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

Research output: Contribution to journalArticle

Original languageEnglish
Pages (from-to)918-931
Number of pages14
JournalJournal of Applied Statistics
Volume45
Issue number5
Early online date9 May 2017
DOIs
DateAccepted/In press - 16 Apr 2017
DateE-pub ahead of print - 9 May 2017
DatePublished (current) - 4 Apr 2018

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.

    Research areas

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

    Structured keywords

  • Jean Golding

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  • Full-text PDF (accepted author manuscript)

    Rights statement: This is the author accepted manuscript (AAM). The final published version (version of record) is available online via Taylor & Francis at http://www.tandfonline.com/doi/abs/10.1080/02664763.2017.1322558. Please refer to any applicable terms of use of the publisher.

    Accepted author manuscript, 743 KB, PDF-document

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