Applied Multivariate Research

Design and Interpretation
Third Edition
Lawrence S. Meyers - California State University, Sacramento, USA
Glenn C. Gamst - University of La Verne, USA
Anthony J. Guarino - Massachusetts General Hospital Institute of Health
Applied Multivariate Research
November 2016 | 1016 pages | SAGE US
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Description

Using a conceptual, non-mathematical approach, the updated Third Edition provides full coverage of the wide range of multivariate topics that graduate students across the social and behavioral sciences encounter. Authors Lawrence S. Meyers, Glenn Gamst, and A. J. Guarino integrate innovative multicultural topics in examples throughout the book, which include both conceptual and practical coverage of: statistical techniques of data screening; multiple regression; multilevel modeling; exploratory factor analysis; discriminant analysis; structural equation modeling; structural equation modeling invariance; survival analysis; multidimensional scaling; and cluster analysis. 

Contents

Preface

Preface

About the Authors

About the Authors

PART I: FUNDAMENTALS OF MULTIVARIATE DESIGN

  • Chapter 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 Book
  • Chapter 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 Readings
  • Chapter 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 Readings
  • Chapter 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 Results

PART II: BASIC AND ADVANCED REGRESSION ANALYSIS

  • Chapter 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 Readings
  • Chapter 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 Results
  • Chapter 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 Readings
  • Chapter 5B: Multiple Regression Analysis Using IBM SPSS
  • 5B.1 Standard Multiple Regression
  • 5B.2 Stepwise Multiple Regression
  • Chapter 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 Readings
  • Chapter 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 Mediation
  • Chapter 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 Readings
  • Chapter 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 Results
  • Chapter 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 Readings
  • Chapter 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 Results
  • Chapter 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 Readings
  • Chapter 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 Regression

