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Index
Cover
Title
Copyright
Dedication
Contents
Preface
1. Introduction
1.1 Introduction
1.2 Type I Error, Type II Error, and Power
1.3 Multiple Statistical Tests and the Probability of Spurious Results
1.4 Statistical Significance Versus Practical Importance
1.5 Outliers
1.6 Missing Data
1.7 Unit or Participant Nonresponse
1.8 Research Examples for Some Analyses Considered in This Text
1.9 The SAS and SPSS Statistical Packages
1.10 SAS and SPSS Syntax
1.11 SAS and SPSS Syntax and Data Sets on the Internet
1.12 Some Issues Unique to Multivariate Analysis
1.13 Data Collection and Integrity
1.14 Internal and External Validity
1.15 Conflict of Interest
1.16 Summary
1.17 Exercises
2. Matrix Algebra
2.1 Introduction
2.2 Addition, Subtraction, and Multiplication of a Matrix by a Scalar
2.3 Obtaining the Matrix of Variances and Covariances
2.4 Determinant of a Matrix
2.5 Inverse of a Matrix
2.6 SPSS Matrix Procedure
2.7 SAS IML Procedure
2.8 Summary
2.9 Exercises
3. Multiple Regression for Prediction
3.1 Introduction
3.2 Simple Regression
3.3 Multiple Regression for Two Predictors: Matrix Formulation
3.4 Mathematical Maximization Nature of Least Squares Regression
3.5 Breakdown of Sum of Squares and F Test for Multiple Correlation
3.6 Relationship of Simple Correlations to Multiple Correlation
3.7 Multicollinearity
3.8 Model Selection
3.9 Two Computer Examples
3.10 Checking Assumptions for the Regression Model
3.11 Model Validation
3.12 Importance of the Order of the Predictors
3.13 Other Important Issues
3.14 Outliers and Influential Data Points
3.15 Further Discussion of the Two Computer Examples
3.16 Sample Size Determination for a Reliable Prediction Equation
3.17 Other Types of Regression Analysis
3.18 Multivariate Regression
3.19 Summary
3.20 Exercises
4. Two-Group Multivariate Analysis of Variance
4.1 Introduction
4.2 Four Statistical Reasons for Preferring a Multivariate Analysis
4.3 The Multivariate Test Statistic as a Generalization of the Univariate t Test
4.4 Numerical Calculations for a Two-Group Problem
4.5 Three Post Hoc Procedures
4.6 SAS and SPSS Control Lines for Sample Problem and Selected Output
4.7 Multivariate Significance but No Univariate Significance
4.8 Multivariate Regression Analysis for the Sample Problem
4.9 Power Analysis
4.10 Ways of Improving Power
4.11 A Priori Power Estimation for a Two-Group MANOVA
4.12 Summary
4.13 Exercises
5. K-Group MANOVA: A Priori and Post Hoc Procedures
5.1 Introduction
5.2 Multivariate Regression Analysis for a Sample Problem
5.3 Traditional Multivariate Analysis of Variance
5.4 Multivariate Analysis of Variance for Sample Data
5.5 Post Hoc Procedures
5.6 The Tukey Procedure
5.7 Planned Comparisons
5.8 Test Statistics for Planned Comparisons
5.9 Multivariate Planned Comparisons on SPSS MANOVA
5.10 Correlated Contrasts
5.11 Studies Using Multivariate Planned Comparisons
5.12 Other Multivariate Test Statistics
5.13 How Many Dependent Variables for a MANOVA?
5.14 Power Analysis—A Priori Determination of Sample Size
5.15 Summary
5.16 Exercises
6. Assumptions in MANOVA
6.1 Introduction
6.2 ANOVA and MANOVA Assumptions
6.3 Independence Assumption
6.4 What Should Be Done With Correlated Observations?
6.5 Normality Assumption
6.6 Multivariate Normality
6.7 Assessing the Normality Assumption
6.8 Homogeneity of Variance Assumption
6.9 Homogeneity of the Covariance Matrices
6.10 Summary
6.11 Complete Three-Group MANOVA Example
6.12 Example Results Section for One-Way MANOVA
