Data Analysis Using SAS Enterprise Guide

Erscheinungsjahr 2009
ISBN: 978-0-521-13007-3
Verlag: Cambridge University Press

Buch, Englisch, 398 Seiten, Format (B × H): 179 mm x 236 mm, Gewicht: 645 g

This book presents the basic procedures for utilizing SAS Enterprise Guide to analyze statistical data. SAS Enterprise Guide is a graphical user interface (point and click) to the main SAS application. Each chapter contains a brief conceptual overview and then guides the reader through concrete step-by-step examples to complete the analyses. The eleven sections of the book cover a wide range of statistical procedures including descriptive statistics, correlation and simple regression, t tests, one-way chi square, data transformations, multiple regression, analysis of variance, analysis of covariance, multivariate analysis of variance, factor analysis, and canonical correlation analysis. Designed to be used either as a stand-alone resource or as an accompaniment to a statistics course, the book offers a smooth path to statistical analysis with SAS Enterprise Guide for advanced undergraduate and beginning graduate students, as well as professionals in psychology, education, business, health, social work, sociology, and many other fields.

Autoren/Hrsg.

Meyers, Lawrence S

Gamst, Glenn

Guarino, A J

Fachgebiete

• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Angewandte Mathematik, Mathematische Modelle
• Sozialwissenschaften Soziologie | Soziale Arbeit Soziologie Allgemein Empirische Sozialforschung, Statistik

Inhaltsverzeichnis

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.

Über Autor(innen)

Meyers, Lawrence S
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.

Gamst, Glenn
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.

Guarino, A J
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.