PART III: STRUCTURAL RELATIONSHIPS OF MEASURED AND LATENT VARIABLES

  • Chapter 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 Readings
  • Chapter 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 Results
  • Chapter 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 General Considerations
  • 11A.15 Recommended Readings
  • Chapter 11B: Confirmatory Factor Analysis Using IBM SPSS Amos
  • 11B.1 Using IBM SPSS Amos
  • 11B.2 Numerical Example
  • 11B.3 Analysis Setup to Specify the Model
  • 11B.4 Model Identification
  • 11B.5 Structuring and Performing the Analysis
  • 11B.6 Working With the Analysis Output
  • 11B.7 Respecifying the Model
  • 11B.8 Output From the Respecified Model
  • 11B.9 Reporting Confirmatory Factor Analysis Results
  • Chapter 12A: Path Analysis: Multiple Regression Analysis
  • 12A.1 Overview
  • 12A.2 The Concept of a Path Model
  • 12A.3 The Appeal of Path Over Multiple Regression Analysis
  • 12A.4 Causality and Path Analysis
  • 12A.5 The Roles Played by Variables in a Path Structure
  • 12A.6 The Assumptions of Path Analysis
  • 12A.7 Missing Values in Path Analysis
  • 12A.8 The Multiple Regression Approach to Path Analysis
  • 12A.9 Indirect and Total Effects
  • 12A.10 Recommended Readings
  • Chapter 12B: Path Analysis: Multiple Regression Analysis Using IBM SPSS
  • 12B.1 The Data Set and Model Used in Our Example
  • 12B.2 Identifying the Variables in Each Analysis
  • 12B.3 Predicting Months_Teaching
  • 12B.4 Predicting Good_Teaching
  • 12B.5 Reporting the Path Analysis Results
  • Chapter 13A: Path Analysis: Structural Equation Modeling
  • 13A.1 Comparing Multiple Regression and Structural Equation Model Approaches
  • 13A.2 Differences Between the Equations Underlying Multiple Regression and Structural Equation Model Procedures
  • 13A.3 Configuring the Structural Model
  • 13A.4 Identifying the Structural Equation Model
  • 13A.5 Recommended Readings
  • Chapter 13B: Path Analysis: Structural Equation Modeling Using IBM SPSS Amos
  • 13B.1 Overview
  • 13B.2 The Data Set and Model Used in Our Example
  • 13B.3 Analysis Setup
  • 13B.4 The Analysis Output
  • 13B.5 Reporting the Path Analysis Results
  • Chapter 14A: Structural Equation Modeling
  • 14A.1 Overview of Structural Equation Modeling
  • 14A.2 Model Quality and the Structural Aspects of the Model
  • 14A.3 Latent Variables and Their Indicators
  • 14A.4 Identifying Structural Equation Models
  • 14A.5 Recommended Readings
  • Chapter 14B: Structural Equation Modeling Using IBM SPSS Amos
  • 14B.1 Overview
  • 14B.2 The Data Set and Model Used in Our Example
  • 14B.3 Model Configuration and Analysis Setup
  • 14B.4 Model Identification
  • 14B.5 Generating the Output
  • 14B.6 Analysis Output for the Model
  • 14B.7 Configuring and Evaluating the Respecified Model
  • 14B.8 Summary of the Results of the Model and Noting the Follow-up Analyses
  • 14B.9 Assessing the Indirect Effects in the Full Model
  • 14B.10 Assessing the Possibility of Having Obtained Complete Mediation in the Full Model
  • 14B.11 Assessing Mediation Through Self_ Regulation
  • 14B.12 Assessing Mediation Through Extrinsic_Goals
  • 14B.13 Synthesis of the Results
  • 14B.14 Reporting the SEM Results
  • Chapter 15A: Measurement and Structural Equation Modeling Invariance: Applying a Model to a Different Group
  • 15A.1 Overview
  • 15A.2 The General Strategy Used to Compare Groups
  • 15A.3 The Omnibus Model Comparison Phase
  • 15A.4 The Coefficient Comparison Phase
  • 15A.5 Recommended Readings
  • 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
  • 15B.2 The Data Set Used for Examining Invariance in Both the Confirmatory Factor Analysis and Structural Equation Model Examples
  • 15B.3 Confirmatory Factor Analysis Invariance: Global Preliminary Analysis
  • 15B.4 Confirmatory Factor Analysis Invariance: Group 1 (Rural) Analysis
  • 15B.5 Confirmatory Factor Analysis Invariance: Group 2 Analysis
  • 15B.6 Confirmatory Factor Analysis Invariance: Model Evaluation Setup
  • 15B.7 Confirmatory Factor Analysis Invariance: Model Evaluation Output
  • 15B.8 Reporting the Confirmatory Factor Analysis Invariance Results
  • 15B.9 Structural Equation Model Invariance: Global Preliminary Analysis
  • 15B.10 Structural Equation Model Invariance: Group 1 (Rural) Analysis
  • 15B.11 Structural Equation Model Invariance: Group 2 Analysis
  • 15B.12 Structural Equation Model Invariance: Model Evaluation Setup
  • 15B.13 Structural Equation Model Invariance: Model Evaluation Output
  • 15B.14 Reporting the Structural Equation Model Invariance Results

PART IV: CONSOLIDATING STIMULI AND CASES

  • Chapter 16A: Multidimensional Scaling
  • 16A.1 Overview
  • 16A.2 The Paired Comparison Method
  • 16A.3 Dissimilarity Data in MDS
  • 16A.4 Similarity/Dissimilarity Conceived as an Index of Distance
  • 16A.5 Dimensionality in MDS
  • 16A.6 Data Collection Methods
  • 16A.7 Similarity Versus Dissimilarity
  • 16A.8 Distance Models
  • 16A.9 A Classification Schema for MDS Techniques
  • 16A.10 Types of MDS Models
  • 16A.11 Assessing Model Fit
  • 16A.12 Recommended Readings
  • Chapter 16B: Multidimensional Scaling Using IBM SPSS
  • 16B.1 The Structure of This Chapter
  • 16B.2 Metric CMDS
  • 16B.3 Nonmetric CMDS
  • 16B.4 Metric WMDS
  • Chapter 17A: Cluster Analysis
  • 17A.1 Introduction
  • 17A.2 Two Types of Clustering
  • 17A.3 Hierarchical Clustering
  • 17A.4 k-Means Clustering
  • 17A.5 Recommended Readings
  • Chapter 17B: Cluster Analysis Using IBM SPSS
  • 17B.1 Hierarchical Cluster Analysis
  • 17B.2 k-Means Cluster Analysis

