A convexity‐constrained parameterization of the random effects generalized partial credit model

An alternative closed-form expression for the marginal joint probability distribution of item scores under the random effects generalized partial credit model is presented. The closed-form expression involves a cumulant generating function and is therefore subjected to convexity constraints. As a consequence, complicated moment inequalities are taken into account in maximum likelihood estimation of the parameters of the model, so that the estimation solution is always proper. Another important favorable consequence is that the likelihood function has a single local extreme point, the global maximum. Furthermore, attention is paid to expected a posteriori person parameter estimation, generalizations of the model, and testing the goodness-of-fit of the model. Procedures proposed are demonstrated in an illustrative example.

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Publication Details

Journal
British Journal of Mathematical and Statistical Psychology
Published
2024-10-27
DOI
https://doi.org/10.1111/bmsp.12365
Primary Topic
Psychometric Methodologies and Testing
Type
article
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article

A convexity‐constrained parameterization of the random effects generalized partial credit model

David J. Hessen
British Journal of Mathematical and Statistical Psychology
Psychometric Methodologies and Testing
article

A convexity‐constrained parameterization of the random effects generalized partial credit model

David J. Hessen
article en

Abstract

An alternative closed-form expression for the marginal joint probability distribution of item scores under the random effects generalized partial credit model is presented. The closed-form expression involves a cumulant generating function and is therefore subjected to convexity constraints. As a consequence, complicated moment inequalities are taken into account in maximum likelihood estimation of the parameters of the model, so that the estimation solution is always proper. Another important favorable consequence is that the likelihood function has a single local extreme point, the global maximum. Furthermore, attention is paid to expected a posteriori person parameter estimation, generalizations of the model, and testing the goodness-of-fit of the model. Procedures proposed are demonstrated in an illustrative example.

British Journal of Mathematical and Statistical PsychologyVol. 78(2)
Utrecht University (NL)
Reduced inequalities
Openalex Percentile: Top 77%
Psychometric Methodologies and Testing
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