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Assessing Growth in a Diagnostic Classification Model Framework

Published online by Cambridge University Press:  01 January 2025

Matthew J. Madison  and
Laine P. Bradshaw
Matthew J. Madison*
Affiliation:
Clemson University
Laine P. Bradshaw
Affiliation:
University of Georgia
*
Correspondence should be made to Matthew J. Madison, Department of Education and Human Development, Clemson University, 226 Holtzendorff Hall, Clemson, SC 29634, USA. Email: [email protected]
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Abstract

A common assessment research design is the single-group pre-test/post-test design in which examinees are administered an assessment before instruction and then another assessment after instruction. In this type of study, the primary objective is to measure growth in examinees, individually and collectively. In an item response theory (IRT) framework, longitudinal IRT models can be used to assess growth in examinee ability over time. In a diagnostic classification model (DCM) framework, assessing growth translates to measuring changes in attribute mastery status over time, thereby providing a categorical, criterion-referenced interpretation of growth. This study introduces the Transition Diagnostic Classification Model (TDCM), which combines latent transition analysis with the log-linear cognitive diagnosis model to provide methodology for analyzing growth in a general DCM framework. Simulation study results indicate that the proposed model is flexible, provides accurate and reliable classifications, and is quite robust to violations to measurement invariance over time. The TDCM is used to analyze pre-test/post-test data from a diagnostic mathematics assessment.

Keywords

diagnostic classification modelcognitive diagnosis modellatent transition analysisitem parameter driftmeasurement invariancegrowthpre-test/post-test design
Type
Original Paper
Information
Psychometrika , Volume 83 , Issue 4 , December 2018 , pp. 963 - 990
Copyright
Copyright © 2018 The Psychometric Society

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