Statistics and Its Interface

Volume 12 (2019)

Number 1

Bayesian estimation of a multilevel multidimensional item response model using auxiliary variables method: an exploration of the correlation between multiple latent variables and covariates in hierarchical data

Pages: 35 – 48

DOI: http://dx.doi.org/10.4310/SII.2019.v12.n1.a4

Authors

Jiwei Zhang (School of Mathematics and Statistics, Northeast Normal University, Changchun, Jilin, China)

Jing Lu (School of Mathematics and Statistics, Northeast Normal University, Changchun, Jilin, China)

Jian Tao (School of Mathematics and Statistics, Northeast Normal University, Changchun, Jilin, China)

Abstract

Within the framework of Bayesian analysis, we present a multilevel multidimensional item response modeling and estimation method to study the relations among multiple abilities and covariates in a hierarchical data structure. The proposed method is well suited to examining a scenario in which a test measures multidimensional latent traits (e.g., reading ability, cognitive ability, and computing ability) and in which students are nested within classes or schools. The developed Gibbs sampling algorithm based on auxiliary variables can accurately estimate the correlations among multidimensional latent traits, along with the correlation between person- and school-level covariates and latent traits. Three information criteria and the pseudo-Bayes factor approach are used to evaluate model fit and make model comparison. Simulation studies show that the proposed method works well in estimating all model parameters across a broad spectrum of scenarios. A case study on an educational assessment data is investigated to demonstrate the practical application of the proposed procedure.

Keywords

Bayesian inference, Gibbs sampling algorithm, Information criteria, cross-validation loglikelihood, pseudo-Bayes factor

Full Text (PDF format)

Jiwei Zhang and Jing Lu are co-first authors. They contributed equally to this work.

This work is supported by the National Natural Science Foundation of China (grant number 11571069).

Received 18 January 2017

Published 26 October 2018