and in the post–post model. The MSCEIT faces task and MSCEIT pictures task thus demonstrated stable measurement properties across groups and time. 3.5. Short-term effects (pre–post model) To examine the effect of the training over time, we tested for differences in the latent means of the TG and CG conditions at Time 1 and Time 2, and changes in latent means between Time 1 and Time 2 for each of the two groups. We were looking for (1) a significant group difference between both groups after the intervention and (2) a significant

3.5.1. MSCEIT faces task To test differences in latent means, we compared a model where means were constrained to zero in the CG and were left free in the TG (Model 4) with a model in which the means were constrained to zero across both groups (Model 5). As expected, the analysis showed that there were no significant baseline differences between the TG and CG before the intervention (MSCEIT faces: dmean = 0.08, z = 0.36, p = .72). Supporting Hypothesis 1, constraining means to be zero in both groups produced a decrement in fit due to sizable group differences in the expected direction (see model comparison Model 4 vs. 5 in Table A3). Individuals in the TG had significantly higher scores in the MSCEIT faces task at the end of the intervention (Time 2: dmean = 1.02, z = 4.90, p b .001) than individuals in the CG. In line with our hypothesis, the examination of the latent mean change across time indicated as well a significant increase in the MSCEIT faces task between Time 1 and Time 2 in the TG (dchange = 0.76, z = 5.08, p b .001), and not in the CG (dchange = 0.02, z = 0.17, p = .86). 3.5.2. MSCEIT pictures task We conducted the same analyses for the MSCEIT pictures task. Analysis of baseline differences showed no significant differences between the TG and CG before the intervention (dmean = 0.40, z = 1.54, p = .12). As shown in Table A3, the Satorra–Bentler scaled difference chisquare test indicated a better fit for Model 5 (all means are zero). The analyses of the MSCEIT pictures task did not find a significant effect for training at Time 2 (dmean = 0.18, z = 1.11, p = .27). Consistent with this, the analyses did not find a significant change in latent means between Time 1 and Time 2 for either the training group TG

Table A2 Intercorrelations for each variable and each group. Variable

(1)

(2)

(3)

(4)

(5)

(6)

(7)

Time 1 (1) (2) (3) (4) (5) (6) (7)

MSCEIT-F MSCEIT-P BFI-E BFI-A BFI-C BFI-N BFI-O

– .72⁎⁎ .22 .15 .31⁎ .03 −.08

(1)

(2)

Time 2 .61⁎⁎ – .17 .15 .36⁎ .02 −.05

.15 .14 – .07 .08 −.18 .27

−.15 −.02 .09 – .27 −.40⁎⁎ −.01

.01 .01 −.18 .18 – −.04 −.23

.11 −.01 .12 −.17 −.49⁎⁎ – .03

.08 .14 .45⁎⁎ .16 −.03 −.02 –

– .73⁎⁎

(1)

(2)

Time 3 .69⁎⁎ –

– .50⁎⁎

.74⁎⁎ –

Note. MSCEIT = Mayer–Salovey–Caruso Emotional Intelligence Test; MSCEIT-F = faces task; MSCEIT-P = pictures task; BFI = Big Five Inventory; BFI-E = extraversion; BFI-A = agreeableness; BFI-C = conscientiousness; BFI-N = neuroticism; BFI-O = openness. Time 1 = prior the intervention (TG: n = 45; CG: n = 42); Time 2 = at the end of the intervention (TG: n = 45; CG: n = 42); Time 3 = 6 months following the intervention (TG: = 30; CG: n = 29). Intercorrelations for the training group are shown above the diagonal; intercorrelations for control group are shown below the diagonal. ⁎ p b .05. ⁎⁎ p b .01.

