Missing Data

Analysis and Design

  • John W. Graham

Part of the Statistics for Social and Behavioral Sciences book series (SSBS)

Table of contents

  1. Front Matter
    Pages i-xxiii
  2. Missing Data Theory

    1. Front Matter
      Pages 1-1
    2. John W. Graham
      Pages 3-46
    3. John W. Graham
      Pages 47-69
  3. Multiple Imputation and Basic Analysis

  4. Practical Issues in Missing Data Analysis

  5. Planned Missing Data Design

    1. Front Matter
      Pages 277-277
    2. John W. Graham, Allison E. Shevock
      Pages 295-323

About this book

Introduction

Missing data have long plagued those conducting applied research in the social, behavioral, and health sciences.  Good missing data analysis solutions are available, but practical information about implementation of these solutions has been lacking.  The objective of Missing Data: Analysis and Design is to enable investigators who are non-statisticians to implement modern missing data procedures properly in their research, and reap the benefits in terms of improved accuracy and statistical power.

Missing Data: Analysis and Design contains essential information for both beginners and advanced readers.  For researchers with limited missing data analysis experience, this book offers an easy-to-read introduction to the theoretical underpinnings of analysis of missing data; provides clear, step-by-step instructions for performing state-of-the-art multiple imputation analyses; and offers practical advice, based on over 20 years' experience, for avoiding and troubleshooting problems.  For more advanced readers, unique discussions of attrition, non-Monte-Carlo techniques for simulations involving missing data, evaluation of the benefits of auxiliary variables, and highly cost-effective planned missing data designs are provided.

The author lays out missing data theory in a plain English style that is accessible and precise.  Most analyses described in the book are conducted using the well-known statistical software packages SAS and SPSS, supplemented by Norm 2.03 and associated Java-based automation utilities.  A related web site contains free downloads of the supplementary software, as well as sample empirical data sets and a variety of practical exercises described in the book to enhance and reinforce the reader’s learning experience.  Missing Data: Analysis and Design and its web site work together to enable beginners to gain confidence in their ability to conduct missing data analysis, and more advanced readers to expand their skill set. 

JOHN W. GRAHAM, PhD, is Professor of Biobehavioral Health at The Pennsylvania State University.  His research and publishing focus on the evaluation of health promotion and disease prevention interventions.  He specializes in evaluation research methods, including missing data analysis and design, structural equation modeling, and measurement.

Keywords

Data analysis Missing data Monte Carlo Multilevel data Multiple imputation

Authors and affiliations

  • John W. Graham
    • 1
  1. 1.Department of Biobehavioral HealthThe Pennsylvania State UniversityUniversity ParkUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4614-4018-5
  • Copyright Information Springer Science+Business Media New York 2012
  • Publisher Name Springer, New York, NY
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-1-4614-4017-8
  • Online ISBN 978-1-4614-4018-5
  • About this book