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

Debelak, Rudolf; Meiser, Thorsten; Gernand, Alicia (2025). Investigating heterogeneity in IRTree models for multiple response processes with score‐based partitioning. British Journal of Mathematical and Statistical Psychology, 78(2):420-439.

Abstract

Item response tree (IRTree) models form a family of psychometric models that allow researchers to control for multiple response processes, such as different sorts of response styles, in the measurement of latent traits. While IRTree models can capture quantitative individual differences in both the latent traits of interest and the use of response categories, they maintain the basic assumption that the nature and weighting of latent response processes are homogeneous across the entire population of respondents. In the present research, we therefore propose a novel approach for detecting heterogeneity in the parameters of IRTree models across subgroups that engage in different response behavior. The approach uses score‐based tests to reveal violations of parameter heterogeneity along extraneous person covariates, and it can be employed as a model‐based partitioning algorithm to identify sources of differences in the strength of trait‐based responding or other response processes. Simulation studies demonstrate generally accurate Type I error rates and sufficient power for metric, ordinal, and categorical person covariates and for different types of test statistics, with the potential to differentiate between different types of parameter heterogeneity. An empirical application illustrates the use of score‐based partitioning in the analysis of latent response processes with real data.

Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:06 Faculty of Arts > Institute of Education
06 Faculty of Arts > Institute of Psychology
Dewey Decimal Classification:370 Education
150 Psychology
Scopus Subject Areas:Physical Sciences > Statistics and Probability
Social Sciences & Humanities > Arts and Humanities (miscellaneous)
Social Sciences & Humanities > General Psychology
Uncontrolled Keywords:IRTree models, item response theory, model-based recursive partitioning, parameter heterogeneity, response styles, score-based tests
Language:English
Date:1 May 2025
Deposited On:16 Jan 2025 10:30
Last Modified:31 May 2025 01:37
Publisher:Wiley-Blackwell Publishing, Inc.
ISSN:0007-1102
OA Status:Hybrid
Publisher DOI:https://doi.org/10.1111/bmsp.12367
Project Information:
  • Funder: DFG
  • Grant ID: 2277
  • Project Title: Statistical Modeling in Psychology
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