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Data Analysis Using SAS

Data Analysis Using SAS

August 2008 | 640 pages | SAGE Publications, Inc
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 at 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.

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
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?
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
University of Scranton


1) I am not quite pleased with the didactic presentation of the data analysis methods considered in book - one needs in any case some other statistical book to understand the methods.
2) I expected more from a book called "Data analysis...". You find there no more advanced data analysis methods than regression.
3) I was articularly looking for a statistical book with accompanied software SAS. This book is in fact about SAS language with some practical applications. Moreover, there is no connection to the SAS Enterprise Guide at all.

The strong point of the book I see in its detailed explanation of SAS language.

So, I would recommend this book for those who has already basic knowledge in statistical methods, for his analysis does not need any non-linear model and want to learn how to implement his model in SAS language.
But I found it not suitable to accompany any (basic or advanced) statistical course.

Dr Tatiana Miazhynskaia
Master Program "Quantitative Asset and Risk Management", University of applied sciences bfi Vienna
November 16, 2009

Despite offering several affordable packages, we were not allowed to require students to use SAS.

Excellent introduction to SAS and would be an excellent resource.

Mr Brian Kreeger
Business Administration, Metropolitan State University
October 23, 2009
Key features
  • A disciplined focus on statistical topics that are always presented in beginning- to intermediate-level statistics courses, allowing for greater depth of understanding rather than breadth.
  • Statistical techniques and concepts are presented from the perspective of solving problems, to better engage students in the data analysis process.
  • An expansive use of visuals, including screen shots, figures, and graphics, to illustrate statistical concepts and show results from SAS output.
  • Even coverage of both programming capabilities of the SAS system and its statistical procedures (most books cover one or the other).
  • Exercises with SAS programming, statistical analyses, and their answer keys are provided for each chapter.
  • A full explanation of the ODS system for saving/exporting analysis results as well as a corresponding appendix devoted to the syntax of ODS.
  • A specific set of guidelines for how to check statistical assumptions, further giving students the critical tools for solving problems - both data-driven ones as well as programming/data management ones.


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