Factor score path analysis : an alternative for SEM

Abstract. Theoretical researchers consider Structural Equation Modeling (SEM) to be the preferred method to study the relationships among latent variables. However, SEM has the disadvantage of requiring a large sample size, especially if the model is complex. Furthermore, since SEM estimates all parameters simultaneously, one misspecification in the model may influence the whole model. For these reasons, applied researchers often use a two-step Factor Score Regression (FSR) approach. In the first step, factor scores are calculated for the latent variables, which are used to perform a linear regression in the second step. However, this method results in incorrect regression coefficients. Croon (2002) developed a method that corrects for this bias. We combine this method of Croon (2002) with path analysis, resulting in Factor Score Path Analysis. This method results in correct path coefficients and has some advantages over SEM: it requires smaller sample sizes, can handle more complex models and the method is less sensitive to misspecifications, because of its stepwise nature. In conclusion, this method can be a suitable alternative for SEM, when one is dealing with a complex model and small sample sizes. </jats:p

Migrant Students’ Sense of Belonging and the Covid‐19 Pandemic: Implications for Educational Inclusion

This article investigates school belonging among migrant students and how this changed during the Covid‐19 pandemic. Drawing on quantitative data gathered from 751 migrant students in secondary schools in six European countries (Belgium, Denmark, Finland, Norway, Sweden, and the UK), we examined the impact of Covid‐19 school closures, social support, and post‐traumatic stress symptoms on changes in school belonging. Linear regression showed a non‐significant decrease in school belonging, and none of the studied variables had a significant effect on this change in our whole sample. However, sensitivity analysis on a subsample from three countries (Denmark, Finland, and the UK) showed a small but significant negative effect of increasing post‐traumatic stress symptoms on school belonging during Covid‐19 school closures. Given that scholarship on school belonging during Covid‐19 is emergent, this study delineates some key areas for future research on the relationship between wellbeing, school belonging, and inclusion.</p

Migrant Students’ Sense of Belonging and the Covid‐19 Pandemic: Implications for Educational Inclusion

This article investigates school belonging among migrant students and how this changed during the Covid‐19 pandemic. Drawing on quantitative data gathered from 751 migrant students in secondary schools in six European countries (Belgium, Denmark, Finland, Norway, Sweden, and the UK), we examined the impact of Covid‐19 school closures, social support, and post‐traumatic stress symptoms on changes in school belonging. Linear regression showed a non‐significant decrease in school belonging, and none of the studied variables had a significant effect on this change in our whole sample. However, sensitivity analysis on a subsample from three countries (Denmark, Finland, and the UK) showed a small but significant negative effect of increasing post‐traumatic stress symptoms on school belonging during Covid‐19 school closures. Given that scholarship on school belonging during Covid‐19 is emergent, this study delineates some key areas for future research on the relationship between wellbeing, school belonging, and inclusion.</p

Multilevel factor score regression

Multilevel SEM is an increasingly popular technique to analyze data that are both hierarchical and contain latent variables. The parameters are usually jointly estimated using a maximum likelihood estimator (MLE). This has the disadvantage that a large sample size is needed and misspecifications in one part of the model may influence the whole model. We propose an alternative stepwise estimation method, which is an extension of the Croon method for factor score regression. In this article, we extend this method to the multilevel setting. A simulation study was used to compare this new estimation method to the standard MLE. The Croon method outperformed MLE with regard to convergence rate, bias, MSE, and coverage, in particular when models contained a structural misspecification. In conclusion, the Croon method seems to be a promising alternative to MLE