Data Analysis Using SAS

C.Y. Joanne Peng (PhD, University of Wisconsin-Madison, Quantitative Methods with a minor in Statistics) is Professor of Educational Inquiry Methodology and Adjunct Professor of Statistics atIndianaUniversity. Her research interests include logistic regression, missing data methods, and statistical computing using SAS, SPSS, BMDP, Minitab, CLUSTAN, Systat, and S+. She has published more than 50 refereed articles, book chapters, technical reports, and encyclopedia entries on applied statistics, psychometrics, and statistical computing. She is the author or co-author of two books on using SAS(R) for statistical analyses and received one BEST PAPER Award at a SAS(R) users annual conference. She has taught applied statistics and data analysis courses at major Research I universities for the past 20 years, includingUniversity ofWisconsin,University ofIowa,University ofNorth Carolina, andIndianaUniversity. She is a member of the American Statistical Association, American Educational Research Association, American Psychological Association, and the SAS Users Group International.

PART I. INTRODUCTION TO SAS AND BASIC DATA ANALYSIS 1. Why do you need to learn SAS for data analyses? 2. Where do you start? 3. How to prepare data for SAS processing 4. From data to a SAS data set 5. Enhancing SAS programs and output 6. Verifying data 7. Data transformation PART II. STATISTICAL PROCEDURES 8. Quick descriptive analysis 9. Comprehensive descriptive analysis and normality test 10.Graphing data 11. Categorical data analysis 12. T-test of population means 13. Analysis of variance 14. Inferences about two or more population typical scores by ranks 15. Examining trends in data 16. Correlation 17. When do you stop worrying and start loving regression? PART III. ADVANCED DATA AND FILE MANAGEMENT 18. Selecting variables or observations from a SAS data set 19. Repetitive and conditional data processing 20. Structuring SAS data sets Appendix A. What lies beyond this book? Information on reference books, hotlines, and Internet resources Appendix B. Data sets used in this book Appendix C. Converting SPSS, STATA, Excel, Minitab, Systat data set files to SAS data sets or data set files

"Peng provides an excellent overview of data analysis using the powerful statistical software package SAS. This book is quite appropriate as a self-placed tutorial for researchers, as well as a textbook or supplemental workbook for data analysis courses such as statistics or research methods. Peng provides detailed coverage of SAS capabilities using step-by-step procedures and includes numerous comprehensive graphics and figures, as well as SAS printouts. Readers do not need a background in computer science or programming. Includes numerous examples in education, health sciences, and business." -- D. J. Gougeon