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Techniques for Analyzing Incomplete Data in Public Health Research

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Innovative Statistical Methods for Public Health Data

Part of the book series: ICSA Book Series in Statistics ((ICSABSS))

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Abstract

Statistical inference of incomplete data has been an obstacle in numerous areas of research, and public health studies are no exception. Since studies in this field are often survey-based and can center around sensitive personal information, it can make them susceptible to missing records. This chapter discusses the causes and problems created by incomplete data and recommends techniques for how to handle it through multiple imputation.

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Acknowledgements

The authors wish to thank Dr. Seth Kalichman for generously sharing his data. This project was supported in part by the National Institute of Mental Health, Award Number K01MH087219. The content is solely the responsibility of the authors, and it does not represent the official views of the National Institute of Mental Health or the National Institutes of Health.

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Authors and Affiliations

  1. Department of Statistics, University of Connecticut, 215 Glenbrook Road, Storrs, CT, 06269, USA

    Valerie Pare & Ofer Harel

Authors
  1. Valerie Pare
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  2. Ofer Harel
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Corresponding author

Correspondence to Ofer Harel .

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Editors and Affiliations

  1. Wallace H. Kuralt Distinguished Professor, Director of Statistical Development and Consultation, School of Social Work, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA

    Ding-Geng (Din) Chen

  2. Arizona State University, Tempe, Arizona, USA

    Jeffrey Wilson

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Pare, V., Harel, O. (2015). Techniques for Analyzing Incomplete Data in Public Health Research. In: Chen, DG., Wilson, J. (eds) Innovative Statistical Methods for Public Health Data. ICSA Book Series in Statistics. Springer, Cham. https://doi.org/10.1007/978-3-319-18536-1_8

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