Bayesian covariance structure modelling for measurement invariance testing
| Autor: | Remco Feskens, Jean-Paul Fox, Lukas Beinhauer, Jesse Koops |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Group (mathematics)
Applied Mathematics 05 social sciences Bayesian probability Structure (category theory) UT-Hybrid-D 050401 social sciences methods Experimental and Cognitive Psychology Bayes factor Random item parameters Covariance 01 natural sciences Correlation 010104 statistics & probability Clinical Psychology 0504 sociology Bayesian IRT BCSM Statistics Measurement invariance 0101 mathematics Structured model Analysis Mathematics |
| Zdroj: | Behaviormetrika, 47(2), 385-410. Behaviormetric Society of Japan |
| ISSN: | 0385-7417 |
| Popis: | In a Bayesian Covariance Structure Model (BCSM) the dependence structure implied by random item parameters is modelled directly through the covariance structure. The corresponding measurement invariance assumption for an item is represented by an additional correlation in the item responses in a group. The BCSM for measurement invariance testing is defined for mixed response types, where the additional correlation is tested with the Bayes factor. It is shown that measurement invariance can be tested simultaneously across items and thresholds for multiple groups. This avoids the risk of capitalization on chance that occurs in multiple-step procedures and avoids cumbersome procedures where items are examined sequentially. The proposed measurement invariance procedure is applied to PISA data, where the advantages of the method are illustrated. |
| Databáze: | OpenAIRE |
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