A hierarchical multi-unidimensional IRT approach for analyzing sparse, multi-group data for integrative data analysis

DOI10.1007/s11336-014-9420-2zbMath1323.62114OpenAlexW2049376076WikidataQ30855850 ScholiaQ30855850MaRDI QIDQ888041

Su-Young Kim, Anne E. Ray, Yang Jiao, Helene R. White, Eun-Young Mun, Jimmy de la Torre, Yan Huo

Publication date: 4 November 2015

Published in: Psychometrika (Search for Journal in Brave)

Full work available at URL: http://europepmc.org/articles/pmc4379139

zbMATH Keywords

MCMCIRTIDAmulti-unidimensionalmultiple groups

Mathematics Subject Classification ID

Applications of statistics to psychology (62P15)

Related Items (1)

Bayesian multilevel multidimensional item response modeling approach for multiple latent variables in a hierarchical structure

Uses Software

• BaM
• Ox
• BayesDA
• IRTPRO
• mirt

Cites Work

• Randomization-based inference about latent variables from complex samples
• The role of secondary covariates when estimating latent trait population distributions
• Multidimensional item reponse theory.
• Inference from iterative simulation using multiple sequences
• Posterior predictive \(p\)-values
• Bayesian estimation of a multilevel IRT model using Gibbs sampling
• MCMC estimation and some model-fit analysis of multidimensional IRT models
• Parameter expansion for sampling a correlation matrix: an efficient GPX-RPMH algorithm
• Efficient Matrix Sampling Instruments for Correlated Latent Traits: Examples from the National Assessment of Educational Progress
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item

This page was built for publication: A hierarchical multi-unidimensional IRT approach for analyzing sparse, multi-group data for integrative data analysis