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ERIC Number: EJ1174625
Record Type: Journal
Publication Date: 2018
Pages: 15
Abstractor: As Provided
ISSN: ISSN-1536-6367
Bayesian Analysis of Multidimensional Item Response Theory Models: A Discussion and Illustration of Three Response Style Models
Leventhal, Brian C.; Stone, Clement A.
Measurement: Interdisciplinary Research and Perspectives, v16 n2 p114-128 2018
Interest in Bayesian analysis of item response theory (IRT) models has grown tremendously due to the appeal of the paradigm among psychometricians, advantages of these methods when analyzing complex models, and availability of general-purpose software. Possible models include models which reflect multidimensionality due to designed test structure, construct-irrelevant variance, and mixed item formats. Using Markov Chain Monte Carlo methods, models can be estimated and evaluated for model-fit. In addition to discussing Bayesian analysis, analyses of three IRT models designed to account for extreme response style are illustrated: IRTree, multidimensional nominal response model (MNRM), and modified generalized partial credit model (MPCM). While results indicated there may be little impact of model choice on substantive trait estimates for the data set studied herein, model-fit results favored the MNRM and MPCM over the IRTree Model.
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Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Grant or Contract Numbers: N/A