Modeling Changes in Response Style with Longitudinal IRTree Models

Traditional psychometric modeling focuses on observed categorical item responses, which can over-simplify the respondent cognitive response process. A further weakness is that analysis of ordinal responses has been primarily limited to a single substantive trait at one time point. We propose a signi...

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Bibliographic Details
Published in:Multivariate behavioral research 2022-09, Vol.57 (5), p.859-878
Main Authors: Ames, Allison J., Leventhal, Brian C.
Format: Article
Language:English
Subjects:
Bayesian estimation
Empirical analysis
item response tree
longitudinal model
Longitudinal studies
Mathematical models
Modelling
Parameters
Recovery
Response style
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Summary:Traditional psychometric modeling focuses on observed categorical item responses, which can over-simplify the respondent cognitive response process. A further weakness is that analysis of ordinal responses has been primarily limited to a single substantive trait at one time point. We propose a significant expansion of this modeling framework to account for complex response processes across multiple waves of data collection using the beneficial item response tree framework. This study proposes a novel model, the longitudinal IRTree, for response processes in longitudinal studies, and investigates whether the response style changes are proportional to changes in the substantive trait of interest. A simulation study demonstrates adequate item parameter recovery in a Bayesian framework, especially with larger sample sizes of 2000. The longitudinal change parameters were recovered similarly well, with improved recovery using informative priors over default priors in Mplus. The empirical application demonstrates that relatively stable observed scores are due to a decrease in response styles offsetting an increase in the latent trait of interest.
ISSN:0027-3171
1532-7906
DOI:10.1080/00273171.2021.1920361