Carolin Strobl

C. Strobl

Model-based recursive partitioning of psychometric models: A data-driven
approach for detecting heterogeneity in model parameters

Carolin Strobl
Universität Zürich

 

Abstract

Model-based recursive partitioning is a flexible framework for detecting
differences in model parameters between two or more groups of subjects.
Its origins lie in machine learning, where its predecessor methods,
classification and regression trees, had been introduced around the
1980s as a nonparametric regression technique. Today, after the
statistical flaws of the early algorithms have been overcome, their
extension to detecting heterogeneity in parametric models makes
recursive partitioning methods a valuable addition to the statistical
“toolbox” in various areas of application, including econometrics and
psychometrics. This talk gives an overview about the rationale and
statistical background of model-based recursive partitioning in general
and in particular with extensions to psychometric models for paired
comparisons as well as item response models. In this context, the
data-driven approach of model-based recursive partitioning proves to be
particularly suited for detecting violations of homogeneity or
invariance, such as differential item functioning, where we usually have
no a priori hypotheses about the underlying group structure.

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