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A Step-by-Step Approach to Using SAS® for Univariate & Multivariate Statistics, Second Edition

Book Description

Updated for SA®9, this second edition is an easy-to-understand introduction to SAS as well as to univariate and multivariate statistics. Clear explanations and simple language guide you through the research terminology, data input, data manipulation, and types of statistical analysis that are most commonly used in the social and behavioral sciences. Providing practice data inspired by actual studies, this book teaches you how to choose the right statistic, understand the assumptions underlying the procedure, prepare the SAS program for the analysis, interpret the output, and summarize the analysis and results according to the format prescribed in the Publication Manual of the American Psychological Association. Step by step you will learn how to perform the following types of analysis: simple descriptive statistics, measures of bivariate association, t tests: independent samples and paired samples, NOVA and MANOVA, multiple regression, principal component analysis, and assessing scale reliability with coefficient alpha. This text is ideally suited to students who are beginning their study of data analysis, and to professors and researchers who want a handy reference on their bookshelf.

Table of Contents

  1. Copyright
    1. Dedication
  2. Praise for the Second Edition
  3. Acknowledgments
    1. Acknowledgments from the First Edition
  4. Using This Book
    1. Purpose
    2. Audience
    3. Organization
    4. References
  5. Basic Concepts in Research and DATA Analysis
    1. Introduction: A Common Language for Researchers
    2. Steps to Follow When Conducting Research
    3. Variables, Values, and Observations
    4. Scales of Measurement
    5. Basic Approaches to Research
    6. Descriptive versus Inferential Statistical Analysis
    7. Hypothesis Testing
    8. Conclusion
  6. Introduction to SAS® Programs, SAS® Logs, and SAS® Output
    1. Introduction: What Is SAS?
    2. Three Types of SAS Files
    3. SAS Customer Support Center
    4. Conclusion
    5. Reference
  7. Data Input
    1. Introduction: Inputting Questionnaire Data versus Other Types of Data
    2. Entering Data: An Illustrative Example
    3. Inputting Data Using the DATALINES Statement
    4. Additional Guidelines
    5. Inputting a Correlation or Covariance Matrix
    6. Inputting Data Using the INFILE Statement Rather than the DATALINES Statement
    7. Controlling the Output Size and Log Pages with the OPTIONS Statement
    8. Conclusion
    9. Reference
  8. Working with Variables and Observations in SAS® Datasets
    1. Introduction: Manipulating, Subsetting, Concatenating, and Merging Data
    2. Placement of Data Manipulation and Data Subsetting Statements
    3. Data Manipulation
    4. Data Subsetting
    5. A More Comprehensive Example
    6. Concatenating and Merging Datasets
    7. Conclusion
  9. Exploring Data with PROC MEANS, PROC FREQ, PROC PRINT, and PROC UNIVARIATE
    1. Introduction: Why Perform Simple Descriptive Analyses?
    2. Example: An Abridged Volunteerism Survey
    3. Computing Descriptive Statistics with PROC MEANS
    4. Creating Frequency Tables with PROC FREQ
    5. Printing Raw Data with PROC PRINT
    6. Testing for Normality with PROC UNIVARIATE
    7. Conclusion
    8. References
  10. Measures of Bivariate Association
    1. Introduction: Significance Tests versus Measures of Association
    2. Choosing the Correct Statistic
    3. Pearson Correlations
    4. Spearman Correlations
    5. The Chi-Square Test of Independence
    6. Conclusion
    7. Assumptions Underlying the Tests
    8. References
  11. Assessing Scale Reliability with Coefficient Alpha
    1. Introduction: The Basics of Scale Reliability
    2. Coefficient Alpha
    3. Assessing Coefficient Alpha with PROC CORR
    4. Summarizing the Results
    5. Conclusion
    6. References
  12. t Tests: Independent Samples and Paired Samples
    1. Introduction: Two Types of t Tests
    2. The Independent-Samples t Test
