Abdullah Alamer

Exploratory structural equation modelling (ESEM) is an alternative to the well‐known method of confirmatory factor analysis (CFA). ESEM is mainly used to assess the quality of measurement models of common factors but can be efficiently extended to test structural models. However, ESEM may not be the best option in some model specifications, especially when structural models are involved, because the full flexibility of ESEM could result in technical difficulties in model estimation. Thus, set‐ESEM was developed to accommodate the balance between full‐ESEM and CFA. In the present paper, we show examples where set‐ESEM should be used rather than full‐ESEM. Rather than relying on a simulation study, we provide two applied examples using real data that are included in the OSF repository. Additionally, we provide the code needed to run set‐ESEM in the free R package lavaan to make the paper practical. Set‐ESEM structural models outperform their CFA‐based counterparts in terms of goodness of fit and realistic factor correlation, and hence path coefficients in the two empirical examples. In several instances, effects that were non‐significant (i.e., attenuated) in the CFA‐based structural model become larger and significant in the set‐ESEM structural model, suggesting that set‐ESEM models may generate more accurate model parameters and, hence, lower Type II error rate.

It is well known that successful second language (L2) learners are motivated individuals. Accordingly, L2 researchers have tested the predictive power of different motivational constructs on language learning outcomes. However, this perspective appears to neglect the assessment of achievement as a predictor of future motivation. To assess this possibility, we first employed the latent growth curve model (LGCM) to evaluate the initial values and growth rates of the two variables. We further applied a newly developed statistical method, the random-intercept cross-lagged panel model (RI-CLPM), to study the causal relationship. A total of 226 language students were monitored for 17 weeks at three time points. The analysis showed an increasing trend in the association between the growth levels of both variables. However, students’ autonomous motivation at Time 1 appears to affect achievement at Time 2. Further, the second wave of the RI-CLPM illustrated that achievement at Time 2 impacted autonomous motivation at Time 3, while motivation failed to predict scores on achievement at Time 3.