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SAS Essentials: A Guide to Mastering SAS for Research

ISBN: 978-0-470-55264-3
350 pages
January 2010, Jossey-Bass
SAS Essentials: A Guide to Mastering SAS for Research (0470552646) cover image
SAS Essentials provides an introduction to SAS statistical software, the premiere statistical data analysis tool for scientific research. Through its straightforward approach, the text presents SAS with step-by-step examples. With over fifteen years of teaching SAS courses and over fifty combined years of teaching and consulting by the authors, this valuable reference presents data manipulation and statistical techniques, including a website with examples. This textbook is essential for teachers because the chapters are self-contained and may be used accordingly to the teacher s preference, whether for a one-semester or two-semesters course.
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Preface.

1 Getting Started.

Using SAS in a Windows Environment.

Your First SAS Analysis.

How SAS Works.

Tips and Tricks for Running SAS Programs.

Summary.

Exercises.

2 Getting Data into SAS.

Understanding SAS Data Sets.

Understanding SAS Data Set Structure.

Rules for SAS Variable Names.

Understanding Three SAS Variable Types.

Methods of Reading Data into SAS.

Going Deeper: More Techniques for Entering Data.

Summary.

Exercises.

3 Reading, Writing, and Importing Data.

Working with SAS Libraries and Permanent Data Sets.

Reading and Creating Permanent SAS Data Sets Using the Windows File.

Name Technique.

Reading Data from Permanent SAS Data Sets.

Reading and Creating Permanent SAS Data Sets Using a SAS Library.

Creating a SAS Library Using a Dialog Box.

Importing Data from Another Program.

Going Deeper: More Ways to Manage Data.

Going Deeper: Importing Microsoft Excel Data Using SAS Code.

Discovering the Contents of a SAS Data Set.

Summary.

Exercises.

4 Preparing Data for Analysis.

Labeling Variables with Explanatory Names.

Creating New Variables.

Using DROP and KEEP to Select Variables.

Subsetting Data Sets.

Using the SET Statement to Read an Existing Data Set.

Using PROC SORT.

Appending and Merging Data Sets.

Going Deeper: Using PROC FORMAT.

Summary.

Exercises.

5 Preparing to Use SAS Procedures.

Understanding SAS Support Statements.

Understanding PROC Statement Syntax.

Using the ID Statement in a SAS Procedure.

Using the LABEL Statement in a SAS Procedure.

Using the WHERE Statement in a SAS Procedure.

Using PROC PRINT.

Going Deeper: Introducing the SAS Output Delivery System (ODS).

Summary.

Exercises.

6 Evaluating Quantitative Data.

Using PROC MEANS.

Using PROC UNIVARIATE.

Going Deeper: Advanced PROC UNIVARIATE Options

Summary.

Exercises.

7 Analyzing Counts and Tables.

Using PROC FREQ.

Analyzing One-Way Frequency Tables.

Creating One-Way Frequency Tables from Summarized Data.

Analyzing Two-Way Tables.

Going Deeper: Calculating Relative Risk Measures.

Going Deeper: Inter-Rater Reliability (Kappa).

Summary.

Exercises.

8 Comparing Means Using t-tests.

Performing a One-Sample t-test.

Performing a Two-Sample t-test.

Performing a Paired t-test.

Summary.

Exercises.

9 Analysis of Variance.

Comparing Three or More Means Using One-Way Analysis of Variance.

Comparing Three or More Repeated Measures.

Going Deeper: Graphing Mean Comparisons.

Summary.

Exercises.

10 Correlation and Regression.

Correlation Analysis Using PROC CORR.

Simple Linear Regression.

Multiple Linear Regression Using PROC REG.

Going Deeper: Calculating Predictions.

Going Deeper: Residual Analysis.

Summary.

Exercises.

11 Nonparametric Analysis.

Comparing Two Independent Samples Using NPAR1WAY.

Comparing k Independent Samples (Kruskal-Wallis).

Comparing Two Dependent (Paired) Samples.

Comparing k Dependent Samples (Friedman’s Test).

Going Deeper: Nonparametric Multiple Comparisons.

Summary.

Exercises.

12 Logistic Regression.

Logistic Analysis Basics.

Performing a Logistic Analysis using PROC LOGISTIC.

Using Simple Logistic Analysis.

Multiple Binary Logistic Analysis.

Going Deeper: Assessing a Model’s Fit and Predictive Ability.

Summary.

Exercises.

13 Analysis of Variance.

Part II Analysis of Covariance.

Going Deeper: Two-Factor ANOVA Using PROC MIXED.

Going Deeper: Repeated Measures with a Grouping Factor.

Summary.

Exercises.

14 Creating Graphs.

Creating Scatterplots and Line Graphs Using GPLOT.

Creating Bar Charts and Pie Charts.

Creating Stacked Bar Charts.

Creating Mean Bars Using GCHART.

Creating Boxplots.

Going Deeper: ODS Graphics.

Summary.

Exercises.

15 Controlling Output Using ODS.

Specifying the ODS Output Format and Destination

Specifying ODS Output Style.

Using ODS to Select Specific Output Tables for Procedures.

Going Deeper: Enhancing Graphics Using ODS and Creating Hyperlinks.

Going Deeper: Capturing Information from ODS Tables.

Extended ODS Features.

Summary.

Exercises.

16 Advanced SAS Programming Topics.

Reading and Writing Data Using DDE.

Using the RETAIN Statement.

Arrays and DO Loops.

Transposing Data Sets.

Using SAS Macros.

Summary.

Exercises.

Appendix A SAS Graph Options Reference.

Using SAS Fonts.

Specifying SAS Color Choices.

Specifying Patterns for PROCS GPLOT and PROC UNIVARIATE.

Bar and Block Patterns for Bar Charts, Pie Charts, and Other Graphics.

SAS Line Styles.

Using SAS Plotting Symbols.

Appendix B SAS Function Reference.

Using SAS Functions.

Arithmetic/Mathematical Functions.

Trignometric Functions.

Date and Time Functions.

Character Functions.

Truncation Functions.

Special Use Functions.

Financial Functions.

Working with Previous Observations.

Miscellaneous Functions.

Appendix C Choosing a SAS Procedure.

Descriptive Statistics.

Comparison Tests.

Relational Analyses (Correlation and Regression).

Appendix D Quick Reference.

References.

Index.

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The Authors

Alan C. Elliott is a biostatistician and faculty member in the Department of Clinical Sciences at the University of Texas Southwestern Medical Center at Dallas. A prolific writer, he is the author or coauthor of Directory of Microcomputer Statistical Software, Microcomputing with Applications, Getting Started in Internet Auctions, Statistical Analysis Quick Reference Guidebook, and other books.

Wayne A. Woodward is a professor of statistics and chair of the Department of Statistical Science at Southern Methodist University. He is a fellow of the American Statistical Association and was the 2004 recipient of the Don Owen award for excellence in research, statistical consulting, and service to the statistical community. During the last 30 years he has served as statistical consultant to a wide variety of clients in the scientific community.

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