Preface
About the Authors
PART I: FUNDAMENTALS OF MULTIVARIATE DESIGN
Chapter 1: An Introduction to Multivariate Design
| 1.1 The Use of Multivariate Designs |
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| 1.2 The Definition of the Multivariate Domain |
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| 1.3 The Importance of Multivariate Designs |
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| 1.4 The General Form of a Variate |
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| 1.5 The Type of Variables Combined to Form a Variate |
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| 1.6 The General Organization of the Book |
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Chapter 2: Some Fundamental Research Design Concepts
| 2.1 Populations and Samples |
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| 2.2 Variables and Scales of Measurement |
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| 2.3 Independent Variables, Dependent Variables, and Covariates |
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| 2.4 Between Subjects and Within Subjects Independent Variables |
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| 2.5 Latent Variables and Measured Variables |
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| 2.6 Endogenous and Exogenous Variables |
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| 2.7 Statistical Significance |
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Chapter 3A: Data Screening
| 3A.3 Patterns of Missing Values |
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| 3A.4 Overview of Methods of Handling Missing Data |
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| 3A.5 Deletion Methods of Handling Missing Data |
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| 3A.6 Single Imputation Methods of Handling Missing Data |
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| 3A.7 Modern Imputation Methods of Handling Missing Data |
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| 3A.8 Recommendations for Handling Missing Data |
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| 3A.10 Using Descriptive Statistics in Data Screening |
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| 3A.11 Using Pictorial Representations in Data Screening |
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| 3A.12 Multivariate Statistical Assumptions Underlying the General Linear Model |
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| 3A.13 Data Transformations |
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| 3A.14 Recommended Readings |
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Chapter 3B: Data Screening Using IBM SPSS
| 3B.1 The Look of IBM SPSS |
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| 3B.2 Data Cleaning: All Variables |
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| 3B.3 Screening Quantitative Variables |
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| 3B.4 Missing Values: Overview |
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| 3B.5 Missing Value Analysis |
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| 3B.7 Mean Substitution as a Single Imputation Approach |
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| 3B.11 Multivariate Outliers |
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| 3B.12 Screening Within Levels of Categorical Variables |
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| 3B.13 Reporting the Data Screening Results |
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PART II: BASIC AND ADVANCED REGRESSION ANALYSIS
Chapter 4A: Bivariate Correlation and Simple Linear Regression
| 4A.1 The Concept of Correlation |
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| 4A.2 Different Types of Relationships |
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| 4A.3 Statistical Significance of the Correlation Coefficient |
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| 4A.4 Strength of Relationship |
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| 4A.5 Pearson Correlation Using a Quantitative Variable and a Dichotomous Nominal Variable |
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| 4A.6 Simple Linear Regression |
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| 4A.7 Statistical Error in Prediction: Why Bother With Regression? |
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| 4A.8 How Simple Linear Regression Is Used |
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| 4A.9 Factors Affecting the Computed Pearson r and Regression Coefficients |
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| 4A.10 Recommended Readings |
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Chapter 4B: Bivariate Correlation and Simple Linear Regression Using IBM SPSS
| 4B.1 Bivariate Correlation: Analysis Setup |
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| 4B.2 Simple Linear Regression |
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| 4B.3 Reporting Simple Linear Regression Results |
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Chapter 5A: Multiple Regression Analysis
| 5A.1 General Considerations |
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| 5A.2 Statistical Regression Methods |
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| 5A.3 The Two Classes of Variables in a Multiple Regression Analysis |
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| 5A.4 Multiple Regression Research |
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| 5A.5 The Regression Equations |
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| 5A.6 The Variate in Multiple Regression |
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| 5A.7 The Standard (Simultaneous) Regression Method |
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| 5A.9 The Squared Multiple Correlation |
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| 5A.10 The Squared Semipartial Correlation |
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| 5A.11 Structure Coefficients |
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| 5A.12 Statistical Summary of the Regression Solution |
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| 5A.13 Evaluating the Overall Model |
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| 5A.14 Evaluating the Individual Predictor Results |
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| 5A.15 Step Methods of Building the Model |
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| 5A.17 The Backward Method |
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| 5A.18 Backward Versus Forward Solutions |
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| 5A.19 The Stepwise Method |
