1. Poster template by ResearchPosters.co.za Essentially, its all about the measurement of EI. There is a significant amount of debate about which approach is best. More specifically, each approach has its problems. The MSCEIT has problems with its factor structure, the process by which it is scored, and that it does not correlate very well with other tests which measure non-verbal affect and facial perception. Mayer, Salovey & Barsade (2008) argue that tests which measure similar criterion should converge. In reality, they have been showing divergence, particularly with regard to Emotion Perception. They term their findings as ‘perplexing and troubling’ (p. 518). Their methods also come under attack by proponents of Trait EI, saying that it is divorced from the subjective nature of emotional experience (Petrides, 2011). Trait EI (as measured by the TEIQue) has serious issues in that it relates too heavily to the personality factors of the FFM. More specifically, it is argued that the 15 facets do not have any discriminant validity from personality, that they lack scientific value, brining in too many ‘eclectic’ terms into a single measurement which do not belong there (Mayer, Caruso & Salovey, 2008). Furthermore, the TEIQue has poor validities when it comes to predicting physical health and well being outcomes (Zeidner, Matthews & Roberts, 2012). Whilst some ‘Mixed-Model’ approaches have attempted to bring these approaches together by trying to assess EI related abilities with self- reports, such as the Bar-On EQi, they have only added confusion to the field of EI, which potentially cripples its value (Petrides, 2011; Locke, 2005). Instead, this report suggests that Situational Judgments offer both a finite measurement system through set choices between resolutions to a problem, whilst considering contextual cues which tap into realistic behavioral tendencies. Indeed, Sharma, Gangopadhyay, Austin & Mandal (2013) have designed such a test for the measurement of EI, and here, it is evaluated with regards to its internal structure and predictive power in relation to the TEIQue. Specifically, we wanted to test which one better predicted self-perceived stress (PSS-14, Cohen & Williamson, 1988). Background – A Brief History of EI Emotional Intelligence (EI) was popularized by Goleman (1995) in his bestselling psychology book, where he claimed that it was as important as IQ for many life outcomes. A surge of research began after its publication, in an effort to measure EI more accurately. The result has been two separate movements: the first considers EI as a cognitive-emotional ability, which can be measured by maximum performance tests (i.e. correct/incorrect answers); the second considers EI as a set of traits related to, but distinct from personality (Petrides, Pita & Kokkinaki, 2007; Petrides, 2011). Proponents of ‘Trait EI’ measure through self-report typical performance tests. The fact that these researchers have different approaches in their operationalization of EI implies that they conceptualize it differently too. Indeed, Mayer & Salovey (1997) suggest a 4 branch model of EI: Perception, Facilitation, Understanding & Managing Emotion. Eventually, the MSCEIT was developed, which represents their efforts in isolating EI as distinct from extant intelligences (Mayer, Saolvey, Caruso & Sitarenios, 2003; Mayer, Caruso & Salovey, 1999). The MSCEIT is the most widely used test under this “Ability EI” approach In contrast, Trait EI researchers propose a 15 facet model, covering all elements (i.e. the ‘sampling domain’) of EI. Their facets all share the same factor space as the Eysenckian Big Three as well as the Five Factor Model (Petrides et al., 2007). They claim that the most effective model to measure emotions based on self-efficacy is their TEIQue. Right… So What’s This Study All About? ABAB Methodology Factor Analysis Results Fig. 1. Pattern Matrix – Factor Loadings for the SJT (N=183) Sampling Adequacy & Component Correlations Assessing Predictive & Incremental Validity of SJT Demographic variables of Age (Mean 20.25 |SD| = 7.19), Gender (41m, 140f, 2 undisclosed) and Occupation (169 Students, 14 Non-students) were entered as predictor variables into A Standard Multiple Regression for the criterion variable of SJT score. A significant model emerged F (3, 