Multiple Imputation of Missing Data Using SAS provides both theoretical background and constructive solutions for those working with incomplete data sets in an engaging example-driven format. It offers practical instruction on the use of SAS for multiple imputation and provides numerous examples that use a variety of public release data sets with applications to survey data.
Written for users with an intermediate background in SAS programming and statistics, this book is an excellent resource for anyone seeking guidance on multiple imputation. The authors cover the MI and MIANALYZE procedures in detail, along with other procedures used for analysis of complete data sets. They guide analysts through the multiple imputation process, including evaluation of missing data patterns, choice of an imputation method, execution of the process, and interpretation of results.
Topics discussed include how to deal with missing data problems in a statistically appropriate manner, how to intelligently select an imputation method, how to incorporate the uncertainty introduced by the imputation process, and how to incorporate the complex sample design (if appropriate) through use of the SAS SURVEY procedures.
Discover the theoretical background and see extensive applications of the multiple imputation process in action.
About the Authors
Patricia Berglund is a Senior Research Associate in the Survey Methodology Program at the Institute for Social Research. She has extensive experience in the use of computing systems for data management and complex sample survey data analysis. She works on research projects in youth substance abuse, adult mental health, and survey methodology using data from Army STARRS, Monitoring the Future, the National Comorbidity Surveys, World Mental Health Surveys, Collaborative Psychiatric Epidemiology Surveys, and various other national and international surveys. In addition, she is involved in development, implementation, and teaching of analysis courses and computer training programs at the Survey Research Center-Institute for Social Research. She also lectures in the SAS® Institute-Business Knowledge Series. Contact her at firstname.lastname@example.org.
Steven Heeringa is a Senior Research Scientist at the University of Michigan Institute for Social Research (ISR) where he is the Director of the Statistical Design Group. He is a member of the faculty of the University of Michigan Program in Survey Methods and the Joint Program in Survey Methodology at the University of Maryland. Heeringa is a Fellow of the American Statistical Association and elected member of the International Statistical Institute. He is the author of many publications on statistical design and sampling methods for research in the fields of public health and the social sciences. Heeringa has over 35 years of statistical sampling experience in the development of the ISR’s National Sample design, as well as research designs for the ISR’s major longitudinal and cross-sectional survey programs.