PART V: COMPARING SCORES

  • Chapter 18A: Between Subjects Comparisons of Means
  • 18A.1 Overview
  • 18A.2 Historical Context
  • 18A.3 A Brief Review of Some Basic Concepts
  • 18A.4 Using Multiple Dependent Variables
  • 18A.5 Evaluating Statistical Significance
  • 18A.6 Strength of Effect
  • 18A.7 Designs, Effects, and Partitioning of the Variance
  • 18A.8 Post-ANOVA Comparisons of Means
  • 18A.9 Hierarchical Analysis of Effects
  • 18A.10 Covariance Analysis
  • 18A.11 Recommended Readings
  • Chapter 18B: Between Subjects ANCOVA, MANOVA, and MANCOVA Using IBM SPSS
  • 18B.1 One-Way ANOVA Without the Covariate
  • 18B.2 One-Way ANCOVA
  • 18B.3 Three-Group MANOVA
  • 18B.4 Two-Group MANCOVA
  • 18B.5 Two-Way MANOVA Without the Covariate
  • 18B.6 Two-Way MANOVA Incorporating the Covariate (MANCOVA)
  • Chapter 19A: Discriminant Function Analysis
  • 19A.1 Overview
  • 19A.2 The Formal Roles of the Variables in Discriminant Function Analysis and MANOVA
  • 19A.3 Discriminant Function Analysis and Logistic Analysis Compared
  • 19A.4 Sample Size for Discriminant Analysis
  • 19A.5 The Discriminant Model
  • 19A.6 Extracting Multiple Discriminant Functions
  • 19A.7 Dynamics of Extracting Discriminant Functions
  • 19A.8 Interpreting the Discriminant Function
  • 19A.9 Assessing Statistical Significance and the Relative Strength of the Discriminative Functions
  • 19A.10 Using Discriminant Function Analysis for Classification
  • 19A.11 Different Discriminant Function Methods
  • 19A.12 Recommended Readings
  • Chapter 19B: Three-Group Discriminant Function Analysis Using IBM SPSS
  • 19B.1 Numerical Example
  • 19B.2 Analysis Setup
  • 19B.3 Analysis Output
  • 19B.4 Reporting the Results of a Three- Group Discriminant Function Analysis
  • Chapter 20A: Survival Analysis
  • 20A.1 Overview
  • 20A.2 The Dependent Variable in Survival Analysis
  • 20A.3 Ordinary Least Squares Regression Versus Survival Analysis
  • 20A.4 Censored Observations
  • 20A.5 Overview of Analysis Techniques for Survival Analysis in IBM SPSS
  • 20A.6 Life Table Analysis
  • 20A.7 Kaplan–Meier (Product-Limit) Survival Function Analysis
  • 20A.8 Cox Proportional Hazard Regression Model
  • 20A.9 Recommended Readings
  • Chapter 20B: Survival Analysis Using IBM SPSS
  • 20B.1 Numerical Example
  • 20B.3 Kaplan–Meier (Product-Limit) Survival Function Analysis
  • 20B.4 Cox Proportional Hazard Regression Model

References

References

Appendix A: Statistics Tables

Appendix A: Statistics Tables

Author Index

Author Index

Subject Index

Subject Index

Additional materials

Description

Using a conceptual, non-mathematical approach, the updated Third Edition provides full coverage of the wide range of multivariate topics that graduate students across the social and behavioral sciences encounter. Authors Lawrence S. Meyers, Glenn Gamst, and A. J. Guarino integrate innovative multicultural topics in examples throughout the book, which include both conceptual and practical coverage of: statistical techniques of data screening; multiple regression; multilevel modeling; exploratory factor analysis; discriminant analysis; structural equation modeling; structural equation modeling invariance; survival analysis; multidimensional scaling; and cluster analysis. 

Contents

Preface

Preface

About the Authors

About the Authors

PART I: FUNDAMENTALS OF MULTIVARIATE DESIGN

  • Chapter 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 Book
  • Chapter 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 Readings
  • Chapter 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 Readings
  • Chapter 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 Results

PART II: BASIC AND ADVANCED REGRESSION ANALYSIS

  • Chapter 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 Readings
  • Chapter 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 Results
  • Chapter 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 Readings
  • Chapter 5B: Multiple Regression Analysis Using IBM SPSS
  • 5B.1 Standard Multiple Regression
  • 5B.2 Stepwise Multiple Regression
  • Chapter 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 Readings
  • Chapter 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 Mediation
  • Chapter 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 Readings
  • Chapter 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 Results
  • Chapter 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 Readings
  • Chapter 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 Results
  • Chapter 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 Readings
  • Chapter 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 Regression

