Lewis - Complex survey data analysis with SAS

COMPLEX SURVEY DATA ANALYSIS WITH SAS

COMPLEX SURVEY DATA ANALYSIS WITH SAS

Taylor H. Lewis

Department of Statistics
George Mason University
Fairfax, Virginia, USA

CRC Press

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Version Date: 20160427

International Standard Book Number-13: 978-1-4987-7677-6 (Hardback)

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Library of Congress Cataloging-in-Publication Data

Names: Lewis, Taylor H.

Title: Complex survey data analysis with SAS / Taylor H. Lewis.

Description: Boca Raton : Taylor & Francis, 2016. | A CRC title. | Includes bibliographical references and index.

Identifiers: LCCN 2016008468 | ISBN 9781498776776

Subjects: LCSH: Multivariate analysis--Data processing. | Regression analysis--Data processing. | Sampling (Statistics) | SAS (Computer file) | Surveys.

Classification: LCC QA278 .L49 2016 | DDC 519.5/30285555--dc23

LC record available at https://lccn.loc.gov/2016008468

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To my three beautiful girls: Katie, Tessa, and Willow

Contents

I wrote this book to serve as a handy desk-side reference for students, researchers, or any other kind of data analysts who have an intermediate to advanced grasp on fundamental statistical techniques, but who may not be familiar with the nuances introduced by complex survey data. As I embarked on this project, my intent was never to supplant or even compete with any of the established textbooks on the subject, such as Kish (1965), Cochran (1977), Lohr (2009), or Heeringa et al. (2010), for that would have surely been a futile endeavor. Rather, my aim was to demonstrate easy-to-follow applications of survey data analysis for researchers like myself who rely on SAS primarily, if not exclusively, for conducting statistical analyses.

All material in this book was developed using SAS Version 9.4 and SAS/STAT 13.1. It features the SURVEY family of SAS/STAT procedures, of which there are six at the time of this writing: PROC SURVEYSELECT, PROC SURVEYMEANS, PROC SURVEYFREQ, PROC SURVEYREG, PROC SURVEYLOGISTIC, and PROC SURVEYPHREG. The last five listed are companions to PROC MEANS, PROC FREQ, PROC REG, PROC LOGISTIC, and PROC PHREG, respectively. As will be explained in great detail over the course of this book, you should use one of the SURVEY procedures when analyzing data from a complex survey design. Using one of the non-SURVEY procedures opens the door to making false inferences due to biased point estimates, biased measures of variability, or both.

The book is structured as follows. considers methods to impute, or fill in, plausible values for the missing data.

Each chapter was designed to be read on its own and not necessarily in sequence. As each chapter progresses, the material tends to build on itself and become more complex. It is my belief that oscillating between numerous survey data sets can only serve to detract from the readers ability to compare and contrast the current syntax example with those preceding it in the chapter. As such, examples in most chapters are motivated within the context of a single complex survey data set.

Many people contributed to this effort and are owed my sincere thanks. First and foremost, I am especially grateful for the encouragement I received from Shelley Sessoms at SAS, who believed in me and the vision I had for this book and on several occasions helped me overcome my self-doubt about whether I could complete this colossal task. I thank Rob Calver for resuscitating this project, and for the professionalism he and his entire team at CRC Press exhibited. I am also very grateful for the encouragement I received on both professional and personal levels from Frauke Kreuter, Partha Lahiri, Brady West, Roberto Valverde, Abera Wouhib, Sidney Fisher, Mircea Marcu, Karl Hess, Kimya Lee, Chris Daman, Glenn White, Eugene Pangalos, Graham Evans, Ryan Fulcher, Matt Fuller, and Doug Drewry. Richard Valliant and Richard Sigman were kind enough to endure several lengthy conversations during which I solicited their advice on certain content issues that surfaced as the book took shape. I also thank Richard Sigman for agreeing to serve as a reviewer. He, along with Patricia Berglund, Peter Timusk, Donna Brogan, and several anonymous reviewers at SAS Institute, provided valuable feedback that greatly improved the overall quality of the book. The same can be said for Casey Copen and Kimberly Daniels at the National Center for Health Statistics, who carefully reviewed all discussion and examples pertaining to the National Survey of Family Growth. I claim full responsibility for any and all errors that remain. Lastly, and most importantly, I thank my wife, Katie, for being extraordinarily patient and understanding during this process, selflessly tolerating my extended absences from home without (too much) complaint. None of this would have been possible without her love and support.

Taylor Lewis

Arlington, Virginia

Taylor H. Lewis is a PhD graduate of the Joint Program in Survey Methodology at the University of Maryland, College Park, and an adjunct professor in the George Mason University Department of Statistics. An avid SAS user for 15 years, Taylor is a SAS Certified Advanced programmer and a nationally recognized SAS educator who has produced dozens of papers and workshops illustrating how to efficiently and effectively conduct statistical analyses using SAS.