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ERIC Number: EJ923896
Record Type: Journal
Publication Date: 2011-Jun
Pages: 13
Abstractor: As Provided
Reference Count: 24
ISSN: ISSN-0146-6216
Estimating a Noncompensatory IRT Model Using Metropolis within Gibbs Sampling
Babcock, Ben
Applied Psychological Measurement, v35 n4 p317-329 Jun 2011
Relatively little research has been conducted with the noncompensatory class of multidimensional item response theory (MIRT) models. A Monte Carlo simulation study was conducted exploring the estimation of a two-parameter noncompensatory item response theory (IRT) model. The estimation method used was a Metropolis-Hastings within Gibbs algorithm that accepted or rejected new parameters in a bivariate fashion. Results showed that acceptable estimation of the noncompensatory model required a sample size of 4,000 people, six unidimensional items per dimension, and latent traits that are not highly correlated. Although the data requirements to estimate this model are a bit daunting, future advances in methodology could make this model valuable for modeling multidimensional data where the latent traits are not expected to be highly correlated. (Contains 5 tables and 2 figures.)
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Publication Type: Journal Articles; Reports - Evaluative
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A