Missing Data and Sample Attrition

  • Heather Brown


In this chapter, we discuss sample attrition and missing variables and methods to overcome the bias on the data arising from these issues. Specifically, we outline with examples missing imputation and inverse probability weighting. Stata code written in STATA v.14 for examples is provided.


Sample attrition Missing data Multiple imputation Inverse probability weighting 

References and Further Reading

  1. Jones, A. M., Rice, N., & Bago d’Uva, T. (2007). Applied health economics. London: Routledge Advanced Texts in Economics and Finance.Google Scholar
  2. Rubin, D. B. (1987). Multiple imputation for nonresponse in surveys. New York: Wiley.CrossRefGoogle Scholar
  3. Seaman, S. R., & White, I. R. (2013). Review of inverse probability weighting for dealing with missing data. Statistical Methods in Medical Research, 22(3), 278–295.CrossRefGoogle Scholar
  4. Wooden, M., & Watson, N. (2007). The HILDA survey and its contribution to economic and social research (so far). The Economic Record, 83(261), 208–231.CrossRefGoogle Scholar
  5. Yuan, Y. C. (2010). Multiple imputation for missing data: Concepts and new development (Version 9.0). SAS Institute Inc, Rockville, MD, 49, 1–11.Google Scholar

Copyright information

© The Author(s) 2018

Authors and Affiliations

  • Heather Brown
    • 1
  1. 1.Newcastle UniversityNewcastle upon TyneUK

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