Results

Figure (2) shows the funnel plot for precision. The Egger’s regression test was not significant, b = -0.59, SE = 0.67, p = .18. In addition, the Begg and Mazumdar’s correlation test was not significant, τ = -0.02, = 0.41, p = .34. These results show that publication bias did not affect the results. Insert Figure 2 around here The effect size values ranged between -0.21 to 0.51. To estimate the mean effect size, results from 20 studies (k = 105; N = 4,227) indicated that, overall, there is a significant positive correlation between EI and academic success, r = 0.13, 95% CI [0.08–0.27], p < .01. As expected, a high heterogeneity was observed, Q(105) = 375.48, p < .001, I2 = 72.04. Moderator analysis showed that the mean effect size significantly varied by EI test, Q(3) = 42.93, p < .001, and EI subscale, Q(3) = 18.87,p = .04, while EI task nature [Q(1) = .71, p = .40], country [Q(1) = 3.08, p = .08] and academic performance criterion [Q(1) = .38, p = .54] did not significantly explain variability in the mean effect (see Table 3). Together, the EI test and EI subscale explained 34% of variability in the mean effect. Age was treated as a continuous variable, and the results showed that age did not significantly explain variability in the mean effect, b = 0.011, SE = 0.007, p = .17 (see Figure 2). Insert Table 3 and Figure 3 around here As Table (3) shows, the EQ-i test was highly correlated with academic success compared with other EI tests, and the perceiving emotions subscale was highly associated with academic performance compared with other EI subscales. Finally, the three-level multiple regression analyses showed that Level 3 (between-studies variance) explained 29.5% of variability in the mean effect, while Level 2 (within -studies variance) explained 33.5%. Together, Levels 2 and 3 explained 63% of variability in the mean effect.