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| 5A.20 Evaluation of the Statistical Methods |
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| 5A.21 Collinearity and Multicollinearity |
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| 5A.22 Recommended Readings |
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Chapter 5B: Multiple Regression Analysis Using IBM SPSS
| 5B.1 Standard Multiple Regression |
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| 5B.2 Stepwise Multiple Regression |
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Chapter 6A: Beyond Statistical Regression
| 6A.1 A Larger World of Regression |
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| 6A.2 Hierarchical Linear Regression |
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| 6A.3 Suppressor Variables |
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| 6A.4 Linear and Nonlinear Regression |
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| 6A.5 Dummy and Effect Coding |
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| 6A.6 Moderator Variables and Interactions |
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| 6A.7 Simple Mediation: A Minimal Path Analysis |
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| 6A.8 Recommended Readings |
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Chapter 6B: Beyond Statistical Regression Using IBM SPSS
| 6B.1 Hierarchical Linear Regression |
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| 6B.2 Polynomial Regression |
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| 6B.3 Dummy and Effect Coding |
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| 6B.4 Interaction Effects of Quantitative Variables in Regression |
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Chapter 7A: Canonical Correlation Analysis
| 7A.2 Canonical Functions or Roots |
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| 7A.3 The Index of Shared Variance |
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| 7A.4 The Dynamics of Extracting Canonical Functions |
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| 7A.5 Accounting for Variance: Eigenvalues and Theta Values |
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| 7A.6 The Multivariate Tests of Statistical Significance |
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| 7A.7 Specifying the Amount of Variance Explained in Canonical Correlation Analysis |
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| 7A.8 Coefficients Associated With the Canonical Functions |
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| 7A.9 Interpreting the Canonical Functions |
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| 7A.10 Recommended Readings |
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Chapter 7B: Canonical Correlation Analysis Using IBM SPSS
| 7B.1 Canonical Correlation: Analysis Setup |
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| 7B.2 Canonical Correlation: Overview of Output |
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| 7B.3 Canonical Correlation: Multivariate Tests of Significance |
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| 7B.4 Canonical Correlation: Eigenvalues and Canonical Correlations |
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| 7B.5 Canonical Correlation: Dimension Reduction Analysis |
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| 7B.6 Canonical Correlation: How Many Functions Should Be Interpreted? |
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| 7B.7 Canonical Correlation: The Coefficients in the Output |
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| 7B.8 Canonical Correlation: Interpreting the Dependent Variates |
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| 7B.9 Canonical Correlation: Interpreting the Predictor Variates |
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| 7B.10 Canonical Correlation: Interpreting the Canonical Functions |
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| 7B.11 Reporting of the Canonical Correlation Analysis Results |
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Chapter 8A: Multilevel Modeling
| 8A.1 The Name of the Procedure |
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| 8A.2 The Rise of Multilevel Modeling |
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| 8A.3 The Defining Feature of Multilevel Modeling: Hierarchically Structured Data |
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| 8A.4 Nesting and the Independence Assumption |
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| 8A.5 The Intraclass Correlation as an Index of Clustering |
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| 8A.6 Consequences of Violating the Independence Assumption |
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| 8A.7 Some Ways in Which Level 2 Groups Can Differ |
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| 8A.8 The Random Coefficient Regression Model |
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| 8A.9 Centering the Variables |
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| 8A.10 The Process of Building the Multilevel Model |
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| 8A.11 Recommended Readings |
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Chapter 8B: Multilevel Modeling Using IBM SPSS
| 8B.2 Assessing the Unconditional Model |
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| 8B.3 Centering the Covariates |
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| 8B.4 Building the Multilevel Models: Overview |
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| 8B.5 Building the First Model |
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| 8B.6 Building the Second Model |
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| 8B.7 Building the Third Model |
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| 8B.8 Building the Fourth Model |
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| 8B.9 Reporting the Multilevel Modeling Results |
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Chapter 9A: Binary and Multinomial Logistic Regression and ROC Analysis
| 9A.2 The Variables in Logistic Regression Analysis |
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| 9A.3 Assumptions of Logistic Regression |
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| 9A.4 Coding of the Binary Variables in Logistic Regression |
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| 9A.5 The Shape of the Logistic Regression Function |
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| 9A.6 Probability, Odds, and Odds Ratios |
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| 9A.7 The Logistic Regression Model |