179) = 4.024, p< 0.05 whereby 6.3% (R 2 ) of the variance in SJT score was accounted for by these predictors (R 2 adjusted = 4.7%). Gender did significantly predict SJT score (Beta =.209, t(179) = 2.86, p<0.05) along with Occupation (Beta =.161, t(179) = 2.17, p<0.05). However, Age did not significantly predict SJT score (Beta =.036, t(179) =.484, ns). The scores from the SJT of EI and TEIQue-SF were entered as predictors into a Hierarchical Multiple Regression analysis with criterion of PSS Score. The TEIQue Score Model: F(1, 181) = 8.29, p<0.01 whereby 4.4% (R 2 ) of the variance in PSS score was accounted for (R 2 adjusted = 3.8%). SJT Score in second model significantly improved the model (F change = 7.07, Sig F change =.009). The the R 2 change = 3.6%. Discussion Furthermore, in the model where all predictors were considered the TEIQue Score did not uniquely predict PSS scores to a significant level (Beta = -.134, t(180) = -1.74, ns) despite its pre-regression correlation with the DV. PSS score (Pearson’s p = -.210, p<0.05, one-tailed). However, the SJT score did uniquely predict PSS score to a significant level (Beta = -.204, t(180) = -2.66, p<0.01) in line with its pre-regression correlation (Pearson’s p = -.254, p<0.05, one-tailed). Thus, it was concluded that SJT Score incrementally predicted levels of self-appraised stress over and above the TEIQue Score. Conclusions & Future Research Directions References Acknowledgements 35 I’d like to thank my Supervisor Dr. Lesley Allinson for her help throughout this project. A special thanks to Heather Shaw, Christopher John Luke, and Ferenc Igali of the Psychology Technicians Office. Without them, this project would’ve collapsed under its own weight. I am also extremely grateful for Sudeep Sharma and her team who published the original SJT Paper. She gave me permissions to adapt the SJT, offering kind words of support for my research hypotheses. Without my Personal Tutor Dr. Paul Goddard, the report, & poster would not have been produced. His unwavering patience and support is worthy of gods. Friends/Colleagues & Family – To the masses of you that helped both directly and indirectly, I dedicate this project to you. Assessing the Incremental Validity of a Situational Judgment Test of Emotional Intelligence Student Researcher: Manveer Sidhu Supervisor: Lesley Allinson Contact Information: manveers.sidhu@gmail.com Principal Components Analysis (PCA), 3 factors accepted. Oblique Rotation (Direct Oblimin) because factors all were likely to be related to each other as well as to an overall emotional intelligence construct (Sharma, et al., 2013). Labels same as they were in Sharma et al., total variance explained = 31.35%. Sampling adequacy assessment via Kaiser-Meyer-Olkin method yielded an overall KMO = 0.792. A value close to 0 tends to indicate that a data set is diffuse, and not appropriate for Factor Analysis (Field, 2009; but see also Hutcheson & Sofroniou, 1999), but this is not the case with the KMO reported here. KMO summary across all SJT items can be seen in Fig. 2. Furthermore, Barlett’s Test of Sphericity revealed a chi square of χ2 (990) = 2673.56, p < 0.001, indicating that initial correlations between SJT items were significantly large enough for PCA. The components that emerged (see Fig. 1) inter-correlated as per Fig. 3. Fig. 2 Fig. 3 Incremental/Predictive Validity cont. The overall model: F(2, 180) = 7.82, p<0.01 explained 8% (R 2 ) of the variance in PSS score (R 2 adjusted = 7%). The PCA revealed a strong factor solution, though there were some methodological issues. Furthermore, The SJT significantly and uniquely predicted PSS scores over and above that of the TEIQue. There was also a small but significant predictor effect for Gender and Occupation on SJT scores, but not for Age. Taken together, these findings indicate a promising first step towards validating the SJT of EI. Future Research should more closely explore Gender effects, Involve a bigger sample size, and a bigger battery of tests. It should also include a confirmatory factor analysis of this scale to validate it more convincingly. This study involved the development of a fairly large online questionnaire In Qualtrics Survey Software that combined: 1.Demographics Questions (Age, Gender, Occupation etc.) 2.A 46 item Situational