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Investigating heterogeneity in IRTree models for multiple response processes with score-based partitioning.

Rudolf Debelak¹, Thorsten Meiser², Alicia Gernand³

¹University of Zurich, Zurich, Switzerland.

The British Journal of Mathematical and Statistical Psychology

|November 4, 2024

Summary

This summary is machine-generated.

This study introduces a new method to detect variations in item response tree (IRT) model parameters across different respondent groups. This approach helps identify sources of differing response behaviors in psychometric analyses.

Keywords:

IRTree models item response theory model‐based recursive partitioning parameter heterogeneity response styles score‐based tests

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Area of Science:

Psychometrics
Statistical modeling

Background:

Item response tree (IRT) models measure latent traits while accounting for response processes.
A key assumption of IRT models is the homogeneity of response processes across all respondents.
Detecting heterogeneity in these processes is crucial for accurate measurement.

Purpose of the Study:

To propose a novel method for detecting parameter heterogeneity in IRT models.
To develop a model-based partitioning algorithm to identify sources of differing response behaviors.
To address the limitation of assumed homogeneity in IRT models.

Main Methods:

Utilizing score-based tests to detect violations of parameter homogeneity.
Applying extraneous person covariates to identify heterogeneity sources.
Employing a partitioning algorithm for subgroup analysis.

Main Results:

Simulation studies confirmed accurate Type I error rates and sufficient power.
The method effectively differentiates between various types of parameter heterogeneity.
The approach demonstrated utility with metric, ordinal, and categorical person covariates.

Conclusions:

The proposed score-based partitioning approach effectively detects parameter heterogeneity in IRT models.
This method allows for the identification of subgroups with distinct response behaviors.
Empirical application confirmed the practical utility of the approach in analyzing latent response processes.