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

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  • Harvey Goldstein
  • William J. Browne
  • Christopher Charlton

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.

Suggested Citation

  • Harvey Goldstein & William J. Browne & Christopher Charlton, 2018. "A Bayesian model for measurement and misclassification errors alongside missing data, with an application to higher education participation in Australia," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(5), pages 918-931, April.
    • Handle: RePEc:taf:japsta:v:45:y:2018:i:5:p:918-931
    DOI: 10.1080/02664763.2017.1322558
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    Cited by:

    1. William J. Browne, 2022. "A celebration of Harvey Goldstein’s lifetime contributions: Memories of working with Harvey Goldstein on multilevel modelling methods and applications," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(3), pages 753-758, July.

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