Complex Latent Variable Modeling in Educational Assessment

Bayesian item response theory models have been widely used in different research fields. They support measuring constructs and modeling relationships between constructs, while accounting for complex test situations (e.g., complex sampling designs, missing data, heterogenous population). Advantages o...

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Published inCommunications in statistics. Simulation and computation Vol. 45; no. 5; pp. 1499 - 1510
Main Authors Fox, Jean-Paul, Marsman, Maarten, Mulder, Joris, Verhagen, Josine
Format Journal Article
LanguageEnglish
Published Philadelphia Taylor & Francis 27.05.2016
Taylor & Francis Ltd
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Online AccessGet full text
ISSN0361-0918
1532-4141
1532-4141
DOI10.1080/03610918.2014.939518

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Abstract Bayesian item response theory models have been widely used in different research fields. They support measuring constructs and modeling relationships between constructs, while accounting for complex test situations (e.g., complex sampling designs, missing data, heterogenous population). Advantages of this flexible modeling framework together with powerful simulation-based estimation techniques are discussed. Furthermore, it is shown how the Bayes factor can be used to test relevant hypotheses in assessment using the College Basic Academic Subjects Examination (CBASE) data.
AbstractList Bayesian item response theory models have been widely used in different research fields. They support measuring constructs and modeling relationships between constructs, while accounting for complex test situations (e.g., complex sampling designs, missing data, heterogenous population). Advantages of this flexible modeling framework together with powerful simulation-based estimation techniques are discussed. Furthermore, it is shown how the Bayes factor can be used to test relevant hypotheses in assessment using the College Basic Academic Subjects Examination (CBASE) data.
Author Marsman, Maarten
Mulder, Joris
Verhagen, Josine
Fox, Jean-Paul
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Accounting
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Mathematical models
MCMC
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