6.13 Analysis Summary
Appendix 6.1 Analyzing Correlated Observations
Appendix 6.2 Multivariate Test Statistics for Unequal Covariance Matrices
6.14 Exercises
7. Factorial ANOVA and MANOVA
7.1 Introduction
7.2 Advantages of a Two-Way Design
7.3 Univariate Factorial Analysis
7.4 Factorial Multivariate Analysis of Variance
7.5 Weighting of the Cell Means
7.6 Analysis Procedures for Two-Way MANOVA
7.7 Factorial MANOVA With SeniorWISE Data
7.8 Example Results Section for Factorial MANOVA With SeniorWise Data
7.9 Three-Way MANOVA
7.10 Factorial Descriptive Discriminant Analysis
7.11 Summary
7.12 Exercises
8. Analysis of Covariance
8.1 Introduction
8.2 Purposes of ANCOVA
8.3 Adjustment of Posttest Means and Reduction of Error Variance
8.4 Choice of Covariates
8.5 Assumptions in Analysis of Covariance
8.6 Use of ANCOVA With Intact Groups
8.7 Alternative Analyses for Pretest–Posttest Designs
8.8 Error Reduction and Adjustment of Posttest Means for Several Covariates
8.9 MANCOVA—Several Dependent Variables and Several Covariates
8.10 Testing the Assumption of Homogeneous Hyperplanes on SPSS
8.11 Effect Size Measures for Group Comparisons in MANCOVA/ANCOVA
8.12 Two Computer Examples
8.13 Note on Post Hoc Procedures
8.14 Note on the Use of MVMM
8.15 Example Results Section for MANCOVA
8.16 Summary
8.17 Analysis Summary
8.18 Exercises
9. Exploratory Factor Analysis
9.1 Introduction
9.2 The Principal Components Method
9.3 Criteria for Determining How Many Factors to Retain Using Principal Components Extraction
9.4 Increasing Interpretability of Factors by Rotation
9.5 What Coefficients Should Be Used for Interpretation?
9.6 Sample Size and Reliable Factors
9.7 Some Simple Factor Analyses Using Principal Components Extraction
9.8 The Communality Issue
9.9 The Factor Analysis Model
9.10 Assumptions for Common Factor Analysis
9.11 Determining How Many Factors Are Present With Principal Axis Factoring
9.12 Exploratory Factor Analysis Example With Principal Axis Factoring
9.13 Factor Scores
9.14 Using SPSS in Factor Analysis
9.15 Using SAS in Factor Analysis
9.16 Exploratory and Confirmatory Factor Analysis
9.17 Example Results Section for EFA of Reactions-to- Tests Scale
9.18 Summary
9.19 Exercises
10. Discriminant Analysis
10.1 Introduction
10.2 Descriptive Discriminant Analysis
10.3 Dimension Reduction Analysis
10.4 Interpreting the Discriminant Functions
10.5 Minimum Sample Size
10.6 Graphing the Groups in the Discriminant Plane
10.7 Example With SeniorWISE Data
10.8 National Merit Scholar Example
10.9 Rotation of the Discriminant Functions
10.10 Stepwise Discriminant Analysis
10.11 The Classification Problem
10.12 Linear Versus Quadratic Classification Rule
10.13 Characteristics of a Good Classification Procedure
10.14 Analysis Summary of Descriptive Discriminant Analysis
10.15 Example Results Section for Discriminant Analysis of the National Merit Scholar Example
10.16 Summary
10.17 Exercises
11. Binary Logistic Regression
11.1 Introduction
11.2 The Research Example
11.3 Problems With Linear Regression Analysis
11.4 Transformations and the Odds Ratio With a Dichotomous Explanatory Variable
11.5 The Logistic Regression Equation With a Single Dichotomous Explanatory Variable
11.6 The Logistic Regression Equation With a Single Continuous Explanatory Variable
11.7 Logistic Regression as a Generalized Linear Model
11.8 Parameter Estimation
11.9 Significance Test for the Entire Model and Sets of Variables
11.10 McFadden’s Pseudo R-Square for Strength of Association
