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