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

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 expec...

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Bibliographic Details
Published in:Journal of educational and behavioral statistics 2015-12, Vol.40 (6), p.547-578
Main Authors: Casabianca, Jodi M., Lewis, Charles
Format: Article
Language:English
Subjects:
Algorithms
Comparative Analysis
Computation
Foreign Countries
International Assessment
Item Response Theory
Mathematical models
Maximum likelihood method
Maximum Likelihood Statistics
Monte Carlo Methods
Normal distribution
Parameter estimation
Undergraduate Students
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Summary: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 expectationmaximization 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.
ISSN:1076-9986
1935-1054
DOI:10.3102/1076998615606112