Machine learning (ML) methods for causal inference have gained popularity due to their flexibility to predict the outcome model and the propensity score. In this article, we provide a within-group approach for ML-based causal inference methods in order to robustly estimate average treatment effects in multilevel studies when there is cluster-level…

The purpose of this study was to explore the degree of grain size of the attributes and the sample sizes that can support accurate parameter recovery with the General Diagnostic Model (GDM) for a large-scale international assessment. In this resampling study, bootstrap samples were obtained from the 2003 Grade 8 TIMSS in Mathematics at varying…

The purpose of the current study is to investigate the effects of frequency of peer victimization experiences on psychological and academic adjustment during early adolescence, with a focus on testing psychological adjustment as a mediator, as well as differences based on gender and type of victimization. The sample in this short-term longitudinal…

Despite the widespread popularity of growth curve analysis, few studies have investigated robust growth curve models. In this article, the "t" distribution is applied to model heavy-tailed data and contaminated normal data with outliers for growth curve analysis. The derived robust growth curve models are estimated through Bayesian…

This paper illustrates how information collected through interviews can develop a richer understanding of the assignment mechanism, which can result in more plausible causal effect estimates from observational studies and provides a roadmap for sensitivity analysis. Focusing on the issue of assignment to algebra in 8th grade, the author shows how…