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Table A3 Fit indices for testing of latent mean differences across groups. Model Pre–post model MSCEIT faces task 1. Model 4 2. Model 5 MSCEIT pictures task 1. Model 4 2. Model 5 Post–post model MSCEIT faces task 1. Model 4 2. Model 5 MSCEIT pictures task 1. Model 4 2. Model 5

χ2

df

CFI

RMSEA

SRMR

Model comparison

TRd

Δdf

ΔCFI

35.67 60.09

46 48

1.00 .97

.00 .08

.10 .20

5 vs. 4

24.42⁎⁎⁎

2

−.03

44.92 48.31

48 50

1.00 1.00

.00 .00

.07 .10

5 vs. 4

3.81

2

.00

51.21 55.62

49 51

.99 .99

.04 .06

.10 .15

5 vs. 4

5.03†

2

.00

44.08 51.06

47 49

1.00 1.00

.00 .03

.08 .12

5 vs. 4

6.01⁎

2

.00

Note. n = 87 (pre–post model); n = 59 (post–post model). MSCEIT = Mayer–Salovey–Caruso Emotional Intelligence Test; BFI = Big Five Inventory. Model 4 = latent factor means are zero in the CG and free in the TG; Model 5 = means are zero across both groups. CFI = comparative fit index; RMSEA = root-mean-square error of approximation; SRMR = standardized root mean squared residual; TRd = Satorra–Bentler scales difference chi-square statistic. † p b .10. ⁎ p b .05. ⁎⁎⁎ p b .001.

(dchange = 0.10, z = 0.79, p = .43) or the control group (dchange = −0.12, z = −1.40, p = 16). 3.6. Moderating effects of personality We investigated the Big Five personality traits as potential moderators of pre–post changes in scores on the MSCEIT faces task, and we did this by using a latent moderated structural equation approach (LMS) as described by Klein and Moosbrugger (2000). In these analyses, the moderating effect of a personality trait was tested (1) by examining the fit of a model without the interaction term and (2) by estimating a model with moderating interaction term (see Fig. B2). We examined the moderating effect of each of the Big Five traits separately. As expected due to random assignment, tests of differences in latent means found no significant differences between the TG and CG groups in terms of any of the measures of the Big Five (all ps N .17). These results of these analyses were quite clear. As Table A4 shows, the results revealed an acceptable model fit for the five models without interaction parameters (nonsignificant χ2, RMSEA ≤ .06, CFI ≥ .95,

SRMR ≤ .11). Nevertheless, the results of testing models with a latent interaction indicated a significant interaction term in the TG for agreeableness (z = 2.23, p = .03) and conscientiousness (z = 16.91, p b .001). In contrast, a model that included an interaction term did not yield a significant result for neuroticism, extraversion, and openness (zs ranged from −1.02 to 1.54, ps N .21). These results indicated that the impact of the EI training varied as a function of how agreeable and conscientious participants were, whereas the impact of the training did not vary as a function of how neurotic, extraverted, or open to new experiences participants were. A summary of these analyses is presented in Table A4. To understand these moderating effects, we examined pre–post differences in scores on the MSCEIT faces task for individuals who were low and high (+/− 1 SD) on agreeableness and conscientiousness. These analyses found that for agreeableness, individuals who were low and individuals who were high in agreeableness benefited significantly from the intervention, but individuals who were low in agreeableness benefited less than individuals who were high (low: b = 0.33, SE = 0.10, p b .001; high: b = 1.05, SE = 0.44, p = .02). A similar pattern was found for conscientiousness (low: b = 1.42, SE = 0.11, p b .001; high: b = 2.21, SE = 0.10, p b .001). Although our analyses did not find that the intervention led to changes in scores on the MSCEIT pictures task, we examined the moderating effects of individual differences in the Big Five on these changes. None of the measures of personality moderated these changes (all ps N 25). 3.7. Long-term effects (post–post model) To examine long-term effects, we tested for latent mean differences at Time 3, and as well for latent mean changes in each group between Time 2 and Time 3 (see Table A3 for model fit and Satorra–Bentler scaled difference chi-square test).

Fig. B2. Latent moderated SEM.