PART III: STRUCTURAL RELATIONSHIPS OF MEASURED AND LATENT VARIABLES

  • Chapter 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 Readings
  • Chapter 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 Results
  • Chapter 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 General Considerations
  • 11A.15 Recommended Readings
  • Chapter 11B: Confirmatory Factor Analysis Using IBM SPSS Amos
  • 11B.1 Using IBM SPSS Amos
  • 11B.2 Numerical Example
  • 11B.3 Analysis Setup to Specify the Model
  • 11B.4 Model Identification
  • 11B.5 Structuring and Performing the Analysis
  • 11B.6 Working With the Analysis Output
  • 11B.7 Respecifying the Model
  • 11B.8 Output From the Respecified Model
  • 11B.9 Reporting Confirmatory Factor Analysis Results
  • Chapter 12A: Path Analysis: Multiple Regression Analysis
  • 12A.1 Overview
  • 12A.2 The Concept of a Path Model
  • 12A.3 The Appeal of Path Over Multiple Regression Analysis
  • 12A.4 Causality and Path Analysis
  • 12A.5 The Roles Played by Variables in a Path Structure
  • 12A.6 The Assumptions of Path Analysis
  • 12A.7 Missing Values in Path Analysis
  • 12A.8 The Multiple Regression Approach to Path Analysis
  • 12A.9 Indirect and Total Effects
  • 12A.10 Recommended Readings
  • Chapter 12B: Path Analysis: Multiple Regression Analysis Using IBM SPSS
  • 12B.1 The Data Set and Model Used in Our Example
  • 12B.2 Identifying the Variables in Each Analysis
  • 12B.3 Predicting Months_Teaching
  • 12B.4 Predicting Good_Teaching
  • 12B.5 Reporting the Path Analysis Results
  • Chapter 13A: Path Analysis: Structural Equation Modeling
  • 13A.1 Comparing Multiple Regression and Structural Equation Model Approaches
  • 13A.2 Differences Between the Equations Underlying Multiple Regression and Structural Equation Model Procedures
  • 13A.3 Configuring the Structural Model
  • 13A.4 Identifying the Structural Equation Model
  • 13A.5 Recommended Readings
  • Chapter 13B: Path Analysis: Structural Equation Modeling Using IBM SPSS Amos
  • 13B.1 Overview
  • 13B.2 The Data Set and Model Used in Our Example
  • 13B.3 Analysis Setup
  • 13B.4 The Analysis Output
  • 13B.5 Reporting the Path Analysis Results
  • Chapter 14A: Structural Equation Modeling
  • 14A.1 Overview of Structural Equation Modeling
  • 14A.2 Model Quality and the Structural Aspects of the Model
  • 14A.3 Latent Variables and Their Indicators
  • 14A.4 Identifying Structural Equation Models
  • 14A.5 Recommended Readings
  • Chapter 14B: Structural Equation Modeling Using IBM SPSS Amos
  • 14B.1 Overview
  • 14B.2 The Data Set and Model Used in Our Example
  • 14B.3 Model Configuration and Analysis Setup
  • 14B.4 Model Identification
  • 14B.5 Generating the Output
  • 14B.6 Analysis Output for the Model
  • 14B.7 Configuring and Evaluating the Respecified Model
  • 14B.8 Summary of the Results of the Model and Noting the Follow-up Analyses
  • 14B.9 Assessing the Indirect Effects in the Full Model
  • 14B.10 Assessing the Possibility of Having Obtained Complete Mediation in the Full Model
  • 14B.11 Assessing Mediation Through Self_ Regulation
  • 14B.12 Assessing Mediation Through Extrinsic_Goals
  • 14B.13 Synthesis of the Results
  • 14B.14 Reporting the SEM Results
  • Chapter 15A: Measurement and Structural Equation Modeling Invariance: Applying a Model to a Different Group
  • 15A.1 Overview
  • 15A.2 The General Strategy Used to Compare Groups
  • 15A.3 The Omnibus Model Comparison Phase
  • 15A.4 The Coefficient Comparison Phase
  • 15A.5 Recommended Readings
  • 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
  • 15B.2 The Data Set Used for Examining Invariance in Both the Confirmatory Factor Analysis and Structural Equation Model Examples
  • 15B.3 Confirmatory Factor Analysis Invariance: Global Preliminary Analysis
  • 15B.4 Confirmatory Factor Analysis Invariance: Group 1 (Rural) Analysis
  • 15B.5 Confirmatory Factor Analysis Invariance: Group 2 Analysis
  • 15B.6 Confirmatory Factor Analysis Invariance: Model Evaluation Setup
  • 15B.7 Confirmatory Factor Analysis Invariance: Model Evaluation Output
  • 15B.8 Reporting the Confirmatory Factor Analysis Invariance Results
  • 15B.9 Structural Equation Model Invariance: Global Preliminary Analysis
  • 15B.10 Structural Equation Model Invariance: Group 1 (Rural) Analysis
  • 15B.11 Structural Equation Model Invariance: Group 2 Analysis
  • 15B.12 Structural Equation Model Invariance: Model Evaluation Setup
  • 15B.13 Structural Equation Model Invariance: Model Evaluation Output
  • 15B.14 Reporting the Structural Equation Model Invariance Results

