“…Regarding the analytical strategies and techniques used in the studies reviewed here, Table 4 shows that only a relatively small percentage of papers (25.88%) performed only bivariate analysis to test their hypotheses, such as the estimation of chi-square statistics, t-test statistics, or correlation coefficients (e.g., Nogueira et al, 2018). The majority of the selected studies (74.12%) conducted multivariate analysis, such as logistic regressions (e.g., Palma-Contreras et al, 2020), hierarchical regressions (e.g., Pujol-Cols & Lazzaro-Salazar, 2020), multinomial regressions (e.g., Martinez & Fischer, 2019b), Poisson regressions (e.g., Rocco et al, 2017), and structural equation modeling (e.g., Pujol-Cols et al, 2021). This approach allowed researchers to test the relative influence of several predictors simultaneously and to control their results for the effects of numerous covariates or control variables.…”