One model may not fit all: Subgroup detection using model-based recursive partitioning

It provides a versatile tool for detecting and explaining heterogeneity in, for example, intervention studies. In this tutorial article, we introduce the general MOB framework.

In two specific case studies, we illustrate how MOB-based methods can be used to detect and explain heterogeneity in two widely used frameworks in educational studies: (a) The generalized linear mixed model (GLMM) and (b) item response theory (IRT). In the first case study, we show how GLMM trees (Fokkema et al., 2018) can be used to detect subgroups with different parameters in mixed-effects models. We apply GLMM trees to longitudinal data from a study on the effects of the Head Start pre-school program to identify subgroups of families where children show comparatively larger or smaller gains in performance.