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

Journal article

Goldstein, Harvey, Browne, William J. and Charlton, Christopher. (2017). A Bayesian model for measurement and misclassification errors alongside missing data, with an application to higher education participation in Australia. Journal of Applied Statistics. 45(5), pp. 918 - 931. https://doi.org/10.1080/02664763.2017.1322558

Authors	Goldstein, Harvey, Browne, William J. and Charlton, Christopher
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.
Year	2017
Journal	Journal of Applied Statistics
Journal citation	45 (5), pp. 918 - 931
Publisher	Taylor and Francis Ltd
ISSN	0266-4763
Digital Object Identifier (DOI)	https://doi.org/10.1080/02664763.2017.1322558
Scopus EID	2-s2.0-85019159270
Page range	918 - 931
Research Group	Institute for Learning Sciences and Teacher Education (ILSTE)
Publisher's version	File Access Level Controlled
Place of publication	United Kingdom
Editors	J. Chen

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