PART IV: CONSOLIDATING STIMULI AND CASES

  • Chapter 16A: Multidimensional Scaling
  • 16A.1 Overview
  • 16A.2 The Paired Comparison Method
  • 16A.3 Dissimilarity Data in MDS
  • 16A.4 Similarity/Dissimilarity Conceived as an Index of Distance
  • 16A.5 Dimensionality in MDS
  • 16A.6 Data Collection Methods
  • 16A.7 Similarity Versus Dissimilarity
  • 16A.8 Distance Models
  • 16A.9 A Classification Schema for MDS Techniques
  • 16A.10 Types of MDS Models
  • 16A.11 Assessing Model Fit
  • 16A.12 Recommended Readings
  • Chapter 16B: Multidimensional Scaling Using IBM SPSS
  • 16B.1 The Structure of This Chapter
  • 16B.2 Metric CMDS
  • 16B.3 Nonmetric CMDS
  • 16B.4 Metric WMDS
  • Chapter 17A: Cluster Analysis
  • 17A.1 Introduction
  • 17A.2 Two Types of Clustering
  • 17A.3 Hierarchical Clustering
  • 17A.4 k-Means Clustering
  • 17A.5 Recommended Readings
  • Chapter 17B: Cluster Analysis Using IBM SPSS
  • 17B.1 Hierarchical Cluster Analysis
  • 17B.2 k-Means Cluster Analysis

PART V: COMPARING SCORES

  • Chapter 18A: Between Subjects Comparisons of Means
  • 18A.1 Overview
  • 18A.2 Historical Context
  • 18A.3 A Brief Review of Some Basic Concepts
  • 18A.4 Using Multiple Dependent Variables
  • 18A.5 Evaluating Statistical Significance
  • 18A.6 Strength of Effect
  • 18A.7 Designs, Effects, and Partitioning of the Variance
  • 18A.8 Post-ANOVA Comparisons of Means
  • 18A.9 Hierarchical Analysis of Effects
  • 18A.10 Covariance Analysis
  • 18A.11 Recommended Readings
  • Chapter 18B: Between Subjects ANCOVA, MANOVA, and MANCOVA Using IBM SPSS
  • 18B.1 One-Way ANOVA Without the Covariate
  • 18B.2 One-Way ANCOVA
  • 18B.3 Three-Group MANOVA
  • 18B.4 Two-Group MANCOVA
  • 18B.5 Two-Way MANOVA Without the Covariate
  • 18B.6 Two-Way MANOVA Incorporating the Covariate (MANCOVA)
  • Chapter 19A: Discriminant Function Analysis
  • 19A.1 Overview
  • 19A.2 The Formal Roles of the Variables in Discriminant Function Analysis and MANOVA
  • 19A.3 Discriminant Function Analysis and Logistic Analysis Compared
  • 19A.4 Sample Size for Discriminant Analysis
  • 19A.5 The Discriminant Model
  • 19A.6 Extracting Multiple Discriminant Functions
  • 19A.7 Dynamics of Extracting Discriminant Functions
  • 19A.8 Interpreting the Discriminant Function
  • 19A.9 Assessing Statistical Significance and the Relative Strength of the Discriminative Functions
  • 19A.10 Using Discriminant Function Analysis for Classification
  • 19A.11 Different Discriminant Function Methods
  • 19A.12 Recommended Readings
  • Chapter 19B: Three-Group Discriminant Function Analysis Using IBM SPSS
  • 19B.1 Numerical Example
  • 19B.2 Analysis Setup
  • 19B.3 Analysis Output
  • 19B.4 Reporting the Results of a Three- Group Discriminant Function Analysis
  • Chapter 20A: Survival Analysis
  • 20A.1 Overview
  • 20A.2 The Dependent Variable in Survival Analysis
  • 20A.3 Ordinary Least Squares Regression Versus Survival Analysis
  • 20A.4 Censored Observations
  • 20A.5 Overview of Analysis Techniques for Survival Analysis in IBM SPSS
  • 20A.6 Life Table Analysis
  • 20A.7 Kaplan–Meier (Product-Limit) Survival Function Analysis
  • 20A.8 Cox Proportional Hazard Regression Model
  • 20A.9 Recommended Readings
  • Chapter 20B: Survival Analysis Using IBM SPSS
  • 20B.1 Numerical Example
  • 20B.3 Kaplan–Meier (Product-Limit) Survival Function Analysis
  • 20B.4 Cox Proportional Hazard Regression Model

