Bayesian Estimation of the DINA Model With Pólya-Gamma Gibbs Sampling
Authors:
Open Access
- 10 March 2020
- journal article
- research article
- Published by Frontiers Media SA in Frontiers in Psychology
- Vol. 11, 384
Abstract
With the increasing demanding for precision of test feedback, cognitive diagnosis models have attracted more and more attention to fine classify students whether has mastered some skills. The purpose of this paper is to propose a highly effective Pólya-Gamma Gibbs sampling algorithm (Polson et al., 2013) based on auxiliary variables to estimate the deterministic inputs, noisy “and” gate model (DINA) model that have been widely used in cognitive diagnosis study. The new algorithm avoids the Metropolis-Hastings algorithm boring adjustment the turning parameters to achieve an appropriate acceptance probability. Four simulation studies are conducted and a detailed analysis of fraction subtraction data is carried out to further illustrate the proposed methodology.Keywords
This publication has 45 references indexed in Scilit:
- The Generalized Multilevel Facets Model for Longitudinal DataJournal of Educational and Behavioral Statistics, 2012
- On the Analysis of Fraction Subtraction Data: The DINA Model, Classification, Latent Class Sizes, and the Q-MatrixApplied Psychological Measurement, 2010
- Bayesian Estimation and Model Choice in Item Response ModelsJournal of Statistical Computation and Simulation, 2002
- Gibbs Sampling for Bayesian Non-Conjugate and Hierarchical Models by Using Auxiliary VariablesJournal of the Royal Statistical Society Series B: Statistical Methodology, 1999
- General Methods for Monitoring Convergence of Iterative SimulationsJournal of Computational and Graphical Statistics, 1998
- Understanding the Metropolis-Hastings AlgorithmThe American Statistician, 1995
- Bayesian Analysis of Binary and Polychotomous Response DataJournal of the American Statistical Association, 1993
- Inference from Iterative Simulation Using Multiple SequencesStatistical Science, 1992
- Sampling-Based Approaches to Calculating Marginal DensitiesJournal of the American Statistical Association, 1990
- The Calculation of Posterior Distributions by Data AugmentationJournal of the American Statistical Association, 1987