Promoting Emotional Intelligence for University Students: a Meta-analysis

Emotional Intelligence (EI) is defined either as a trait (Petrides et al., 2016), an ability (Mayer et al., 2004), or a mixture of skills and personal traits (Bar-On, 1997). The ability model comprises four dimensions of emotional intelligence: the ability to recognize emotions, generate emotions to support thinking, understand emotions and their changes over time, and the ability to regulate emotions in different situations. Existing research reports positive effects of EI on various variables, including improved stress coping (Toriello et al., 2022), higher academic performance (Karkada et al., 2020), increased professional performance (Gong et al., 2019), and enhanced subjective well-being (Xu et al., 2021). Meta-analyses (Hodzic et al., 2018; Molero et al., 2020; Kotsou et al., 2019b) and individual studies (Cotler et al., 2017; Pool and Qualter, 2012) also demonstrated the positive impacts of EI training across different age groups (e.g., kindergarten children, middle school students, university students, and adults). However, meta-analytic research on EI training in university students, a group often susceptible to stress, anxiety, and depression (Toriello et al., 2022), is still scarce. The present study addresses this gap by conducting a meta-analysis of the effects of EI training on university students and examining potential moderators, and aims to answer the following research questions: RQ1: Can EI be promoted among university students and how strong is the averaged overall effect of training interventions? RQ2: What is the impact of moderators upon overall effect? RQ2a: Do dimensions of EI (ability to recognize emotions, generate emotions to facilitate thought, understand emotions, and manage emotions) moderate the overall effect of interventions? RQ2b: Does the ability/ trait/ mixed model and measure moderate the overall effect of interventions? RQ2c: Does the training content moderate the overall effect of interventions? Bibliography used in the present Preregistration Bar-On, R. (1997): BarOn emotional quotient inventory. multi-health systems: Toronto. Cooper, H., Hedges, L. V., & Valentine, J. C. (Eds.). (2019). The handbook of research synthesis and meta-analysis. Russell Sage Foundation. Cotler, J. L., DiTursi, D., Goldstein, I., Yates, J., Del Belso, D. (2017): A mindful approach to teaching. In: Information Systems Education Journal 15 (1), S. 12. Egger, M., Smith, G. D., Schneider, M., & Minder, C. (1997). Bias in meta-analysis detected by a simple, graphical test. bmj, 315(7109), 629-634. Fisher, Z., & Tipton, E. (2015). robumeta: An R-package for robust variance estimation in meta-analysis. arXiv preprint arXiv:1503.02220. Gong, Z., Chen, Y., Wang, Y. (2019): The influence of emotional intelligence on job burnout and job performance: Mediating effect of psychological capital. In: Frontiers in psychology 10, S. 2707. Hedges, L. V., Tipton, E., & Johnson, M. C. (2010). Robust variance estimation in meta‐regression with dependent effect size estimates. Research synthesis methods, 1(1), 39-65. Hodzic, S., Scharfen, J., Ripoll, P., Holling, H., Zenasni, F. (2018): How efficient are emotional intelligence trainings: A meta-analysis. In: Emotion review 10 (2), S. 138–148. Karkada, I. R., D'souza, Urban J. A., Mustapha, J. A. (2020): Relationship of emotional intelligence and academic performance among medical students: Systematic review. In: Universal Journal of Educational Research 8 (3A), S. 72–79. Kotsou, I., Mikolajczak, M., Heeren, A., Grégoire, J., Leys, C. (2019): Improving emotional intelligence: A systematic review of existing work and future challenges. In: Emotion review 11 (2), S. 151–165. Mayer, J. D., Salovey, P., Caruso, D. R. (2004): Target Articles: Emotional intelligence: Theory, findings, and Implications. In: Psychological inquiry 15 (3), S. 197–215. Molero, P. P., Zurita-Ortega, F., Chacon-Cuberos, R., Castro-Sanchez, M., Ramirez-Granizo, I., Valero, G. G. (2020): Emotional intelligence in the educational field: A meta-analysis. In: Anales de psicología 36 (1), S. 84. Morris, S. B., & DeShon, R. P. (2002). Combining effect size estimates in meta-analysis with repeated measures and independent-groups designs. Psychological methods, 7(1), 105. Petrides, K. V., Siegling, A. B., Saklofske, D. H. (2016): Theory and measurement of trait emotional intelligence. In: The Wiley handbook of personality assessment, S. 90–103. Pool, L. D., Qualter, P. (2012): Improving emotional intelligence and emotional self-efficacy through a teaching intervention for university students. In: Learning and Individual Differences 22 (3), S. 306–312. Toriello, H. V., van de Ridder, J. M., Brewer, P., Mavis, B., Allen, R., Arvidson, C. et al. (2022): Emotional intelligence in undergraduate medical students: a scoping review. In: Advances in Health Sciences Education, S. 1–21. Viechtbauer, W. (2010). Conducting meta-analyses in R with the metafor package. Journal of statistical software, 36, 1-48. Xu, X., Pang, W., Xia, M. (2021): Are emotionally intelligent people happier? A meta‐analysis of the relationship between emotional intelligence and subjective well‐being using Chinese samples. In: Asian Journal of Social Psychology 24 (4), S. 477–498. This preregistration was inspired by the following preregistrations: https://osf.io/v2nh6 https://osf.io/ev7tf

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