An Empirical Q-Matrix Validation Method for the Polytomous G-DINA Model

Jimmy de la Torre (), Xue-Lan Qiu and Kevin Carl Santos
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Jimmy de la Torre: The University of Hong Kong
Xue-Lan Qiu: The University of Hong Kong
Kevin Carl Santos: University of the Philippines

Psychometrika, 2022, vol. 87, issue 2, No 12, 693-724

Abstract: Abstract A number of empirically based Q-matrix validation methods are available in the literature, all of which were developed for cognitive diagnosis models (CDMs) involving dichotomous attributes. However, in many applications, it is more instructionally relevant to classify students into more than two categories (e.g., no mastery, basic mastery, and advanced mastery). To extend the practical utility of CDMs, methods for validating the Q-matrix for CDMs that measure polytomous attributes are needed. This study focuses on validating the Q-matrix of the generalized deterministic input, noisy, “and” gate model for polytomous attributes (pG-DINA). The pGDI, an extension of the G-DINA model discrimination index, is proposed for polytomous attributes. The pGDI serves as the basis of a validation method that can be used not only to identify potential misspecified q-entries, but also to suggest more appropriate attribute-level specifications. The theoretical properties of the pGDI are underpinned by several mathematical proofs, whereas its practical viability is examined using simulation studies covering various conditions. The results show that the method can accurately identify misspecified q-entries and suggest the correct attribute-level specifications, particularly when high-quality items are involved. The pGDI is applied to a proportional reasoning test that measures several polytomous attributes.

Keywords: cognitive diagnosis models; G-DINA; Q-matrix validation; polytomous attributes (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s11336-021-09821-x

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