Parameter Estimation of the Reduced RUM Using the EM Algorithm

Diagnostic classification models (DCMs) are psychometric models widely discussed by researchers nowadays because of their promising feature of obtaining detailed information on students’ mastery on specific attributes. Model estimation is essential for further implementation of these models, and est...

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Bibliographic Details
Published in:Applied psychological measurement Vol. 38; no. 2; pp. 137 - 150
Main Authors: Feng, Yuling, Habing, Brian T., Huebner, Alan
Format: Journal Article
Language:English
Published: Los Angeles, CA SAGE Publications 01.03.2014
Subjects:
Algorithms
parameter estimation
diagnostic classification models
the reduced RUM
expectation-maximization
ISSN:0146-6216, 1552-3497
Online Access:Get full text
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Summary:Diagnostic classification models (DCMs) are psychometric models widely discussed by researchers nowadays because of their promising feature of obtaining detailed information on students’ mastery on specific attributes. Model estimation is essential for further implementation of these models, and estimation methods are often developed within some general framework, such as generalized diagnostic model (GDM) of von Davier, the log-linear diagnostic classification model (LDCM), and the generalized deterministic input, noisy-and-gate (G-DINA). Using a maximum likelihood estimation algorithm, this article addresses the estimation issue of a complex compensatory DCM, the reduced reparameterized unified model (rRUM), whose estimation under general frameworks could be lengthy due to the complexity of the model. The proposed estimation method is demonstrated on simulated data as well as a real data set, and is shown to provide accurate item parameter estimates for the rRUM.
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ISSN:0146-6216
1552-3497
DOI:10.1177/0146621613502704