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The Application of Latent Class Models in Adaptive Testing

Published online by Cambridge University Press:  01 January 2025

George B. Macready and
C. Mitchell Dayton
George B. Macready*
Affiliation:
University of Maryland
C. Mitchell Dayton
Affiliation:
University of Maryland
*
Requests for reprints should be sent to George Macready, Department of Measurement, Statistics, and Evaluation, College of Education, University of Maryland, College Park, MD 20742.
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Abstract

This paper provides a description of a new adaptive testing algorithm based on a latent class modeling framework. The algorithm incorporates a four-stage iterative procedure that conditionally minimizes expected loss in classification of respondents across different content domains. The classification decisions relate to the membership of a person in a category of a latent variable for each of the separate domains considered. The algorithm appears to be particularly effective when latent class membership is related across the various domains of interest, since classification decisions on domains assessed early in the process are used to revise the probabilities for latent class membership on domains for which classification decisions have not yet been made. To assess the effectiveness of the proposed algorithm, a simulation based upon real data was conducted. For this example, the algorithm proved to be relatively efficient, requiring only 40% of the number of items needed under a nonadaptive approach. In addition, the algorithm provided classification decisions which, in 96% of the cases, were consistent with decisions based upon all available items when the maximum acceptable classification error rate was set at 5%.

Keywords

latent class modelslatent structure analysisadaptive testing

Information

Type
Original Paper
Information
Psychometrika , Volume 57 , Issue 1 , March 1992, pp. 71 - 88
Copyright
Copyright © 1992 The Psychometric Society

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