Data Analysis Using SAS offers a comprehensive core text focused on key concepts and techniques in quantitative data analysis using the most current SAS commands and programming language. The coverage of the text is more evenly balanced among statistical analysis, SAS programming, and data/file management than any available text on the market. It provides students with a hands-on, exercise-heavy method for learning basic to intermediate SAS commands while understanding how to apply statistics and reasoning to real-world problems.
Designed to be used in order of teaching preference by instructor, the book is comprised of two primary sections: the first half of the text instructs students in techniques for data and file managements such as concatenating and merging files, conditional or repetitive processing of variables, and observations. The second half of the text goes into great depth on the most common statistical techniques and concepts – descriptive statistics, correlation, analysis of variance, and regression – used to analyze data in the social, behavioral, and health sciences using SAS commands. A student study comes replete with a multitude of computer programs, their output, specific details on how to check assumptions, as well as all data sets used in the book.
Data Analysis Using SAS is a complete resource for Data Analysis I and II, Statistics I and II, Quantitative Reasoning, and SAS Programming courses across the social and behavioral sciences and health – especially those that carry a lab component.
Tabella dei contenuti
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
Circa l’autore
C.Y. Joanne Peng (Ph D, University of Wisconsin-Madison, Quantitative Methods
with a minor in Statistics) is Professor of Educational Inquiry Methodology and
Adjunct Professor of Statistics at Indiana
University. 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® for
statistical analyses and received one BEST PAPER Award at a SAS® users annual
conference. She has taught applied statistics and data analysis courses
at major Research I universities for the past 20 years, including University of Wisconsin,
University of Iowa,
University of North
Carolina, and Indiana
University. She is a
member of the American Statistical Association, American Educational Research
Association, American Psychological Association, and the SAS Users Group
International.
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