Data Analysis Using SAS
  
C.Y. Joanne Peng  Indiana University  
   
© 2009   640 pages   SAGE Publications, Inc   

Data Analysis Using SAS is 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. As such, 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 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 -áused to analyze data in the social, behavorial, and health sciences using SAS commands.
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.


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