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

Journal of School Psychology, vol. 109, pp. 101394

Abstract

Model-based recursive partitioning (MOB; Zeileis et al., 2008) is a flexible framework for detecting subgroups of persons showing different effects in a wide range of parametric models. 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. In a second case study, we show how Rasch trees (Strobl et al., 2015) can be used to detect subgroups with different item parameters in IRT models (i.e. differential item functioning [DIF]). DIF should be investigated before using test results for group comparisons. We show how a recently developed stopping criterion (Henninger et al., 2023) can be used to guide subgroup detection based on DIF effect sizes.

Authors 3

1. Marjolein Fokkema Aachen

   Leiden University

   Affiliation as printed

   Leiden University, Room number 3B20, Wassenaarseweg 52, 2333 AK Leiden, The Netherlands. Electronic address: m.fokkema@fsw.leidenuniv.nl

2. Mirka Henninger

   University of Zurich · University of Basel

   Affiliation as printed

   University of Zurich, Binzmuehlestrasse 14, Box 27, 8050 Zurich, Switzerland; University of Basel, Missionsstrasse 62A, 4055 Basel, Switzerland

3. Carolin Strobl

   University of Zurich

   Affiliation as printed

   University of Zurich, Binzmuehlestrasse 14, Box 27, 8050 Zurich, Switzerland