Assessing Growth in a Diagnostic Classification Model Framework

Matthew J. Madison () and Laine P. Bradshaw ()
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Matthew J. Madison: Clemson University
Laine P. Bradshaw: University of Georgia

Psychometrika, 2018, vol. 83, issue 4, No 10, 963-990

Abstract: 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 model; cognitive diagnosis model; latent transition analysis; item parameter drift; measurement invariance; growth; pre-test/post-test design (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (7)

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DOI: 10.1007/s11336-018-9638-5

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