Data Analysis Using SAS Enterprise Guide

Lawrence S. Meyers is Professor of Psychology at Sacramento State University. He teaches undergraduate and graduate courses in research design, data analysis, data interpretation, testing and measurement, and history and systems of psychology. He was coauthor of a textbook on research methods in the 1970s, has recently coauthored books on multivariate research design and analysis of variance, and has more than three dozen publications; some of his relatively recent work has been in areas such as measurement and testing and positive psychology. He received his doctorate from Adelphi University and worked on a National Science Foundation Postdoctoral Fellowship at the University of Texas, Austin, and Purdue University. Glenn Gamst is Professor and Chair of the Psychology Department at the University of La Verne, where he teaches the doctoral advanced statistics sequence. He received his PhD from the University of Arkansas in experimental psychology. His research interests include the effects of multicultural variables such as client-therapist ethnic match, client acculturation status and ethnic identity, and therapist cultural competence on clinical outcomes. Additional research interests focus on conversation memory and discourse processing. A. J. Guarino is on the faculty at Alabama State University where he teaches graduate statistics courses in the Psychology department. He received his bachelor's degree from the University of California, Berkeley, and earned a doctorate in statistics and research methodologies from the University of Southern California through the Department of Educational Psychology.

Part I. Introducing SAS Enterprise Guide: 1. SAS Enterprise Guide projects; 2. Placing data into SAS Enterprise Guide projects; Part II. Performing and Viewing Output: 3. Performing statistical analyses in SAS Enterprise Guide; 4. Managing and viewing output; Part III. Manipulating Data: 5. Sorting data and selecting cases; 6. Recoding existing variables; 7. Computing new variables; Part IV. Describing Data: 8. Descriptive statistics; 9. Graphing data; 10. Standardizing variables based on the sample data; 11. Standardizing variables based on existing norms; Part V. Score Distribution Issues: 12. Detecting outliers; 13. Assessing normality; 14. Nonlinearly transforming variables in order to meet underlying assumptions; Part VI. Correlation and Prediction: 15. Bivariate correlation: Pearson product moment and Spearman rho correlations; 16. Simple linear regression; 17. Multiple linear regression; 18. Simple logistic regression; 19. Multiple logistic regression; Part VII. Comparing Means t Tests: 20. Independent groups t test; 21. Correlated samples t test; 22. Single sample t test; Part VIII. Comparing means ANOVA: 23. One-way between subjects analysis of variance; 24. Two-way between subjects design; 25. One-way within subjects analysis of variance; 26. Two-way mixed ANOVA design; Part IX. Nonparametric Procedures: 27. One-way chi square; 28. Two-way chi square; 29. Nonparametric between subjects one-way ANOVA; Part X. Advanced ANOVA Techniques: 30. One-way between subjects analysis of covariance; 31. One-way between subjects multivariate analysis of variance; Part XI. Analysis of Structure: 32. Factor analysis; 33. Canonical correlation analysis.