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PrefaceAbout the AuthorsPART I: FUNDAMENTALS OF MULTIVARIATE DESIGNChapter 1: An Introduction to Multivariate Design
1.1 The Use of Multivariate Designs
1.2 The Definition of the Multivariate Domain
1.3 The Importance of Multivariate Designs
1.4 The General Form of a Variate
1.5 The Type of Variables Combined to Form a Variate
1.6 The General Organization of the BookChapter 2: Some Fundamental Research Design Concepts
2.1 Populations and Samples
2.2 Variables and Scales of Measurement
2.3 Independent Variables, Dependent Variables, and Covariates
2.4 Between Subjects and Within Subjects Independent Variables
2.5 Latent Variables and Measured Variables
2.6 Endogenous and Exogenous Variables
2.7 Statistical Significance
2.8 Statistical Power
2.9 Recommended ReadingsChapter 3A: Data Screening 3A.1 Overview 3A.2 Value Cleaning 3A.3 Patterns of Missing Values 3A.4 Overview of Methods of Handling Missing Data 3A.5 Deletion Methods of Handling Missing Data 3A.6 Single Imputation Methods of Handling Missing Data 3A.7 Modern Imputation Methods of Handling Missing Data 3A.8 Recommendations for Handling Missing Data 3A.9 Outliers 3A.10 Using Descriptive Statistics in Data Screening 3A.11 Using Pictorial Representations in Data Screening 3A.12 Multivariate Statistical Assumptions Underlying the General Linear Model 3A.13 Data Transformations 3A.14 Recommended ReadingsChapter 3B: Data Screening Using IBM SPSS 3B.1 The Look of IBM SPSS 3B.2 Data Cleaning: All Variables 3B.3 Screening Quantitative Variables 3B.4 Missing Values: Overview 3B.5 Missing Value Analysis 3B.6 Multiple Imputation 3B.7 Mean Substitution as a Single Imputation Approach 3B.8 Univariate Outliers 3B.9 Normality 3B.10 Linearity 3B.11 Multivariate Outliers 3B.12 Screening Within Levels of Categorical Variables 3B.13 Reporting the Data Screening ResultsPART II: BASIC AND ADVANCED REGRESSION ANALYSISChapter 4A: Bivariate Correlation and Simple Linear Regression 4A.1 The Concept of Correlation 4A.2 Different Types of Relationships 4A.3 Statistical Significance of the Correlation Coefficient 4A.4 Strength of Relationship 4A.5 Pearson Correlation Using a Quantitative Variable and a Dichotomous Nominal Variable 4A.6 Simple Linear Regression 4A.7 Statistical Error in Prediction: Why Bother With Regression? 4A.8 How Simple Linear Regression Is Used 4A.9 Factors Affecting the Computed Pearson r and Regression Coefficients 4A.10 Recommended ReadingsChapter 4B: Bivariate Correlation and Simple Linear Regression Using IBM SPSS 4B.1 Bivariate Correlation: Analysis Setup 4B.2 Simple Linear Regression 4B.3 Reporting Simple Linear Regression ResultsChapter 5A: Multiple Regression Analysis 5A.1 General Considerations 5A.2 Statistical Regression Methods 5A.3 The Two Classes of Variables in a Multiple Regression Analysis 5A.4 Multiple Regression Research 5A.5 The Regression Equations 5A.6 The Variate in Multiple Regression 5A.7 The Standard (Simultaneous) Regression Method 5A.8 Partial Correlation 5A.9 The Squared Multiple Correlation 5A.10 The Squared Semipartial Correlation 5A.11 Structure Coefficients 5A.12 Statistical Summary of the Regression Solution 5A.13 Evaluating the Overall Model 5A.14 Evaluating the Individual Predictor Results 5A.15 Step Methods of Building the Model 5A.16 The Forward Method 5A.17 The Backward Method 5A.18 Backward Versus Forward Solutions 5A.19 The Stepwise Method 5A.20 Evaluation of the Statistical Methods 5A.21 Collinearity and Multicollinearity 5A.22 Recommended ReadingsChapter 5B: Multiple Regression Analysis Using IBM SPSS 5B.1 Standard Multiple Regression 5B.2 Stepwise Multiple RegressionChapter 6A: Beyond Statistical Regression 6A.1 A Larger World of Regression 6A.2 Hierarchical Linear Regression 6A.3 Suppressor Variables 6A.4 Linear and Nonlinear Regression 6A.5 Dummy and Effect Coding 6A.6 Moderator Variables and Interactions 6A.7 Simple Mediation: A Minimal Path Analysis 6A.8 Recommended ReadingsChapter 6B: Beyond Statistical Regression Using IBM SPSS 6B.1 Hierarchical Linear Regression 6B.2 Polynomial Regression 6B.3 Dummy and Effect Coding 6B.4 Interaction Effects of Quantitative Variables in Regression 6B.5 MediationChapter 7A: Canonical Correlation Analysis 7A.1 Overview 7A.2 Canonical Functions or Roots 7A.3 The Index of Shared Variance 7A.4 The Dynamics of Extracting Canonical Functions 7A.5 Accounting for Variance: Eigenvalues and Theta Values 7A.6 The Multivariate Tests of Statistical Significance 7A.7 Specifying the Amount of Variance Explained in Canonical Correlation Analysis 7A.8 Coefficients Associated With the Canonical Functions 7A.9 Interpreting the Canonical Functions 7A.10 Recommended ReadingsChapter 7B: Canonical Correlation Analysis Using IBM SPSS 7B.1 Canonical Correlation: Analysis Setup 7B.2 Canonical Correlation: Overview of Output 7B.3 Canonical Correlation: Multivariate Tests of Significance 7B.4 Canonical Correlation: Eigenvalues and Canonical Correlations 7B.5 Canonical Correlation: Dimension Reduction Analysis 7B.6 Canonical Correlation: How Many Functions Should Be Interpreted? 