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Data Analysis Using SAS Enterprise Guide

$68.99 (P)

  • Date Published: August 2009
  • availability: In stock
  • format: Paperback
  • isbn: 9780521130073

$ 68.99 (P)
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About the Authors
  • 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.

    • Basic explanation of the SAS Enterprise Guide
    • Can be used as part of a course or as a stand-alone learning resource
    • Suitable for use across the social sciences
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    Product details

    • Date Published: August 2009
    • format: Paperback
    • isbn: 9780521130073
    • length: 398 pages
    • dimensions: 240 x 180 x 20 mm
    • weight: 0.64kg
    • contains: 340 b/w illus. 8 colour illus.
    • availability: In stock
  • Table of Contents

    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.

  • Resources for

    Data Analysis Using SAS Enterprise Guide

    Lawrence S. Meyers, Glenn Gamst, A. J. Guarino

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  • Instructors have used or reviewed this title for the following courses

    • Applied Stats
    • Data Management and Presentation
    • Intermediate Quantitative Analysis
    • Intermediate Statistics for the Social and Behavioral Sciences
    • Introduction to Biostatistics
    • Introduction to SAS Programming
    • Learning and Cogntion
    • Multitvariate Statistics
    • statistical methods 1
  • Authors

    Lawrence S. Meyers, California State University, Sacramento
    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, University of La Verne, California
    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, Alabama State University
    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.

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