Multiple imputation of missing data using SAS / Patricia Berglund and Steven G. Heeringa.

Format:

Book

Author/Creator:

Berglund, Patricia A., author.

Heeringa, Steven G., 1953- author.

Contributor:

SAS Institute.

Language:

English

Subjects (All):

SAS (Computer file).

Multiple imputation (Statistics).

Mathematical statistics--Data processing.

Mathematical statistics.

Physical Description:

1 online resource (xii, 150 pages) : illustrations

Edition:

First edition.

Place of Publication:

Cary, North Carolina : SAS Institute Inc., [2014]

Summary:

Find guidance on using SAS for multiple imputation and solving common missing data issues 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. This book is part of the SAS Press program.

Contents:

Introduction to missing data and methods for analyzing data with missing values

Introduction to multiple imputation theory and methods

Multiple imputation for the analysis of complex sample survey data

Multiple imputation of continuous variables

Multiple imputation of classification variables

Multiple imputation case studies

Preparation of data sets for PROC MIANALYZE.

Notes:

Includes bibliographical referneces and index.

Description based on print version record.

ISBN:

1-62959-205-6

1-61290-452-1

OCLC:

887736457