Traditionally, cluster randomized controlled trials are analyzed with the average intervention effect of interest. However, in populations that contain higher degrees of heterogeneity or variation may differ across different values of a covariate, which may not be optimal. Within education and social science contexts, exploring the variation in…

In education, multisite trials involve randomization of pupils into intervention and comparison groups within schools. Most analytical models in multisite educational trials ignore that the impact of an intervention may be school dependent. This study investigates the impact of statistical models on the uncertainty associated with an effect size…

Multilevel modelling (MLM) is the most frequently used approach for evaluating interventions with clustered data. MLM, however, has some limitations that are associated with numerous obstacles to model estimation and valid inferences. Longitudinal multiple-group (LMG) modelling is a longstanding approach for testing intervention effects using…

This review explores predictors and consequences of students' growth goals and growth mindset in school with particular emphasis on how correlational statistical methods can be applied to illuminate key issues and implications. Study 1 used cross-sectional data and employed structural equation modelling (SEM) to investigate the role of growth…

As applications of multilevel modelling in educational research increase, researchers realize that multilevel data collected in many educational settings are often not purely nested. The most common multilevel non-nested data structure is one that involves student mobility in longitudinal studies. This article provides a methodological review of…