Judgment Test (SJT) of EI (Sharma et al., 2013) 3.A 33 item Short Form of the TEIQue (Petrides et al., 2007) 4.A 14-item Perceived Stress Scale (Cohen & Williamson, 1988) 5.Brief, Consent Form and a Debrief 183 participants (41 males, 140 females, 2 undisclosed) took part, most of which were undergraduate psychology students in return for course credit. Their data was used to perform an Exploratory Factor Analysis on the SJT data, to assess how its questions grouped together into subsets of EI. Furthermore, Multiple Regressions assessed the predictive power of demographics on EI score, and how EI scores on both the TEIQue and SJT predicted/accounted for scores on the PSS. The 3-factor solution reported for N=183 above explained 31.35% of the variance in SJT data, whereas it accounted for just 25.97% in Sharma et al. (2013) (N=213). This shows that, on the face of it, the Principal Components Analysis (PCA) solution presented here was strong, for a very similar sample size to the one originally used. It was decided to name the factors with the same labels to demonstrate how the scale withstood an analysis on a completely independent UK sample. However, this is where difficulties arose, because only 20/45 of the SJT items loaded cleanly (>0.3) onto the same factors as they did in Sharma et al. (2013). There are several reasons as to why this might be: In terms of the analysis, the obvious issue is with sample size, and the subject to item ratio. Specifically, here it was around 4:1, but it has been suggested that a 10:1 ratio yields more accurate factor solutions, minimizing misclassifications (Costello & Osborne, 2005). However, the sampling adequacy statistics seem positive, indicating that the analysis was more than satisfactory. Results could be interpreted to reason that the SJTs factor structure is poor, and that the Confirmatory models given in Sharma et al. are not appropriate. Investigating that claim would require repeating the analysis using a Maximum Likelihood factor extraction technique, or a Confirmatory Factor Analysis (which are more comprehensive). SJTs themselves have a long history, with good evidence of their incremental predictive power for job performance and academic achievement over and above cognitive aptitude tests (Lievens & Sackett, 2012). Further, they have been shown to predict Life Satisfaction, whilst not suffering from self deception or social desirability biases that Plague Trait EI tests (Sharma et al., 2013). This does not mean that the Trait EI approach is not useful, because they have been shown to predict several psychological well being outcomes such as Stress and Mood Control in the past (Petrides et al., 2007). However, extant SJTs of EI (STEM/STEU) show the domain specificity of EI measurements, which can only be offered through a more contextual assessment. Furthermore, the field of EI needs an ability style measure that offers a compromise between he two streams of research (Sharma et al., 2013). Cohen, S., & Williamson, G. (1988). Perceived stress in a probability sample of the U.S. In S. Spacapam & S. Oskamp (Eds.). The social psychology of health: Claremont Symposium on Applied Social Psychology. Newbury Park, CA: Sage Costello, A. B., & Osborne, J. W. (2005). Best Practices In Exploratory Factor Analysis: Four Recommendations for Getting the Most From Your Analysis. Practical Assessment, Research & Evaluation, 10(7), 1-9. Field, A. (2009). 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American Psychologist, 63(6), 503-517. Petrides, K. V. (2011). Ability and trait emotional intelligence. In Chamorro-Premuzic, T., Furnham, A., & von Stumm, S. (Eds.), The Blackwell-Wiley Handbook of Individual Differences. New York: Wiley. Petrides, K. V., Pita, R., & Kokkinaki, F. (2007). The location of trait emotional intelligence in personality factor space. British Journal of Psychology, 98, 273-289. Sharma, S., Gangopadhyay, M., Austin, E. and Mandal, M. K. (2013), Development and Validation of a Situational Judgment Test of Emotional Intelligence. International Journal of Selection and Assessment, 21, 57–73. Zeidner, M., Matthews, G., & Roberts, R. D. (2012). The Emotional Intelligence, Health, and Well-Being Nexus: What Have We Learned and What Have We Missed? Applied Psychology: Health and Wellbeing, 4(1), 1-30.