Missing Data: Analysis and Design by John Graham
NOTE: This page describes work that is no longer ongoing at The Methodology Center. It represents only a small fraction of the past Methodology Center research about missing data. We maintain this page because it contains materials that may be valuable to researchers, but software on this page is not supported by the helpdesk. Last updated: November 2012.
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 of 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. Free downloads of the supplementary software are available below, along with sample empirical data sets. These materials complement a variety of practical exercises described in the book that enhance and reinforce the reader’s learning experience. Missing Data: Analysis and Design and this 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.
Visit the Springer website to learn more or to order the book.
John W. Graham, Ph.D., 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.
Example Data Sets
This .ZIP file contains the data sets used in the book. The number in the data set name refers to the related chapter number.
The data sets are for use with the book.
The software on this page is available for free download, but is not supported by The Methodology Center's helpdesk.
Multiple imputation of incomplete multivariate data under a normal model
Covariance estimation with missing variables
For making NORM work with SPSS and with 2-level HLM analysis
Aux Simulate: Aux Simulate
Graham, J. W. (2012). Missing data: Analysis and design. New York: Springer.
NORM: Multiple imputation of incomplete multivariate data under a normal model (Version 2) [Software]. (1999). University Park: The Methodology Center, Penn State. Retrieved from http://methodology.psu.edu
Schafer, J. L. (1999). NORM users’ guide (Version 2). University Park: The Methodology Center, Penn State. Retrieved from http://methodology.psu.edu
EMCOV (Version 2.3) [Software] (1995). University Park: John Graham, Penn State. Retrieved from http://methodology.psu.edu
Graham, J. W. (1995). EMCOV reference manual. University Park: John Graham, Penn State. Retrieved from http://methodology.psu.edu
MI Automate (Version 1) [Software]. (2012). University Park: The Methodology Center, Penn State. Retrieved from http://methodology.psu.edu
Graham, J. W. (2012). MI automate users' guide: Steps for making NORM work with SPSS & with 2-level HLM analysis. University Park: Penn State. Retrieved from http://methodology.psu.edu
Aux Simulate (Version 1) [Software]. (2012). University Park: The Methodology Center, Penn State. Retrieved from http://methodology.psu.edu