Modelling measurement errors and category misclassifications in multilevel models

H Goldstein; D Kounali; Anthony Robinson

doi:10.1177/1471082X0800800302

Modelling measurement errors and category misclassifications in multilevel models

H Goldstein
, D Kounali
, Anthony Robinson

Department of Mathematical Sciences

Research output: Contribution to journal › Article › peer-review

32 Citations (SciVal)

Abstract

Models are developed to adjust for measurement errors in normally distributed predictor and response variables and categorical predictors with misclassification errors. The models allow for a hierarchical data structure and for correlations among the errors and misclassifications. Markov Chain Monte Carlo (MCMC) estimation is used and implemented in a set of MATLAB macros.

Original language	English
Pages (from-to)	243-261
Number of pages	19
Journal	Statistical Modelling
Volume	8
Issue number	3
DOIs	https://doi.org/10.1177/1471082X0800800302
Publication status	Published - Sept 2008

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AB - Models are developed to adjust for measurement errors in normally distributed predictor and response variables and categorical predictors with misclassification errors. The models allow for a hierarchical data structure and for correlations among the errors and misclassifications. Markov Chain Monte Carlo (MCMC) estimation is used and implemented in a set of MATLAB macros.

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Modelling measurement errors and category misclassifications in multilevel models

Abstract

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