Bayesian modeling of measurement error in predictor variables using item response theory
Bayesian modeling of measurement error in predictor variables using item response theory
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DOI：
10.1007/bf02294796
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发表时间：
2003-06-01
期刊：
影响因子：
3
通讯作者：
Glas, CAW
中科院分区：
文献类型：
--
作者：
Fox, JP;Glas, CAW
结果表明，测量误差的预测变量可以建模使用项目反应理论（IRT）。可以在分层回归模型的任何级别定义的预测变量被视为潜变量。正态拱形模型用于描述潜变量和二分观测变量之间的关系，这些观测变量可以是对测试或问卷的响应。它将表明，在观测到的预测变量的测量误差的多水平模型可以估计在贝叶斯框架使用吉布斯抽样。在这篇文章中，处理测量误差，通过正常的拱形模型与其他方法使用经典的真实得分模型进行了比较。使用真实的数据的例子。
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.