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ERIC Number: EJ1084527
Record Type: Journal
Publication Date: 2015-Dec
Pages: 32
Abstractor: As Provided
ISSN: ISSN-1076-9986
IRT Item Parameter Recovery with Marginal Maximum Likelihood Estimation Using Loglinear Smoothing Models
Casabianca, Jodi M.; Lewis, Charles
Journal of Educational and Behavioral Statistics, v40 n6 p547-578 Dec 2015
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.
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Publication Type: Journal Articles; Reports - Research
Education Level: Higher Education; Postsecondary Education
Audience: N/A
Language: English
Sponsor: Institute of Education Sciences (ED)
Authoring Institution: N/A
Identifiers - Location: China (Shanghai); North Carolina
Identifiers - Assessments and Surveys: Program for International Student Assessment
IES Funded: Yes
Grant or Contract Numbers: R305B100012