Dataset from Chapter 15: Principal Component Analysis
by Edward J. Stepanksi Ph.d., Larry Hatcher, Norm O'Rourke
A Step-by-Step Approach to Using SAS® for Univariate & Multivariate Statistics, Second Edition
- Copyright
- Praise for the Second Edition
- Acknowledgments
- Using This Book
- Basic Concepts in Research and DATA Analysis
- Introduction to SAS® Programs, SAS® Logs, and SAS® Output
- Data Input
- Introduction: Inputting Questionnaire Data versus Other Types of Data
- Entering Data: An Illustrative Example
- Inputting Data Using the DATALINES Statement
- Additional Guidelines
- Inputting a Correlation or Covariance Matrix
- Inputting Data Using the INFILE Statement Rather than the DATALINES Statement
- Controlling the Output Size and Log Pages with the OPTIONS Statement
- Conclusion
- Reference
- Working with Variables and Observations in SAS® Datasets
- Exploring Data with PROC MEANS, PROC FREQ, PROC PRINT, and PROC UNIVARIATE
- Measures of Bivariate Association
- Assessing Scale Reliability with Coefficient Alpha
- t Tests: Independent Samples and Paired Samples
- One-Way ANOVA with One Between-Subjects Factor
- Introduction: The Basics of One-Way ANOVA, Between-Subjects Design
- Example with Significant Differences between Experimental Conditions
- Example with Nonsignificant Differences between Experimental Conditions
- Understanding the Meaning of the F Statistic
- Using the LSMEANS Statement to Analyze Data from Unbalanced Designs
- Conclusion
- Assumptions Underlying One-Way ANOVA with One Between-Subjects Factor
- References
- Factorial ANOVA with Two Between-Subjects Factors
- Introduction to Factorial Designs
- Some Possible Results from a Factorial ANOVA
- Example with a Nonsignificant Interaction
- Example with a Significant Interaction
- Using the LSMEANS Statement to Analyze Data from Unbalanced Designs
- Conclusion
- Assumptions Underlying Factorial ANOVA with Two Between-Subjects Factors
- Multivariate Analysis of Variance (MANOVA) with One Between-Subjects Factor
- One-Way ANOVA with One Repeated-Measures Factor
- Factorial ANOVA with Repeated-Measures Factors and Between-Subjects Factors
- Introduction: The Basics of Mixed-Design ANOVA
- Some Possible Results from a Two-Way Mixed-Design ANOVA
- Problems with the Mixed-Design ANOVA
- Example with a Nonsignificant Interaction
- Example with a Significant Interaction
- Use of Other Post-Hoc Tests with the Repeated-Measures Variable
- Conclusion
- Assumptions Underlying Factorial ANOVA with Repeated-Measures Factors and Between-Subjects Factors
- References
- Multiple Regression
- Introduction: Answering Questions with Multiple Regression
- Background: Predicting a Criterion Variable from Multiple Predictors
- The Results of a Multiple Regression Analysis
- Example: A Test of the Investment Model
- Overview of the Analysis
- Gathering and Entering Data
- Computing Bivariate Correlations with PROC CORR
- Estimating the Full Multiple Regression Equation with PROC REG
- Computing Uniqueness Indices with PROC REG
- Summarizing the Results in Tables
- Getting the Big Picture
- Conclusion: Learning More about Multiple Regression
- Assumptions Underlying Multiple Regression
- References
- Principal Component Analysis
- Introduction: The Basics of Principal Component Analysis
- Example: Analysis of the Prosocial Orientation Inventory
- SAS Program and Output
- Steps in Conducting Principal Component Analysis
- An Example with Three Retained Components
- Conclusion
- Assumptions Underlying Principal Component Analysis
- References
- Choosing the Correct Statistic
- Datasets
- Critical Values of the F Distribution
- Books Available from SAS Press
- Wiley Series in Probability and Statistics
- Wiley Series in Probability and Statistics
- Index
Dataset from Chapter 15: Principal Component Analysis
The dataset described in Chapter 15 is identical to the dataset from Chapter 7 that appears in the first section.
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