Marginalized Maximum Likelihood Estimation for the 1PL-AG IRT Model

Marginal maximum likelihood estimation based on the expectation–maximization algorithm (MML/EM) is developed for the one-parameter logistic model with ability-based guessing (1PL-AG) item response theory (IRT) model. The use of the MML/EM estimator is cross-validated with estimates from NLMIXED proc...

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Bibliographic Details
Published in:Applied psychological measurement 2015-09, Vol.39 (6), p.448-464
Main Authors: Park, Ryoungsun, Pituch, Keenan A., Kim, Jiseon, Dodd, Barbara G., Chung, Hyewon
Format: Article
Language:English
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Summary:Marginal maximum likelihood estimation based on the expectation–maximization algorithm (MML/EM) is developed for the one-parameter logistic model with ability-based guessing (1PL-AG) item response theory (IRT) model. The use of the MML/EM estimator is cross-validated with estimates from NLMIXED procedure (PROC NLMIXED) in Statistical Analysis System. Numerical data are provided for comparisons of results from MML/EM and PROC NLMIXED.
ISSN:0146-6216
1552-3497
DOI:10.1177/0146621615574694