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

被引：67

作者：

Fox, JP ^{[1
]}

Glas, CAW ^{[1
]}

机构：

[1] Univ Twente, Dept Educ Measurement & Data Anal, NL-7500 AE Enschede, Netherlands

来源：

PSYCHOMETRIKA | 2003年 / 68卷 / 02期

关键词：

classical test theory; Gibbs sampler; item response theory; hierarchical linear models; Markov Chain Monte Carlo; measurement error; multilevel model; multilevel IRT; two-parameter normal ogive model;

D O I：

10.1007/BF02294796

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

It is shown that measurement error in predictor variables can be modeled using item response theory (IRT). The predictor variables, that may be defined at any level of an hierarchical regression model, are treated as,latent variables. The normal ogive model is used to describe the relation between the latent variables and dichotomous observed variables, which may be responses to tests or questionnaires. It will be shown that the multilevel model with measurement error in the observed predictor variables can be estimated in a Bayesian framework using Gibbs sampling. In this article, handling measurement error via the normal ogive model is compared with alternative approaches using the classical true score model. Examples using real data are given.

引用

页码：169 / 191

页数：23

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