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| 9A.8 Interpreting Logistic Regression Results in Simpler Language |
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| 9A.9 Binary Logistic Regression With a Single Binary Predictor |
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| 9A.10 Binary Logistic Regression With a Single Quantitative Predictor |
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| 9A.11 Binary Logistic Regression With a Categorical and a Quantitative Predictor |
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| 9A.12 Evaluating the Logistic Model |
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| 9A.13 Strategies for Building the Logistic Regression Model |
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| 9A.15 Recommended Readings |
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Chapter 9B: Binary and Multinomial Logistic Regression and ROC Analysis Using IBM SPSS
| 9B.1 Binary Logistic Regression |
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| 9B.3 Multinomial Logistic Regression |
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PART III: STRUCTURAL RELATIONSHIPS OF MEASURED AND LATENT VARIABLES
Chapter 10A: Principal Components Analysis and Exploratory Factor Analysis
| 10A.1 Orientation and Terminology |
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| 10A.2 Origins of Factor Analysis |
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| 10A.3 How Factor Analysis Is Used in Psychological Research |
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| 10A.4 The General Organization of This Chapter |
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| 10A.5 Where the Analysis Begins: The Correlation Matrix |
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| 10A.6 Acquiring Perspective on Factor Analysis |
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| 10A.7 Important Distinctions Within Our Generic Label of Factor Analysis |
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| 10A.8 The First Phase: Component Extraction |
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| 10A.9 Distances of Variables From a Component |
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| 10A.10 Principal Components Analysis Versus Factor Analysis |
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| 10A.11 Different Extraction Methods |
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| 10A.12 Recommendations Concerning Extraction |
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| 10A.13 The Rotation Process |
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| 10A.14 Orthogonal Factor Rotation Methods |
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| 10A.15 Oblique Factor Rotation |
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| 10A.16 Choosing Between Orthogonal and Oblique Rotation Strategies |
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| 10A.17 The Factor Analysis Output |
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| 10A.18 Interpreting Factors Based on the Rotated Matrices |
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| 10A.19 Selecting the Factor Solution |
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| 10A.20 Sample Size Issues |
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| 10A.21 Building Reliable Subscales |
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| 10A.22 Recommended Readings |
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Chapter 10B: Principal Components Analysis and Exploratory Factor Analysis Using IBM SPSS
| 10B.2 Preliminary Principal Components Analysis |
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| 10B.3 Principal Components Analysis With a Promax Rotation: Two-Component Solution |
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| 10B.4 ULS Analysis With a Promax Rotation: Two-Factor Solution |
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| 10B.5 Wrap-Up of the Two-Factor Solution |
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| 10B.6 Looking for Six Dimensions |
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| 10B.7 Principal Components Analysis With a Promax Rotation: Six-Component Solution |
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| 10B.8 ULS Analysis With a Promax Rotation: Six-Component Solution |
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| 10B.9 Principal Axis Factor Analysis With a Promax Rotation: Six-Component Solution |
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| 10B.10 Wrap-Up of the Six-Factor Solution |
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| 10B.11 Assessing Reliability: Our General Strategy |
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| 10B.12 Assessing Reliability: The Global Domains |
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| 10B.13 Assessing Reliability: The Six Item Sets Based on the ULS/Promax Structure |
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| 10B.14 Computing Scales Based on the ULS Promax Structure |
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| 10B.15 Using the Computed Variables in Further Analyses |
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| 10B.16 Reporting the Exploratory Factor Analysis Results |
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Chapter 11A: Confirmatory Factor Analysis
| 11A.2 The General Form of a Confirmatory Model |
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| 11A.3 The Difference Between Latent and Measured Variables |
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| 11A.4 Contrasting Principal Components Analysis and Exploratory Factor Analysis With Confirmatory Factor Analysis |
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| 11A.5 Confirmatory Factor Analysis Is Theory Based |
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| 11A.6 The Logic of Performing a Confirmatory Factor Analysis |
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| 11A.7 Model Specification |
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| 11A.8 Model Identification |
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| 11A.10 Model Evaluation Overview |
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| 11A.11 Assessing Fit of Hypothesized Models |
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| 11A.12 Model Estimation: Assessing Pattern Coefficients |
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| 11A.13 Model Respecification |
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| 11A.14 General Considerations |
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| 11A.15 Recommended Readings |
