2023
DOI: 10.3390/psych5030045
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Approximate Invariance Testing in Diagnostic Classification Models in the Presence of Attribute Hierarchies: A Bayesian Network Approach

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 invar… Show more

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“…The article by Martinez and Templin [16], titled "Approximate invariance testing in diagnostic classification models in the presence of attribute hierarchies: A Bayesian network approach", addresses invariance testing regarding items and attribute hierarchies in the log-linear DCM. The invariance testing steps are illustrated using JAGS code.…”
mentioning
confidence: 99%
“…The article by Martinez and Templin [16], titled "Approximate invariance testing in diagnostic classification models in the presence of attribute hierarchies: A Bayesian network approach", addresses invariance testing regarding items and attribute hierarchies in the log-linear DCM. The invariance testing steps are illustrated using JAGS code.…”
mentioning
confidence: 99%