Bayesian Estimation of the DINA Model With Pólya-Gamma Gibbs Sampling

Frontiers in Psychology

article

Zhaoyuan Zhang¹  Jiwei Zhang²  Jing Lu¹  Jian Tao¹ 

[1] Key Laboratory of Applied Statistics of MOE, School of Mathematics and Statistics, Northeast Normal University;Key Lab of Statistical Modeling and Data Analysis of Yunnan Province, School of Mathematics and Statistics, Yunnan University

关键词: Bayesian estimation;    cognitive diagnosis models;    DINA model;    Pólya-Gamma Gibbs sampling algorithm;    Metropolis-Hastings algorithm;    potential scale reduction factor;   

DOI  :  10.3389/fpsyg.2020.00384

学科分类：社会科学、人文和艺术（综合）

来源: Frontiers

【 摘 要 】

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.

【 授权许可】

CC BY