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Chapter 11B: Confirmatory Factor Analysis Using IBM SPSS Amos
| 11B.1 Using IBM SPSS Amos |
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| 11B.3 Analysis Setup to Specify the Model |
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| 11B.4 Model Identification |
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| 11B.5 Structuring and Performing the Analysis |
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| 11B.6 Working With the Analysis Output |
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| 11B.7 Respecifying the Model |
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| 11B.8 Output From the Respecified Model |
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| 11B.9 Reporting Confirmatory Factor Analysis Results |
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Chapter 12A: Path Analysis: Multiple Regression Analysis
| 12A.2 The Concept of a Path Model |
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| 12A.3 The Appeal of Path Over Multiple Regression Analysis |
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| 12A.4 Causality and Path Analysis |
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| 12A.5 The Roles Played by Variables in a Path Structure |
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| 12A.6 The Assumptions of Path Analysis |
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| 12A.7 Missing Values in Path Analysis |
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| 12A.8 The Multiple Regression Approach to Path Analysis |
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| 12A.9 Indirect and Total Effects |
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| 12A.10 Recommended Readings |
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Chapter 12B: Path Analysis: Multiple Regression Analysis Using IBM SPSS
| 12B.1 The Data Set and Model Used in Our Example |
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| 12B.2 Identifying the Variables in Each Analysis |
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| 12B.3 Predicting Months_Teaching |
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| 12B.4 Predicting Good_Teaching |
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| 12B.5 Reporting the Path Analysis Results |
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Chapter 13A: Path Analysis: Structural Equation Modeling
| 13A.1 Comparing Multiple Regression and Structural Equation Model Approaches |
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| 13A.2 Differences Between the Equations Underlying Multiple Regression and Structural Equation Model Procedures |
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| 13A.3 Configuring the Structural Model |
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| 13A.4 Identifying the Structural Equation Model |
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| 13A.5 Recommended Readings |
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Chapter 13B: Path Analysis: Structural Equation Modeling Using IBM SPSS Amos
| 13B.2 The Data Set and Model Used in Our Example |
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| 13B.4 The Analysis Output |
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| 13B.5 Reporting the Path Analysis Results |
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Chapter 14A: Structural Equation Modeling
| 14A.1 Overview of Structural Equation Modeling |
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| 14A.2 Model Quality and the Structural Aspects of the Model |
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| 14A.3 Latent Variables and Their Indicators |
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| 14A.4 Identifying Structural Equation Models |
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| 14A.5 Recommended Readings |
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Chapter 14B: Structural Equation Modeling Using IBM SPSS Amos
| 14B.2 The Data Set and Model Used in Our Example |
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| 14B.3 Model Configuration and Analysis Setup |
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| 14B.4 Model Identification |
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| 14B.5 Generating the Output |
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| 14B.6 Analysis Output for the Model |
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| 14B.7 Configuring and Evaluating the Respecified Model |
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| 14B.8 Summary of the Results of the Model and Noting the Follow-up Analyses |
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| 14B.9 Assessing the Indirect Effects in the Full Model |
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| 14B.10 Assessing the Possibility of Having Obtained Complete Mediation in the Full Model |
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| 14B.11 Assessing Mediation Through Self_ Regulation |
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| 14B.12 Assessing Mediation Through Extrinsic_Goals |
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| 14B.13 Synthesis of the Results |
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| 14B.14 Reporting the SEM Results |
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Chapter 15A: Measurement and Structural Equation Modeling Invariance: Applying a Model to a Different Group
| 15A.2 The General Strategy Used to Compare Groups |
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| 15A.3 The Omnibus Model Comparison Phase |
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| 15A.4 The Coefficient Comparison Phase |
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| 15A.5 Recommended Readings |
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Chapter 15B: Assessing Measurement and Structural Invariance for Confirmatory Factor Analysis and Structural Equation Models Using IBM SPSS Amos
| 15B.1 Overview and General Analysis Strategy |
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| 15B.2 The Data Set Used for Examining Invariance in Both the Confirmatory Factor Analysis and Structural Equation Model Examples |
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| 15B.3 Confirmatory Factor Analysis Invariance: Global Preliminary Analysis |
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| 15B.4 Confirmatory Factor Analysis Invariance: Group 1 (Rural) Analysis |
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| 15B.5 Confirmatory Factor Analysis Invariance: Group 2 Analysis |
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| 15B.6 Confirmatory Factor Analysis Invariance: Model Evaluation Setup |
