A Polytomous Model of Cognitive Diagnostic Assessment for Graded Data

Dongbo Tu; Chanjin Zheng; Yan Cai; Xuliang Gao; Daxun Wang

doi:10.1080/15305058.2017.1396465

A Polytomous Model of Cognitive Diagnostic Assessment for Graded Data

Dongbo Tu, Chanjin Zheng, Yan Cai, Xuliang Gao, Daxun Wang

Jiangxi Normal University

Research output: Contribution to journal › Article › peer-review

14 Scopus citations

Abstract

Pursuing the line of the difference models in IRT (Thissen & Steinberg, 1986), this article proposed a new cognitive diagnostic model for graded/polytomous data based on the deterministic input, noisy, and gate (Haertel, 1989; Junker & Sijtsma, 2001), which is named the DINA model for graded data (DINA-GD). We investigated the performance of a full Bayesian estimation of the proposed model. In the simulation, the classification accuracy and item recovery for the DINA-GD model were investigated. The results indicated that the proposed model had acceptable examinees' correct attribute classification rate and item parameter recovery. In addition, a real-data example was used to illustrate the application of this new model with the graded data or polytomously scored items.

Original language	English
Pages (from-to)	231-252
Number of pages	22
Journal	International Journal of Testing
Volume	18
Issue number	3
DOIs	https://doi.org/10.1080/15305058.2017.1396465
State	Published - 3 Jul 2018
Externally published	Yes

Keywords

DINA model
cognitive diagnostic assessment
graded response model
item response theory
latent response

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10.1080/15305058.2017.1396465

Cite this

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title = "A Polytomous Model of Cognitive Diagnostic Assessment for Graded Data",

abstract = "Pursuing the line of the difference models in IRT (Thissen \& Steinberg, 1986), this article proposed a new cognitive diagnostic model for graded/polytomous data based on the deterministic input, noisy, and gate (Haertel, 1989; Junker \& Sijtsma, 2001), which is named the DINA model for graded data (DINA-GD). We investigated the performance of a full Bayesian estimation of the proposed model. In the simulation, the classification accuracy and item recovery for the DINA-GD model were investigated. The results indicated that the proposed model had acceptable examinees' correct attribute classification rate and item parameter recovery. In addition, a real-data example was used to illustrate the application of this new model with the graded data or polytomously scored items.",

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TY - JOUR

T1 - A Polytomous Model of Cognitive Diagnostic Assessment for Graded Data

AU - Tu, Dongbo

AU - Zheng, Chanjin

AU - Cai, Yan

AU - Gao, Xuliang

AU - Wang, Daxun

PY - 2018/7/3

Y1 - 2018/7/3

N2 - Pursuing the line of the difference models in IRT (Thissen & Steinberg, 1986), this article proposed a new cognitive diagnostic model for graded/polytomous data based on the deterministic input, noisy, and gate (Haertel, 1989; Junker & Sijtsma, 2001), which is named the DINA model for graded data (DINA-GD). We investigated the performance of a full Bayesian estimation of the proposed model. In the simulation, the classification accuracy and item recovery for the DINA-GD model were investigated. The results indicated that the proposed model had acceptable examinees' correct attribute classification rate and item parameter recovery. In addition, a real-data example was used to illustrate the application of this new model with the graded data or polytomously scored items.

AB - Pursuing the line of the difference models in IRT (Thissen & Steinberg, 1986), this article proposed a new cognitive diagnostic model for graded/polytomous data based on the deterministic input, noisy, and gate (Haertel, 1989; Junker & Sijtsma, 2001), which is named the DINA model for graded data (DINA-GD). We investigated the performance of a full Bayesian estimation of the proposed model. In the simulation, the classification accuracy and item recovery for the DINA-GD model were investigated. The results indicated that the proposed model had acceptable examinees' correct attribute classification rate and item parameter recovery. In addition, a real-data example was used to illustrate the application of this new model with the graded data or polytomously scored items.

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KW - item response theory

KW - latent response

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DO - 10.1080/15305058.2017.1396465

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A Polytomous Model of Cognitive Diagnostic Assessment for Graded Data

Abstract

Keywords

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