Bayesian modeling of measurement error in predictor variables using item response theory

DOI10.1007/BF02294796zbMath1306.62412OpenAlexW2143170931MaRDI QIDQ2259866

Jean-Paul Fox, Cees A. W. Glas

Publication date: 5 March 2015

Published in: Psychometrika (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/bf02294796

zbMATH Keywords

item response theoryMarkov chain Monte CarloGibbs samplermeasurement errormultilevel modelhierarchical linear modelsmultilevel IRTtwo-parameter normal ogive modelclassical test theory

Mathematics Subject Classification ID

Applications of statistics to psychology (62P15)

Related Items (10)

Multivariate zero-inflated modeling with latent predictors: modeling feedback behavior ⋮ Simulation-extrapolation with latent heteroskedastic error variance ⋮ Structural modeling of measurement error in generalized linear models with Rasch measures as covariates ⋮ Unnamed Item ⋮ A Bayesian modeling approach for generalized semiparametric structural equation models ⋮ Generalized multilevel structural equation modeling ⋮ Finite mixture multilevel multidimensional ordinal IRT models for large scale cross-cultural research ⋮ Modelling measurement errors and category misclassifications in multilevel models ⋮ A recursive algorithm on estimating the parameters in multilevel models subject to the measurement errors on the covariates ⋮ Correction for item response theory latent trait measurement error in linear mixed effects models

Uses Software

• CODA
• HLM
• BayesDA
• BILOG

Cites Work

• Markov chains for exploring posterior distributions. (With discussion)
• Bayesian estimation of a multilevel IRT model using Gibbs sampling
• MCMC estimation and some model-fit analysis of multidimensional IRT models
• A comparison of four methods of constructing factor scores
• Sampling-Based Approaches to Calculating Marginal Densities
• Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
• The Calculation of Posterior Distributions by Data Augmentation
• Ordinal Data Modeling
• Covariance structure of the Gibbs sampler with applications to the comparisons of estimators and augmentation schemes
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