IRT Item Parameter Recovery With Marginal Maximum Likelihood Estimation Using Loglinear Smoothing Models

Jodi M. Casabianca and Charles Lewis
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Jodi M. Casabianca: The University of Texas at Austin
Charles Lewis: Fordham University

Journal of Educational and Behavioral Statistics, 2015, vol. 40, issue 6, 547-578

Abstract: Loglinear smoothing (LLS) estimates the latent trait distribution while making fewer assumptions about its form and maintaining parsimony, thus leading to more precise item response theory (IRT) item parameter estimates than standard marginal maximum likelihood (MML). This article provides the expectation-maximization algorithm for MML estimation with LLS embedded and compares LLS to other latent trait distribution specifications, a fixed normal distribution, and the empirical histogram solution, in terms of IRT item parameter recovery. Simulation study results using a 3-parameter logistic model reveal that LLS models matching four or five moments are optimal in most cases. Examples with empirical data compare LLS to these approaches as well as Ramsay-curve IRT.

Keywords: item response theory; nonnormal distributions; marginal maximum likelihood; latent trait distribution; quadrature; loglinear smoothing; moments; EM algorithm (search for similar items in EconPapers)
Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:sae:jedbes:v:40:y:2015:i:6:p:547-578

DOI: 10.3102/1076998615606112

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