Exploring the Correlation Between Multiple Latent Variables and Covariates in Hierarchical Data Based on the Multilevel Multidimensional IRT Model
Affiliation
Univ Arizona, Dept East Asian StudiesIssue Date
2019-10-25Keywords
Bayesian estimationeducation assessment
multidimensional item response theory
multilevel model
teacher satisfactions
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FRONTIERS MEDIA SACitation
Zhang J, Lu J, Chen F and Tao J (2019) Exploring the Correlation Between Multiple Latent Variables and Covariates in Hierarchical Data Based on the Multilevel Multidimensional IRT Model. Front. Psychol. 10:2387. doi: 10.3389/fpsyg.2019.02387Journal
FRONTIERS IN PSYCHOLOGYRights
Copyright © 2019 Zhang, Lu, Chen and Tao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY).Collection Information
This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.Abstract
In many large-scale tests, it is very common that students are nested within classes or schools and that the test designers try to measure their multidimensional latent traits (e.g., logical reasoning ability and computational ability in the mathematics test). It is particularly important to explore the influences of covariates on multiple abilities for development and improvement of educational quality monitoring mechanism. In this study, motivated by a real dataset of a large-scale English achievement test, we will address how to construct an appropriate multilevel structural models to fit the data in many of multilevel models, and what are the effects of gender and socioeconomic-status differences on English multidimensional abilities at the individual level, and how does the teachers' satisfaction and school climate affect students' English abilities at the school level. A full Gibbs sampling algorithm within the Markov chain Monte Carlo (MCMC) framework is used for model estimation. Moreover, a unique form of the deviance information criterion (DIC) is used as a model comparison index. In order to verify the accuracy of the algorithm estimation, two simulations are considered in this paper. Simulation studies show that the Gibbs sampling algorithm works well in estimating all model parameters across a broad spectrum of scenarios, which can be used to guide the real data analysis. A brief discussion and suggestions for further research are shown in the concluding remarks.Note
Open access journalISSN
1664-1078PubMed ID
31708833Version
Final published versionSponsors
National Natural Science Foundation of China [11571069]ae974a485f413a2113503eed53cd6c53
10.3389/fpsyg.2019.02387
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Except where otherwise noted, this item's license is described as Copyright © 2019 Zhang, Lu, Chen and Tao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY).
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