Bridging Parametric and Nonparametric Methods in Cognitive Diagnosis

Chenchen Ma, Jimmy Torre and Gongjun Xu ()
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Chenchen Ma: University of Michigan
Jimmy Torre: University of Hong Kong
Gongjun Xu: University of Michigan

Psychometrika, 2023, vol. 88, issue 1, No 3, 75 pages

Abstract: Abstract A number of parametric and nonparametric methods for estimating cognitive diagnosis models (CDMs) have been developed and applied in a wide range of contexts. However, in the literature, a wide chasm exists between these two families of methods, and their relationship to each other is not well understood. In this paper, we propose a unified estimation framework to bridge the divide between parametric and nonparametric methods in cognitive diagnosis to better understand their relationship. We also develop iterative joint estimation algorithms and establish consistency properties within the proposed framework. Lastly, we present comprehensive simulation results to compare different methods and provide practical recommendations on the appropriate use of the proposed framework in various CDM contexts.

Keywords: cognitive diagnosis; likelihood estimation; nonparametric estimation (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s11336-022-09878-2

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