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

IF 3.8 1区 心理学 Q1 PSYCHOLOGY, SOCIAL Journal of School Psychology Pub Date : 2025-01-16 DOI:10.1016/j.jsp.2024.101394
Marjolein Fokkema , Mirka Henninger , Carolin Strobl
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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.
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来源期刊
Journal of School Psychology
Journal of School Psychology PSYCHOLOGY, EDUCATIONAL-
CiteScore
6.70
自引率
8.00%
发文量
71
期刊介绍: The Journal of School Psychology publishes original empirical articles and critical reviews of the literature on research and practices relevant to psychological and behavioral processes in school settings. JSP presents research on intervention mechanisms and approaches; schooling effects on the development of social, cognitive, mental-health, and achievement-related outcomes; assessment; and consultation. Submissions from a variety of disciplines are encouraged. All manuscripts are read by the Editor and one or more editorial consultants with the intent of providing appropriate and constructive written reviews.
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Editorial Board Mixed methods systematic review: Using a cultural validity assessment to evaluate prevention programs for Indigenous students Using a naive Bayesian approach to identify academic risk based on multiple sources: A conceptual replication Learning from learning loss: Bayesian updating in academic universal screening during learning disruptions One model may not fit all: Subgroup detection using model-based recursive partitioning
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