3.7.1. MSCEIT faces task To test latent mean differences at Time 3, we compared a model where means were zero in the CG and free in the TG (Model 4) with a model where means were zero across both groups (Model 5). As expected, Model 4 fit better than Model 5 (see model comparison Model 4 vs. 5 in Table A3). Supporting Hypothesis 3, the examination of the latent mean change across time showed no significant decrease or increase between Time 2 and Time 3 for the MSCEIT faces task (TG: dchange = − 0.09, z = − 0.88, p = 0.38; CG: dchange = 0.03, z = 0.19, p = 0.85); however, the analyses did not find a significant difference

S. Herpertz et al. / Personality and Individual Differences 95 (2016) 11–19

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Table A4 Fit indices and coefficients from latent moderator structural equation modeling of the BFI personality traits on the Relationship between MSCEIT faces task Time 1 and MSCEIT faces task Time 2. Model without an interaction term

Model with an interaction term

Fit indices

TG (n = 45)

2

CG (n = 42)

Moderator

χ

df

CFI

RMSEA

SRMR

b

SE

b

SE

Extraversion Neuroticism Openness Conscientiousness Agreeableness

107.06 120.11 98.58 103.12 118.07

100 110 99 101 100

.99 .99 1.00 1.00 .96

.04 .05 .00 .02 .06

.11 .11 .11 .10 .11

−0.11 −1.42 −0.03 0.89⁎⁎⁎ 0.29⁎

0.64 1.40 0.26 0.05 0.13

0.52 −0.63 0.24 −0.56 0.69

1.27 1.15 0.16 0.38 0.39

Note. n = 87 (pre–post model). Model without an interaction term: fit indices from latent moderator structural equation modeling; CFI = comparative fit index; RMSEA = root-meansquare error of approximation; SRMR = standardized root mean squared residual. Model with an interaction term: columns represent different models, cells show coefficients from latent moderator structural equation modeling and standard errors. TG = emotion perception intervention group; CG = control group. ⁎ p b .05. ⁎⁎⁎ p b .001.

in the latent means for the training and control groups at Time 3 (dmean = −0.08, z = −0.46, p = .64). 3.7.2. MSCEIT pictures task Finally, the Satorra-Bentler scaled difference chi-square test indicated a better fit for Model 5 (all means are zero; see Table A3). In line with this result, the analyses found no significant differences in latent means at Time 3 (dmean = 0.17, z = 1.47, p = .14) and no significant latent mean change between Time 2 and Time 3 (TG: dchange = 0.13, z = 1.43, p = .38; CG: dchange b 0.01, z = 0.03, p = .97). 4. Discussion The aim of our study was to examine the effects of an intervention designed to increase EI. As expected, we found that our intervention resulted in a significant increase in the ability to perceive emotions in others. Moreover, this training effect remained stable over a 6 month interval. We also found that more conscientious and more agreeable participants benefitted more from the training than others. The current study was the first to test the assumption of measurement invariance across groups and time. We found that measurement invariance existed across groups and times. Demonstrating scalar invariance is important because it implies that latent mean differences must be due to the “influence of common factors” (Millsap & Olivera-Aguilar, 2012, p. 381). With respect to short-time effects, our study demonstrated that it is possible to improve the ability to perceive emotions in others through training. We examined differences in the TG and CG prior to the intervention (Time 1) and at the end of the intervention (Time 2) and changes from Time 1 to Time 2 in each group. As expected, we found no significant differences prior the intervention but a significant difference between the groups after the training. Similarly, we found that changes from before to after the intervention were significant only for individuals in the TG. Extending previous research, we analyzed intervention effects separately for the MSCEIT (Steinmayr et al., 2011) faces and pictures task. In line with Brannick et al. (2011), the results of our CFA found that a twofactor solution fit better than a one-factor solution. Moreover, we found that the increase in the ability to perceive emotions in other individuals in the TG occurred only for the MSCEIT faces task and not for the pictures task. This result is in line with our intervention focus. We aimed at improving to perceive emotions in faces (and not in landscapes) also during the intervention. Thus, our findings point to the importance to examine intervention effects separately for different aspects of emotion perception. To examine individual differences in the effects of our intervention we used personality traits as potential moderators. We found that participants who were high in conscientiousness and agreeableness benefitted more from the intervention than others. There were no significant effects for extraversion, neuroticism, and openness. The