11.11 Significance Tests and Confidence Intervals for Single Variables
11.12 Preliminary Analysis
11.13 Residuals and Influence
11.14 Assumptions
11.15 Other Data Issues
11.16 Classification
11.17 Using SAS and SPSS for Multiple Logistic Regression
11.18 Using SAS and SPSS to Implement the Box–Tidwell Procedure
11.19 Example Results Section for Logistic Regression With Diabetes Prevention Study
11.20 Analysis Summary
11.21 Exercises
12. Repeated-Measures Analysis
12.1 Introduction
12.2 Single-Group Repeated Measures
12.3 The Multivariate Test Statistic for Repeated Measures
12.4 Assumptions in Repeated-Measures Analysis
12.5 Computer Analysis of the Drug Data
12.6 Post Hoc Procedures in Repeated-Measures Analysis
12.7 Should We Use the Univariate or Multivariate Approach?
12.8 One-Way Repeated Measures—A Trend Analysis
12.9 Sample Size for Power = .80 in Single-Sample Case
12.10 Multivariate Matched-Pairs Analysis
12.11 One-Between and One-Within Design
12.12 Post Hoc Procedures for the One-Between and One-Within Design
12.13 One-Between and Two-Within Factors
12.14 Two-Between and One-Within Factors
12.15 Two-Between and Two-Within Factors
12.16 Totally Within Designs
12.17 Planned Comparisons in Repeated-Measures Designs
12.18 Profile Analysis
12.19 Doubly Multivariate Repeated-Measures Designs
12.20 Summary
12.21 Exercises
13. Hierarchical Linear Modeling
13.1 Introduction
13.2 Problems Using Single-Level Analyses of Multilevel Data
13.3 Formulation of the Multilevel Model
13.4 Two-Level Model—General Formation
13.5 Example 1: Examining School Differences in Mathematics
13.6 Centering Predictor Variables
13.7 Sample Size
13.8 Example 2: Evaluating the Efficacy of a Treatment
13.9 Summary
14. Multivariate Multilevel Modeling
14.1 Introduction
14.2 Benefits of Conducting a Multivariate Multilevel Analysis
14.3 Research Example
14.4 Preparing a Data Set for MVMM Using SAS and SPSS
14.5 Incorporating Multiple Outcomes in the Level-1 Model
14.6 Example 1: Using SAS and SPSS to Conduct Two-Level Multivariate Analysis
14.7 Example 2: Using SAS and SPSS to Conduct Three-Level Multivariate Analysis
14.8 Summary
14.9 SAS and SPSS Commands Used to Estimate All Models in the Chapter
15. Canonical Correlation
15.1 Introduction
15.2 The Nature of Canonical Correlation
15.3 Significance Tests
15.4 Interpreting the Canonical Variates
15.5 Computer Example Using SAS CANCORR
15.6 A Study That Used Canonical Correlation
15.7 Using SAS for Canonical Correlation on Two Sets of Factor Scores
15.8 The Redundancy Index of Stewart and Love
15.9 Rotation of Canonical Variates
15.10 Obtaining More Reliable Canonical Variates
15.11 Summary
15.12 Exercises
16. Structural Equation Modeling
16.1 Introduction
16.2 Notation, Terminology, and Software
16.3 Causal Inference
16.4 Fundamental Topics in SEM
16.5 Three Principal SEM Techniques
16.6 Observed Variable Path Analysis
16.7 Observed Variable Path Analysis With the Mueller Study
16.8 Confirmatory Factor Analysis
16.9 CFA With Reactions-to-Tests Data
16.10 Latent Variable Path Analysis
16.11 Latent Variable Path Analysis With Exercise Behavior Study
16.12 SEM Considerations
16.13 Additional Models in SEM
16.14 Final Thoughts
Appendix 16.1 Abbreviated SAS Output for Final Observed Variable Path Model
Appendix 16.2 Abbreviated SAS Output for the Final Latent Variable Path Model for Exercise Behavior
Appendix A: Statistical Tables
Appendix B: Obtaining Nonorthogonal Contrasts in Repeated Measures Designs
Detailed Answers
Index
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