Using JAGS for Bayesian Cognitive Diagnosis Modeling: A Tutorial

被引:42
|
作者
Zhan, Peida [1 ]
Jiao, Hong [2 ]
Man, Kaiwen [2 ]
Wang, Lijun [1 ]
机构
[1] Zhejiang Normal Univ, Coll Teacher Educ, 688 Yingbin Rd, Jinhua 321004, Zhejiang, Peoples R China
[2] Univ Maryland, Dept Human Dev & Quantitat Methodol, 1230C Benjamin Bldg, College Pk, MD 20742 USA
关键词
cognitive diagnosis modeling; Bayesian estimation; Markov chain Monte Carlo; DINA model; DINO model; rRUM; testlet; longitudinal diagnosis; polytomous attributes; HIDDEN MARKOV MODEL; DINA MODEL; HIGHER-ORDER; FIT;
D O I
10.3102/1076998619826040
中图分类号
G40 [教育学];
学科分类号
040101 ; 120403 ;
摘要
In this article, we systematically introduce the just another Gibbs sampler (JAGS) software program to fit common Bayesian cognitive diagnosis models (CDMs) including the deterministic inputs, noisy and gate model; the deterministic inputs, noisy or gate model; the linear logistic model; the reduced reparameterized unified model; and the log-linear CDM (LCDM). Further, we introduce the unstructured latent structural model and the higher order latent structural model. We also show how to extend these models to consider polytomous attributes, the testlet effect, and longitudinal diagnosis. Finally, we present an empirical example as a tutorial to illustrate how to use JAGS codes in R.
引用
收藏
页码:473 / 503
页数:31
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