Bayesian Estimation of the DINA Model With Gibbs Sampling. (October 2015)

• Record Type: Journal Article
• Authors: Culpepper, Steven Andrew
• Is Part Of: Journal of educational and behavioral statistics. Volume 40:Number 5(2015)
• Page Start: 454
• Page End: 476
• Publication Date: 2015-10
• Subjects: cognitive diagnosis -- bayesian statistics -- markov chain monte carlo -- spatial cognition; Educational statistics -- Periodicals; Social sciences -- Statistical methods -- Periodicals; 370.2
• DOI: 10.3102/1076998615595403
• Languages: English
• ISSNs: 1076-9986

Abstract

A Bayesian model formulation of the deterministic inputs, noisy "and" gate (DINA) model is presented. Gibbs sampling is employed to simulate from the joint posterior distribution of item guessing and slipping parameters, subject attribute parameters, and latent class probabilities. The procedure extends concepts in Béguin and Glas, Culpepper, and Sahu for estimating the guessing and slipping parameters in the three- and four-parameter normal-ogive models. The ability of the model to recover parameters is demonstrated in a simulation study. The technique is applied to a mental rotation test. The algorithm and vignettes are freely available to researchers as the "dina" R package.