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- Title
Approximate Invariance Testing in Diagnostic Classification Models in the Presence of Attribute Hierarchies: A Bayesian Network Approach.
- Authors
Martinez, Alfonso J.; Templin, Jonathan
- 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.
- Subjects
PRINCIPLE of relativity (Physics); BAYESIAN analysis; STRUCTURAL models; DIAGNOSIS; REGRESSION analysis
- Publication
Psych, 2023, Vol 5, Issue 3, p688
- ISSN
2624-8611
- Publication type
Article
- DOI
10.3390/psych5030045