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Article

Using a Bayesian Estimation to Examine Attribute Hierarchies of the 2007 TIMSS Mathematics Test: A Demonstration Using R Packages

Authors
  • Chia-Ling Hsu (Hong Kong Examinations and Assessment Authority)
  • Yi-Hsin Chen (University of South Florida)
  • Yi-Jhen Wu (Technische Universität Dortmund)

Abstract

Correct specifications of hierarchical attribute structures in analyses using diagnostic classification models (DCMs) are pivotal because misspecifications can lead to biased parameter estimations and inaccurate classification profiles. This research is aimed at demonstrating DCM analyses with various hierarchical attribute structures via Bayesian estimation using freely available R packages, including CDM and R2jags. We illustrated a step-by-step procedure in R with an eighth-grade mathematics test from the 2007 Trends in International Mathematics and Science Study (TIMSS).

Keywords: hierarchical attribute structure, cognitive diagnosis, Bayesian estimation, LCDM, TIMSS

How to Cite:

Hsu, C., Chen, Y. & Wu, Y., (2023) “Using a Bayesian Estimation to Examine Attribute Hierarchies of the 2007 TIMSS Mathematics Test: A Demonstration Using R Packages”, Practical Assessment, Research, and Evaluation 28(1): 11. doi: https://doi.org/10.7275/pare.1265

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Published on
23 Aug 2023
Peer Reviewed
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