7B.7 Canonical Correlation: The Coefficients in the Output 7B.8 Canonical Correlation: Interpreting the Dependent Variates 7B.9 Canonical Correlation: Interpreting the Predictor Variates 7B.10 Canonical Correlation: Interpreting the Canonical Functions 7B.11 Reporting of the Canonical Correlation Analysis ResultsChapter 8A: Multilevel Modeling 8A.1 The Name of the Procedure 8A.2 The Rise of Multilevel Modeling 8A.3 The Defining Feature of Multilevel Modeling: Hierarchically Structured Data 8A.4 Nesting and the Independence Assumption 8A.5 The Intraclass Correlation as an Index of Clustering 8A.6 Consequences of Violating the Independence Assumption 8A.7 Some Ways in Which Level 2 Groups Can Differ 8A.8 The Random Coefficient Regression Model 8A.9 Centering the Variables 8A.10 The Process of Building the Multilevel Model 8A.11 Recommended ReadingsChapter 8B: Multilevel Modeling Using IBM SPSS 8B.1 Numerical Example 8B.2 Assessing the Unconditional Model 8B.3 Centering the Covariates 8B.4 Building the Multilevel Models: Overview 8B.5 Building the First Model 8B.6 Building the Second Model 8B.7 Building the Third Model 8B.8 Building the Fourth Model 8B.9 Reporting the Multilevel Modeling ResultsChapter 9A: Binary and Multinomial Logistic Regression and ROC Analysis 9A.1 Overview 9A.2 The Variables in Logistic Regression Analysis 9A.3 Assumptions of Logistic Regression 9A.4 Coding of the Binary Variables in Logistic Regression 9A.5 The Shape of the Logistic Regression Function 9A.6 Probability, Odds, and Odds Ratios 9A.7 The Logistic Regression Model 9A.8 Interpreting Logistic Regression Results in Simpler Language 9A.9 Binary Logistic Regression With a Single Binary Predictor 9A.10 Binary Logistic Regression With a Single Quantitative Predictor 9A.11 Binary Logistic Regression With a Categorical and a Quantitative Predictor 9A.12 Evaluating the Logistic Model 9A.13 Strategies for Building the Logistic Regression Model 9A.14 ROC Analysis 9A.15 Recommended ReadingsChapter 9B: Binary and Multinomial Logistic Regression and ROC Analysis Using IBM SPSS 9B.1 Binary Logistic Regression 9B.2 ROC Analysis 9B.3 Multinomial Logistic RegressionPART III: STRUCTURAL RELATIONSHIPS OF MEASURED AND LATENT VARIABLESChapter 10A: Principal Components Analysis and Exploratory Factor Analysis 10A.1 Orientation and Terminology 10A.2 Origins of Factor Analysis 10A.3 How Factor Analysis Is Used in Psychological Research 10A.4 The General Organization of This
Chapter 10A.5 Where the Analysis Begins: The Correlation Matrix 10A.6 Acquiring Perspective on Factor Analysis 10A.7 Important Distinctions Within Our Generic Label of Factor Analysis 10A.8 The First Phase: Component Extraction 10A.9 Distances of Variables From a Component 10A.10 Principal Components Analysis Versus Factor Analysis 10A.11 Different Extraction Methods 10A.12 Recommendations Concerning Extraction 10A.13 The Rotation Process 10A.14 Orthogonal Factor Rotation Methods 10A.15 Oblique Factor Rotation 10A.16 Choosing Between Orthogonal and Oblique Rotation Strategies 10A.17 The Factor Analysis Output 10A.18 Interpreting Factors Based on the Rotated Matrices 10A.19 Selecting the Factor Solution 10A.20 Sample Size Issues 10A.21 Building Reliable Subscales 10A.22 Recommended ReadingsChapter 10B: Principal Components Analysis and Exploratory Factor Analysis Using IBM SPSS 10B.1 Numerical Example 10B.2 Preliminary Principal Components Analysis 10B.3 Principal Components Analysis With a Promax Rotation: Two-Component Solution 10B.4 ULS Analysis With a Promax Rotation: Two-Factor Solution 10B.5 Wrap-Up of the Two-Factor Solution 10B.6 Looking for Six Dimensions 10B.7 Principal Components Analysis With a Promax Rotation: Six-Component Solution 10B.8 ULS Analysis With a Promax Rotation: Six-Component Solution 10B.9 Principal Axis Factor Analysis With a Promax Rotation: Six-Component Solution 10B.10 Wrap-Up of the Six-Factor Solution 10B.11 Assessing Reliability: Our General Strategy 10B.12 Assessing Reliability: The Global Domains 10B.13 Assessing Reliability: The Six Item Sets Based on the ULS/Promax Structure 10B.14 Computing Scales Based on the ULS Promax Structure 10B.15 Using the Computed Variables in Further Analyses 10B.16 Reporting the Exploratory Factor Analysis ResultsChapter 11A: Confirmatory Factor Analysis 11A.1 Overview 11A.2 The General Form of a Confirmatory Model 11A.3 The Difference Between Latent and Measured Variables 11A.4 Contrasting Principal Components Analysis and Exploratory Factor Analysis With Confirmatory Factor Analysis 11A.5 Confirmatory Factor Analysis Is Theory Based 11A.6 The Logic of Performing a Confirmatory Factor Analysis 11A.7 Model Specification 11A.8 Model Identification 11A.9 Model Estimation 11A.10 Model Evaluation Overview 11A.11 Assessing Fit of Hypothesized Models 11A.12 Model Estimation: Assessing Pattern Coefficients 11A.13 Model Respecification 11A.14 Gen