A Bayesian model for measurement and misclassification errors alongside missing data, with an application to higher education participation in Australia

Harvey Goldstein*, William J. Browne, Christopher Charlton

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)

4 Citations (Scopus)
282 Downloads (Pure)

Abstract

In this paper we consider the impact of both missing data and measurement errors on a longitudinal analysis of participation in higher education in Australia. We develop a general method for handling both discrete and continuous measurement errors that also allows for the incorporation of missing values and random effects in both binary and continuous response multilevel models. Measurement errors are allowed to be mutually dependent and their distribution may depend on further covariates. We show that our methodology works via two simple simulation studies. We then consider the impact of our measurement error assumptions on the analysis of the real data set.

Original languageEnglish
Pages (from-to)918-931
Number of pages14
JournalJournal of Applied Statistics
Volume45
Issue number5
Early online date9 May 2017
DOIs
Publication statusPublished - 4 Apr 2018

Structured keywords

  • Jean Golding

Keywords

  • higher education participation
  • measurement errors
  • missclassification errors
  • MCMC
  • MISSING DATA
  • multilevel

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