The aim of the present study is to conduct a confirmatory factor analysis (CFA) of the Rosenberg Self-esteem Scale (RSES) as part of the study of affective variables using a sample of English as a foreign language (EFL) university students in Morocco. Two hundred and six (N = 206) participants of undergraduate, graduate, and post-graduate levels completed the self-esteem (SE) questionnaire. Using classical methods of factor extraction before employing more robust techniques comprising minimum average partial (MAP) and parallel analysis (PA) to perform preliminary factor analysis (FA) using principal axis factoring (PAF), results conclusively and parsimoniously yielded a one-factor solution with acceptable construct reliability (Composite Reliability). CFA results, including goodness-of-fit indexes, confirmed that the one-factor model was better fitting compared to its competing independent two-factor counterpart, but marginally less so compared to the correlated version of the latter. Two out of the three constructed models showed good fit indexes, thus demonstrating the conformity of two measurement models with their respective hypothesized structural models. Furthermore, using the heterotrait-monotrait (HTMT) ratio, both two-factor models showed acceptable discriminant validity. The obtained results further corroborate both the one-factor and two-factor solutions reported in previous works for which we present new evidence from a Moroccan EFL context.

Using R to conduct univariate meta-analyses is becoming common for publication. However, R can also conduct multivariate meta-analysis (MMA). However, newcomers to both R and MMA may find using R to conduct MMA daunting. Given that, R may not be easy for those unfamiliar with coding. Likewise, MMA is a topic of advanced statistics. Thus, it may be very challenging for most newcomers to conduct MMA using R. If this holds, this can be viewed as a practice gap. In other words, the practice gap is that researchers are not capable of using R to conduct MMA in practice. This is problematic. This paper alleviates this practice gap by illustrating how to use R (the metaSEM package) to conduct MMA on educational psychology data. Here, the metaSEM package is used to obtain the required MMA text outputs. However, the metaSEM package is not capable of producing the other required graphical outputs. As a result, the metafor package is also used as a complimentary to generate the required graphical outputs. Ultimately, we hope that our audience will be able to apply what they learn from this method paper to conduct MMA using R in their teaching, research, and publication.