A Longitudinal Higher-Order Diagnostic Classification Model

Peida Zhan, Hong Jiao, Dandan Liao and Feiming Li
Additional contact information
Peida Zhan: Zhejiang Normal University
Hong Jiao: University of Maryland
Dandan Liao: American Institutes for Research
Feiming Li: Zhejiang Normal University

Journal of Educational and Behavioral Statistics, 2019, vol. 44, issue 3, 251-281

Abstract: Providing diagnostic feedback about growth is crucial to formative decisions such as targeted remedial instructions or interventions. This article proposed a longitudinal higher-order diagnostic classification modeling approach for measuring growth. The new modeling approach is able to provide quantitative values of overall and individual growth by constructing a multidimensional higher-order latent structure to take into account the correlations among multiple latent attributes that are examined across different occasions. In addition, potential local item dependence among anchor (or repeated) items can be taken into account. Model parameter estimation is explored in a simulation study. An empirical example is analyzed to illustrate the applications and advantages of the proposed modeling approach.

Keywords: cognitive diagnosis; diagnostic classification model; longitudinal data; anchor-item; local item dependence; DINA (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:jedbes:v:44:y:2019:i:3:p:251-281

DOI: 10.3102/1076998619827593

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