moderator effect of conscientiousness is consistent with a previous meta-analytic review about predictive training transfer factors in general (Blume et al., 2010). The authors found that conscientiousness is moderately related to training transfer in general. The finding is not surprising if we recall that conscientiousness is related to academic effort and encompasses sticking to plans and task in a reliable and organized manner and fulfilling plans reliable and organized manner (Trautwein, Lüdtke, Roberts, Schnyder, & Niggli, 2009). Interestingly, we also found a significant interaction with agreeableness. Agreeable persons can be described as helpful and kind (Lang et al., 2001), and both agreeableness and conscientiousness has been shown to be related to positive social interactions (e.g., Nezlek, Schütz, Schröder-Abé, & Smith, 2011). We assume that in trainings that are done in social settings (e.g., plenty of group discussions and role plays), such as the one we had tested, agreeableness may also help participants to use input. In sum, the effect for conscientiousness and agreeableness is in line with previous research on personality and job performance and training effectiveness, suggesting that agreeableness is related to training effectiveness, and conscientiousness is associated with job performance (Hurtz & Donovan, 2000).

4.1. Strengths, limitations, and directions for future research The present study has several methodological advantages. Multiplegroup SEM was used to examine the intervention effect. Such models are needed to test the assumption of measurement invariance, which is a precondition for examining the effects of an intervention across time. Moreover, we used a randomized control group that allowed us to examine immediate and sustained effects of the training. Clearly, some limitations of this intervention study should be noted. As outlined before, one of the fundamental components of the fourbranch model of EI (Mayer & Salovey, 1997) is emotion perception, and it encompasses “the ability to identify emotions in oneself and others, as well as in other stimuli, including voices, stories, music, and works of art” (Brackett et al., 2006, p. 781). Our study did not aim to enhance the ability to identify emotions in oneself and in other stimuli (e.g., works of art). Future research should try to replicate the current results and include the ability to perceive one's own emotions and the ability to perceive emotions in other stimuli. Furthermore, our results are limited as they are based on a performance test of emotional intelligence, the MSCEIT (Steinmayr et al., 2011). It is not clear whether results would be the same if we had tested the effectiveness of our training with another type of emotion perception task such as the Profile of Nonverbal Sensitivity (PONS; Rosenthal, Hall, DiMatteo, Rogers, & Archer, 1979) or the Diagnostic Analysis of Nonverbal Accuracy (DANVA; Nowicki & Duke, 1994). Further research is needed to determine if the same results would occur using other types of emotion perception tasks as outcomes.