References

References

Appendix A: Statistics Tables

Appendix A: Statistics Tables

Author Index

Author Index

Subject Index

Subject Index

Additional materials

SAGE Publishing Logo

Applied Multivariate Research

Design and Interpretation

Lawrence S. Meyers , Glenn C. Gamst , Anthony J. Guarino
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November 2016 | 1016 pages | SAGE US

Format Published Date ISBN Price

Using a conceptual, non-mathematical approach, the updated Third Edition provides full coverage of the wide range of multivariate topics that graduate students across the social and behavioral sciences encounter. Authors Lawrence S. Meyers, Glenn Gamst, and A. J. Guarino integrate innovative multicultural topics in examples throughout the book, which include both conceptual and practical coverage of: statistical techniques of data screening; multiple regression; multilevel modeling; exploratory factor analysis; discriminant analysis; structural equation modeling; structural equation modeling invariance; survival analysis; multidimensional scaling; and cluster analysis. 


Table Of Contents:

  • Preface
  • About the Authors
  • PART I: FUNDAMENTALS OF MULTIVARIATE DESIGN
  • Chapter 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 Book
  • Chapter 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 Readings
  • Chapter 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 Readings
  • Chapter 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 Results
  • PART II: BASIC AND ADVANCED REGRESSION ANALYSIS
  • Chapter 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 Readings
  • Chapter 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 Results
  • Chapter 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 Readings
  • Chapter 5B: Multiple Regression Analysis Using IBM SPSS
  • 5B.1 Standard Multiple Regression
  • 5B.2 Stepwise Multiple Regression
  • Chapter 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 Readings
  • Chapter 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 Mediation
  • Chapter 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 Readings
  • Chapter 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 Results
  • Chapter 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 Readings
  • Chapter 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 Results
  • Chapter 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 Readings
  • Chapter 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 Regression
  • PART III: STRUCTURAL RELATIONSHIPS OF MEASURED AND LATENT VARIABLES
  • Chapter 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 Readings
  • Chapter 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 Results
  • Chapter 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 General Considerations
  • 11A.15 Recommended Readings
  • Chapter 11B: Confirmatory Factor Analysis Using IBM SPSS Amos
  • 11B.1 Using IBM SPSS Amos
  • 11B.2 Numerical Example
  • 11B.3 Analysis Setup to Specify the Model
  • 11B.4 Model Identification
  • 11B.5 Structuring and Performing the Analysis
  • 11B.6 Working With the Analysis Output
  • 11B.7 Respecifying the Model
  • 11B.8 Output From the Respecified Model
  • 11B.9 Reporting Confirmatory Factor Analysis Results
  • Chapter 12A: Path Analysis: Multiple Regression Analysis
  • 12A.1 Overview
  • 12A.2 The Concept of a Path Model
  • 12A.3 The Appeal of Path Over Multiple Regression Analysis
  • 12A.4 Causality and Path Analysis
  • 12A.5 The Roles Played by Variables in a Path Structure
