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

Marjolein Fokkema1, Mirka Henninger2, Carolin Strobl3

  • 1Leiden University, Room number 3B20, Wassenaarseweg 52, 2333 AK Leiden, The Netherlands.

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Summary
This summary is machine-generated.

Model-based recursive partitioning (MOB) identifies subgroups with varying effects in parametric models. This approach, demonstrated with generalized linear mixed models and item response theory, aids in understanding heterogeneity in educational studies.

Keywords:
Decision treesDifferential effectsMixed-effects modelsRasch modeling

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Area of Science:

  • Statistics
  • Educational Measurement
  • Psychometrics

Background:

  • Model-based recursive partitioning (MOB) offers a flexible framework for analyzing heterogeneity in parametric models.
  • Detecting and explaining subgroup differences is crucial in fields like intervention and educational studies.
  • Existing methods may not fully capture complex heterogeneity patterns in mixed-effects or item response theory models.

Purpose of the Study:

  • To introduce the general Model-based recursive partitioning (MOB) framework.
  • To illustrate the application of MOB-based methods for detecting and explaining heterogeneity in generalized linear mixed models (GLMM) and item response theory (IRT) frameworks within educational research.
  • To demonstrate the utility of GLMM trees and Rasch trees for subgroup detection.

Main Methods:

  • The study utilizes Model-based recursive partitioning (MOB) as a general framework.
  • Specific MOB extensions, GLMM trees and Rasch trees, are applied to analyze heterogeneity.
  • A novel stopping criterion for Rasch trees, based on differential item functioning (DIF) effect sizes, is employed.

Main Results:

  • GLMM trees successfully identified subgroups with differing parameters in mixed-effects models, applied to longitudinal Head Start data to reveal performance gain variations.
  • Rasch trees detected subgroups exhibiting differential item functioning (DIF) in IRT models, highlighting the importance of DIF analysis before group comparisons.
  • The new stopping criterion effectively guided subgroup detection based on DIF effect sizes.

Conclusions:

  • MOB provides a powerful and versatile tool for uncovering and interpreting subgroup heterogeneity in educational studies.
  • GLMM trees and Rasch trees are effective extensions of MOB for analyzing complex data structures in mixed-effects models and IRT.
  • The integration of a DIF-based stopping criterion enhances the practical application of Rasch trees for robust subgroup analysis.