What question did this study set out to answer?

To evaluate the performance of loglinear smoothing embedded within marginal maximum likelihood estimation for recovering item response theory item parameters.

December 1, 2015Journal of Educational and Behavioral Statistics

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

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Authors

JCJodi M. CasabiancaRadiometrics (United States)CLCharles LewisShandong University

Discussion

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Implication

Simulation study demonstrates superior IRT item parameter recovery using four- or five-moment loglinear smoothing models, indicating enhanced flexibility and precision over standard approaches.

Key Points

To evaluate the performance of loglinear smoothing embedded within marginal maximum likelihood estimation for recovering item response theory item parameters.
Derived an expectation-maximization algorithm for marginal maximum likelihood estimation incorporating loglinear smoothing.
Conducted a simulation study using a 3-parameter logistic model comparing loglinear smoothing against a fixed normal distribution and an empirical histogram solution.
Applied the approach to empirical datasets alongside comparisons with Ramsay-curve item response theory.
Loglinear smoothing models matching four or five moments were optimal for item parameter recovery across most simulation conditions.
Loglinear smoothing yielded more precise parameter estimates than standard marginal maximum likelihood by avoiding overly rigid latent trait distribution assumptions while preserving parsimony.

Cite This Study

Casabianca et al. (2015) studied this question.

synapsesocial.com/papers/6a87b0977d1a7154c5015129 https://doi.org/10.3102/1076998615606112

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Implication

Simulation study demonstrates superior IRT item parameter recovery using four- or five-moment loglinear smoothing models, indicating enhanced flexibility and precision over standard approaches.

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

Discussion

Member takes

Implication

Key Points

Cite This Study

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

Discussion

Member takes

Implication

Key Points

Cite This Study

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