  • 12A.6 The Assumptions of Path Analysis
  • 12A.7 Missing Values in Path Analysis
  • 12A.8 The Multiple Regression Approach to Path Analysis
  • 12A.9 Indirect and Total Effects
  • 12A.10 Recommended Readings
  • Chapter 12B: Path Analysis: Multiple Regression Analysis Using IBM SPSS
  • 12B.1 The Data Set and Model Used in Our Example
  • 12B.2 Identifying the Variables in Each Analysis
  • 12B.3 Predicting Months_Teaching
  • 12B.4 Predicting Good_Teaching
  • 12B.5 Reporting the Path Analysis Results
  • Chapter 13A: Path Analysis: Structural Equation Modeling
  • 13A.1 Comparing Multiple Regression and Structural Equation Model Approaches
  • 13A.2 Differences Between the Equations Underlying Multiple Regression and Structural Equation Model Procedures
  • 13A.3 Configuring the Structural Model
  • 13A.4 Identifying the Structural Equation Model
  • 13A.5 Recommended Readings
  • Chapter 13B: Path Analysis: Structural Equation Modeling Using IBM SPSS Amos
  • 13B.1 Overview
  • 13B.2 The Data Set and Model Used in Our Example
  • 13B.3 Analysis Setup
  • 13B.4 The Analysis Output
  • 13B.5 Reporting the Path Analysis Results
  • Chapter 14A: Structural Equation Modeling
  • 14A.1 Overview of Structural Equation Modeling
  • 14A.2 Model Quality and the Structural Aspects of the Model
  • 14A.3 Latent Variables and Their Indicators
  • 14A.4 Identifying Structural Equation Models
  • 14A.5 Recommended Readings
  • Chapter 14B: Structural Equation Modeling Using IBM SPSS Amos
  • 14B.1 Overview
  • 14B.2 The Data Set and Model Used in Our Example
  • 14B.3 Model Configuration and Analysis Setup
  • 14B.4 Model Identification
  • 14B.5 Generating the Output
  • 14B.6 Analysis Output for the Model
  • 14B.7 Configuring and Evaluating the Respecified Model
  • 14B.8 Summary of the Results of the Model and Noting the Follow-up Analyses
  • 14B.9 Assessing the Indirect Effects in the Full Model
  • 14B.10 Assessing the Possibility of Having Obtained Complete Mediation in the Full Model
  • 14B.11 Assessing Mediation Through Self_ Regulation
  • 14B.12 Assessing Mediation Through Extrinsic_Goals
  • 14B.13 Synthesis of the Results
  • 14B.14 Reporting the SEM Results
  • Chapter 15A: Measurement and Structural Equation Modeling Invariance: Applying a Model to a Different Group
  • 15A.1 Overview
  • 15A.2 The General Strategy Used to Compare Groups
  • 15A.3 The Omnibus Model Comparison Phase
  • 15A.4 The Coefficient Comparison Phase
  • 15A.5 Recommended Readings
  • 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
  • 15B.2 The Data Set Used for Examining Invariance in Both the Confirmatory Factor Analysis and Structural Equation Model Examples
  • 15B.3 Confirmatory Factor Analysis Invariance: Global Preliminary Analysis
  • 15B.4 Confirmatory Factor Analysis Invariance: Group 1 (Rural) Analysis
  • 15B.5 Confirmatory Factor Analysis Invariance: Group 2 Analysis
  • 15B.6 Confirmatory Factor Analysis Invariance: Model Evaluation Setup
  • 15B.7 Confirmatory Factor Analysis Invariance: Model Evaluation Output
  • 15B.8 Reporting the Confirmatory Factor Analysis Invariance Results
  • 15B.9 Structural Equation Model Invariance: Global Preliminary Analysis
  • 15B.10 Structural Equation Model Invariance: Group 1 (Rural) Analysis
  • 15B.11 Structural Equation Model Invariance: Group 2 Analysis
  • 15B.12 Structural Equation Model Invariance: Model Evaluation Setup
  • 15B.13 Structural Equation Model Invariance: Model Evaluation Output
  • 15B.14 Reporting the Structural Equation Model Invariance Results