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A limitation to our findings is that we did not find a significant group differences 6 months after the training program, although we did find that the training effect remained stable over time. Thus we can assume that it is possible to improve the ability to perceive emotions in faces over a long period of time. We recommend that future research investigate how long emotion perception training should be to show stronger long-term effects (e.g., 6 months and 1 year after the intervention). Moreover, there is also the issue of how we measured the ability to perceive emotions. We limited our study to the use of a performancesbased measurement of EI. Even if the use of a performance test has some advantages over self-report (e.g., not vulnerable to selfenhancement and socially desirable responding), we did not assess the ability to perceive emotions on the basis of neural correlates. We thus recommend that future research apply additional measures of emotional perception (e.g., fMRI) to assess biological mechanism (e.g., Hansenne, Nélis, Feyers, Salmon, & Majerus, 2014). Finally, future studies should investigate whether our results can be generalized to other samples. Such ability training may help people in leadership or service positions to be more effective at their jobs. New megatrends (e.g., digitization) should be considered in such trainings. For example, future research might explore how new forms of learning can be exploited to develop emotion perception in the workplace (e.g., mobile learning). Despite these shortcomings, our findings suggest that it is possible to improve the ability to perceive emotions in others through training. Appendix A. Supplementary data Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.paid.2016.02.015. References Bänziger, T., Mortillaro, M., & Scherer, K.R. (2012). Introducing the Geneva multimodal expression corpus for experimental research on emotion perception. Emotion, 12, 1161–1179. http://dx.doi.org/10.1037/a0025827. Blanch-Hartigan, D., Andrzejewski, S.A., & Hill, K.M. (2012). The effectiveness of training to improve person perception accuracy: A meta-analysis. Basic and Applied Social Psychology, 34, 483–498. http://dx.doi.org/10.1080/01973533.2012.728122. Blume, B.D., Ford, K., Baldwin, T.T., & Huang, J.L. (2010). Transfer of training: A metaanalytic review. The Journal of Management Development, 36, 1065–1105. http://dx. doi.org/10.1177/0149206309352880. Brackett, M.A., Rivers, S.E., Shiffman, S., Lerner, N., & Salovey, P. (2006). Relating emotional abilities to social functioning: A comparison of self-report and performance measures of emotional intelligence. Journal of Personality and Social Psychology, 91, 780–795. http://dx.doi.org/10.1037/0022-3514.91.4.780. Brannick, M.T., Wahi, M.M., & Goldin, S.B. (2011). Psychometrics of Mayer–Salovey–Caruso emotional intelligence test (MSCEIT) scores. Psychological Reports, 109, 327–337. http://dx.doi.org/10.2466/03.04.PR0.109.4.327-337. Brunwasser, S.M., Gillham, J.E., & Kim, E.S. (2009). A meta-analytic review of the Penn Resiliency Program's effect on depressive symptoms. Journal of Consulting and Clinical Psychology, 77, 1042–1054. http://dx.doi.org/10.1037/a0017671. Burke, L.A., & Hutchins, H.M. (2007). Training transfer: An integrative literature review. Human Resource Development Review, 6, 263–296. http://dx.doi.org/10.1177/ 1534484307303035. Burke, L.A., & Hutchins, H.M. (2008). A study of best practices in training transfer and proposed model of transfer. Human Resource Development Quarterly, 19, 107–128. http:// dx.doi.org/10.1002/hrdq.1230. Cardoso, C., Ellenbogen, M.A., & Linnen, A. -M. (2014). The effect of intranasal oxytocin on perceiving and understanding emotion on the Mayer–Salovey–Caruso emotional intelligence test (MSCEIT). Emotion, 14, 43–50. http://dx.doi.org/10.1037/a0034314. Cheung, G.W., & Rensvold, R.B. (2002). Evaluating goodness-of-fit indexes for testing measurement invariance. Structural Equation Modeling: A Multidisciplinary Journal, 9, 233–255. http://dx.doi.org/10.1207/s15328007sem0902_5. Crayen, C., Geiser, C., Scheithauer, H., & Eid, M. (2011). Evaluating interventions with multimethod data: A structural equation modeling approach. Structural Equation Modeling: A Multidisciplinary Journal, 18, 497–524. http://dx.doi.org/10.1080/ 10705511.2011.607068. Crombie, D., Lombard, C., & Noakes, T. (2011). Increasing emotional intelligence in cricketers: An intervention study. International Journal of Sports Science & Coaching, 6, 69–86. http://dx.doi.org/10.1260/1747-9541.6.1.69. Dael, N., Mortillaro, M., & Scherer, K.R. (2012). Emotion expression in body action and posture. Emotion, 12, 1085–1101. http://dx.doi.org/10.1037/a0025737. Ekman, P., & Friesen, W.V. (1975). Unmasking the face: A guide to recognizing emotions from facial clues. Englewood Cliffs, NJ: Prentice Hall.

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