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Assessing Growth in a Diagnostic Classification Model Framework

Author

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  • Matthew J. Madison

    (Clemson University)

  • Laine P. Bradshaw

    (University of Georgia)

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.

Suggested Citation

  • Matthew J. Madison & Laine P. Bradshaw, 2018. "Assessing Growth in a Diagnostic Classification Model Framework," Psychometrika, Springer;The Psychometric Society, vol. 83(4), pages 963-990, December.
    • Handle: RePEc:spr:psycho:v:83:y:2018:i:4:d:10.1007_s11336-018-9638-5
    DOI: 10.1007/s11336-018-9638-5
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    References listed on IDEAS

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    Cited by:

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    2. Steven Andrew Culpepper, 2019. "An Exploratory Diagnostic Model for Ordinal Responses with Binary Attributes: Identifiability and Estimation," Psychometrika, Springer;The Psychometric Society, vol. 84(4), pages 921-940, December.
    3. Joemari Olea & Kevin Carl Santos, 2024. "A General Mixture Model for Cognitive Diagnosis," Journal of Educational and Behavioral Statistics, , vol. 49(2), pages 268-307, April.
    4. Yinghan Chen & Ying Liu & Steven Andrew Culpepper & Yuguo Chen, 2021. "Inferring the Number of Attributes for the Exploratory DINA Model," Psychometrika, Springer;The Psychometric Society, vol. 86(1), pages 30-64, March.
    5. Kazuhiro Yamaguchi, 2023. "Bayesian Analysis Methods for Two-Level Diagnosis Classification Models," Journal of Educational and Behavioral Statistics, , vol. 48(6), pages 773-809, December.
    6. Steven Andrew Culpepper, 2023. "A Note on Weaker Conditions for Identifying Restricted Latent Class Models for Binary Responses," Psychometrika, Springer;The Psychometric Society, vol. 88(1), pages 158-174, March.
    7. Kazuhiro Yamaguchi & Jonathan Templin, 2022. "A Gibbs Sampling Algorithm with Monotonicity Constraints for Diagnostic Classification Models," Journal of Classification, Springer;The Classification Society, vol. 39(1), pages 24-54, March.

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