  • PART IV: CONSOLIDATING STIMULI AND CASES
  • Chapter 16A: Multidimensional Scaling
  • 16A.1 Overview
  • 16A.2 The Paired Comparison Method
  • 16A.3 Dissimilarity Data in MDS
  • 16A.4 Similarity/Dissimilarity Conceived as an Index of Distance
  • 16A.5 Dimensionality in MDS
  • 16A.6 Data Collection Methods
  • 16A.7 Similarity Versus Dissimilarity
  • 16A.8 Distance Models
  • 16A.9 A Classification Schema for MDS Techniques
  • 16A.10 Types of MDS Models
  • 16A.11 Assessing Model Fit
  • 16A.12 Recommended Readings
  • Chapter 16B: Multidimensional Scaling Using IBM SPSS
  • 16B.1 The Structure of This Chapter
  • 16B.2 Metric CMDS
  • 16B.3 Nonmetric CMDS
  • 16B.4 Metric WMDS
  • Chapter 17A: Cluster Analysis
  • 17A.1 Introduction
  • 17A.2 Two Types of Clustering
  • 17A.3 Hierarchical Clustering
  • 17A.4 k-Means Clustering
  • 17A.5 Recommended Readings
  • Chapter 17B: Cluster Analysis Using IBM SPSS
  • 17B.1 Hierarchical Cluster Analysis
  • 17B.2 k-Means Cluster Analysis
  • PART V: COMPARING SCORES
  • Chapter 18A: Between Subjects Comparisons of Means
  • 18A.1 Overview
  • 18A.2 Historical Context
  • 18A.3 A Brief Review of Some Basic Concepts
  • 18A.4 Using Multiple Dependent Variables
  • 18A.5 Evaluating Statistical Significance
  • 18A.6 Strength of Effect
  • 18A.7 Designs, Effects, and Partitioning of the Variance
  • 18A.8 Post-ANOVA Comparisons of Means
  • 18A.9 Hierarchical Analysis of Effects
  • 18A.10 Covariance Analysis
  • 18A.11 Recommended Readings
  • Chapter 18B: Between Subjects ANCOVA, MANOVA, and MANCOVA Using IBM SPSS
  • 18B.1 One-Way ANOVA Without the Covariate
  • 18B.2 One-Way ANCOVA
  • 18B.3 Three-Group MANOVA
  • 18B.4 Two-Group MANCOVA
  • 18B.5 Two-Way MANOVA Without the Covariate
  • 18B.6 Two-Way MANOVA Incorporating the Covariate (MANCOVA)
  • Chapter 19A: Discriminant Function Analysis
  • 19A.1 Overview
  • 19A.2 The Formal Roles of the Variables in Discriminant Function Analysis and MANOVA
  • 19A.3 Discriminant Function Analysis and Logistic Analysis Compared
  • 19A.4 Sample Size for Discriminant Analysis
  • 19A.5 The Discriminant Model
  • 19A.6 Extracting Multiple Discriminant Functions
  • 19A.7 Dynamics of Extracting Discriminant Functions
  • 19A.8 Interpreting the Discriminant Function
  • 19A.9 Assessing Statistical Significance and the Relative Strength of the Discriminative Functions
  • 19A.10 Using Discriminant Function Analysis for Classification
  • 19A.11 Different Discriminant Function Methods
  • 19A.12 Recommended Readings
  • Chapter 19B: Three-Group Discriminant Function Analysis Using IBM SPSS
  • 19B.1 Numerical Example
  • 19B.2 Analysis Setup
  • 19B.3 Analysis Output
  • 19B.4 Reporting the Results of a Three- Group Discriminant Function Analysis
  • Chapter 20A: Survival Analysis
  • 20A.1 Overview
  • 20A.2 The Dependent Variable in Survival Analysis
  • 20A.3 Ordinary Least Squares Regression Versus Survival Analysis
  • 20A.4 Censored Observations
  • 20A.5 Overview of Analysis Techniques for Survival Analysis in IBM SPSS
  • 20A.6 Life Table Analysis
  • 20A.7 Kaplan–Meier (Product-Limit) Survival Function Analysis
  • 20A.8 Cox Proportional Hazard Regression Model
  • 20A.9 Recommended Readings
  • Chapter 20B: Survival Analysis Using IBM SPSS
  • 20B.1 Numerical Example
  • 20B.3 Kaplan–Meier (Product-Limit) Survival Function Analysis
  • 20B.4 Cox Proportional Hazard Regression Model
  • References
  • Appendix A: Statistics Tables
  • Author Index
  • Subject Index

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