Modelling measurement errors and category misclassifications in multilevel models

Authors
Goldstein, H., Kounali, D. and Robinson, A.
Year
2008
Journal
Statistical Modelling, 170:4, 941-954
DOI
10.1177/1471082X0800800302
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.

Number of levels
2
Model data structure
Response types
Multivariate response model?
No
Longitudinal data?
No
Substantive discipline
Impact

Develops methodology for allowing for measurement error and category missclassification. Long term impact.

Paper submitted by
Harvey Goldstein, Graduate School of Education, University of Bristol, h.goldstein@bristol.ac.uk
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