Journal article
Approximate Invariance Testing in Diagnostic Classification Models in the Presence of Attribute Hierarchies: A Bayesian Network Approach
Psych, Vol.5(3), pp.688-714
07/13/2023
DOI: 10.3390/psych5030045
Abstract
This paper demonstrates the process of invariance testing in diagnostic classification models in the presence of attribute hierarchies via an extension of the log-linear cognitive diagnosis model (LCDM). This extension allows researchers to test for measurement (item) invariance as well as attribute (structural) invariance simultaneously in a single analysis. The structural model of the LCDM was parameterized as a Bayesian network, which allows attribute hierarchies to be modeled and tested for attribute invariance via a series of latent regression models. We illustrate the steps for carrying out the invariance analyses through an in-depth case study with an empirical dataset and provide JAGS code for carrying out the analysis within the Bayesian framework. The analysis revealed that a subset of the items exhibit partial invariance, and evidence of full invariance was found at the structural level.
Details
- Title: Subtitle
- Approximate Invariance Testing in Diagnostic Classification Models in the Presence of Attribute Hierarchies: A Bayesian Network Approach
- Creators
- Alfonso J. Martinez - University of IowaJonathan Templin - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Psych, Vol.5(3), pp.688-714
- DOI
- 10.3390/psych5030045
- ISSN
- 2624-8611
- eISSN
- 2624-8611
- Language
- English
- Date published
- 07/13/2023
- Academic Unit
- Psychological and Quantitative Foundations
- Record Identifier
- 9984445076702771
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