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| 15B.7 Confirmatory Factor Analysis Invariance: Model Evaluation Output |
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| 15B.8 Reporting the Confirmatory Factor Analysis Invariance Results |
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| 15B.9 Structural Equation Model Invariance: Global Preliminary Analysis |
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| 15B.10 Structural Equation Model Invariance: Group 1 (Rural) Analysis |
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| 15B.11 Structural Equation Model Invariance: Group 2 Analysis |
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| 15B.12 Structural Equation Model Invariance: Model Evaluation Setup |
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| 15B.13 Structural Equation Model Invariance: Model Evaluation Output |
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| 15B.14 Reporting the Structural Equation Model Invariance Results |
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PART IV: CONSOLIDATING STIMULI AND CASES
Chapter 16A: Multidimensional Scaling
| 16A.2 The Paired Comparison Method |
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| 16A.3 Dissimilarity Data in MDS |
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| 16A.4 Similarity/Dissimilarity Conceived as an Index of Distance |
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| 16A.5 Dimensionality in MDS |
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| 16A.6 Data Collection Methods |
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| 16A.7 Similarity Versus Dissimilarity |
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| 16A.9 A Classification Schema for MDS Techniques |
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| 16A.10 Types of MDS Models |
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| 16A.11 Assessing Model Fit |
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| 16A.12 Recommended Readings |
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Chapter 16B: Multidimensional Scaling Using IBM SPSS
| 16B.1 The Structure of This Chapter |
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Chapter 17A: Cluster Analysis
| 17A.2 Two Types of Clustering |
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| 17A.3 Hierarchical Clustering |
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| 17A.5 Recommended Readings |
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Chapter 17B: Cluster Analysis Using IBM SPSS
| 17B.1 Hierarchical Cluster Analysis |
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| 17B.2 k-Means Cluster Analysis |
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PART V: COMPARING SCORES
Chapter 18A: Between Subjects Comparisons of Means
| 18A.3 A Brief Review of Some Basic Concepts |
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| 18A.4 Using Multiple Dependent Variables |
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| 18A.5 Evaluating Statistical Significance |
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| 18A.7 Designs, Effects, and Partitioning of the Variance |
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| 18A.8 Post-ANOVA Comparisons of Means |
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| 18A.9 Hierarchical Analysis of Effects |
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| 18A.10 Covariance Analysis |
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| 18A.11 Recommended Readings |
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Chapter 18B: Between Subjects ANCOVA, MANOVA, and MANCOVA Using IBM SPSS
| 18B.1 One-Way ANOVA Without the Covariate |
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| 18B.5 Two-Way MANOVA Without the Covariate |
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| 18B.6 Two-Way MANOVA Incorporating the Covariate (MANCOVA) |
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Chapter 19A: Discriminant Function Analysis
| 19A.2 The Formal Roles of the Variables in Discriminant Function Analysis and MANOVA |
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| 19A.3 Discriminant Function Analysis and Logistic Analysis Compared |
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| 19A.4 Sample Size for Discriminant Analysis |
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| 19A.5 The Discriminant Model |
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| 19A.6 Extracting Multiple Discriminant Functions |
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| 19A.7 Dynamics of Extracting Discriminant Functions |
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| 19A.8 Interpreting the Discriminant Function |
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| 19A.9 Assessing Statistical Significance and the Relative Strength of the Discriminative Functions |
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| 19A.10 Using Discriminant Function Analysis for Classification |
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| 19A.11 Different Discriminant Function Methods |
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| 19A.12 Recommended Readings |
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Chapter 19B: Three-Group Discriminant Function Analysis Using IBM SPSS
| 19B.4 Reporting the Results of a Three- Group Discriminant Function Analysis |
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Chapter 20A: Survival Analysis
| 20A.2 The Dependent Variable in Survival Analysis |
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| 20A.3 Ordinary Least Squares Regression Versus Survival Analysis |
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| 20A.4 Censored Observations |
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| 20A.5 Overview of Analysis Techniques for Survival Analysis in IBM SPSS |
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| 20A.6 Life Table Analysis |
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| 20A.7 Kaplan–Meier (Product-Limit) Survival Function Analysis |
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| 20A.8 Cox Proportional Hazard Regression Model |
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| 20A.9 Recommended Readings |
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Chapter 20B: Survival Analysis Using IBM SPSS
| 20B.3 Kaplan–Meier (Product-Limit) Survival Function Analysis |
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| 20B.4 Cox Proportional Hazard Regression Model |
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References
Appendix A: Statistics Tables
Author Index
Subject Index