    3. The Paired-Samples t Test
    4. Conclusion
    5. Assumptions Underlying the t Test
    6. References
  13. One-Way ANOVA with One Between-Subjects Factor
    1. Introduction: The Basics of One-Way ANOVA, Between-Subjects Design
    2. Example with Significant Differences between Experimental Conditions
    3. Example with Nonsignificant Differences between Experimental Conditions
    4. Understanding the Meaning of the F Statistic
    5. Using the LSMEANS Statement to Analyze Data from Unbalanced Designs
    6. Conclusion
    7. Assumptions Underlying One-Way ANOVA with One Between-Subjects Factor
    8. References
  14. Factorial ANOVA with Two Between-Subjects Factors
    1. Introduction to Factorial Designs
    2. Some Possible Results from a Factorial ANOVA
    3. Example with a Nonsignificant Interaction
    4. Example with a Significant Interaction
    5. Using the LSMEANS Statement to Analyze Data from Unbalanced Designs
    6. Conclusion
    7. Assumptions Underlying Factorial ANOVA with Two Between-Subjects Factors
  15. Multivariate Analysis of Variance (MANOVA) with One Between-Subjects Factor
    1. Introduction: The Basics of Multivariate Analysis of Variance
    2. Example with Significant Differences between Experimental Conditions
    3. Example with Nonsignificant Differences between Experimental Conditions
    4. Conclusion
    5. Assumptions Underlying Multivariate ANOVA with One Between-Subjects Factor
    6. References
  16. One-Way ANOVA with One Repeated-Measures Factor
    1. Introduction: What Is a Repeated-Measures Design?
    2. Example: Significant Differences in Investment Size across Time
    3. Further Notes on Repeated-Measures Analyses
    4. Conclusion
    5. Assumptions Underlying the One-Way ANOVA with One Repeated-Measures Factor
    6. References
  17. Factorial ANOVA with Repeated-Measures Factors and Between-Subjects Factors
    1. Introduction: The Basics of Mixed-Design ANOVA
    2. Some Possible Results from a Two-Way Mixed-Design ANOVA
    3. Problems with the Mixed-Design ANOVA
    4. Example with a Nonsignificant Interaction
    5. Example with a Significant Interaction
    6. Use of Other Post-Hoc Tests with the Repeated-Measures Variable
    7. Conclusion
    8. Assumptions Underlying Factorial ANOVA with Repeated-Measures Factors and Between-Subjects Factors
    9. References
  18. Multiple Regression
    1. Introduction: Answering Questions with Multiple Regression
    2. Background: Predicting a Criterion Variable from Multiple Predictors
    3. The Results of a Multiple Regression Analysis
    4. Example: A Test of the Investment Model
    5. Overview of the Analysis
    6. Gathering and Entering Data
    7. Computing Bivariate Correlations with PROC CORR
    8. Estimating the Full Multiple Regression Equation with PROC REG
    9. Computing Uniqueness Indices with PROC REG
    10. Summarizing the Results in Tables
    11. Getting the Big Picture
    12. Conclusion: Learning More about Multiple Regression
    13. Assumptions Underlying Multiple Regression
    14. References
  19. Principal Component Analysis
    1. Introduction: The Basics of Principal Component Analysis
    2. Example: Analysis of the Prosocial Orientation Inventory
    3. SAS Program and Output
    4. Steps in Conducting Principal Component Analysis
    5. An Example with Three Retained Components
    6. Conclusion
    7. Assumptions Underlying Principal Component Analysis
    8. References
  20. Choosing the Correct Statistic
    1. Introduction: Thinking about the Number and Scale of Your Variables
    2. Guidelines for Choosing the Correct Statistic
    3. Conclusion
    4. Reference
  21. Datasets
    1. Dataset from Chapter 7: Assessing Scale Reliability with Coefficient Alpha
    2. Dataset from Chapter 14: Multiple Regression
    3. Dataset from Chapter 15: Principal Component Analysis
  22. Critical Values of the F Distribution
  23. Books Available from SAS Press
    1. JMP® Books
  24. Wiley Series in Probability and Statistics
  25. Wiley Series in Probability and Statistics
  26. Index