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Multiple Regression and Beyond offers a conceptually oriented introduction to multiple regression (MR) analysis and structural equation modeling (SEM), along with analyses that flow naturally from those methods.
offers a conceptually oriented introduction to multiple regression (MR) analysis and structural equation modeling (SEM), along with analyses that flow naturally from those methods.
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Timothy Z. Keith is Professor of Educational Psychology at the University of Texas, Austin. His research is focused on the nature and measurement of intelligence, including the validity of tests of intelligence and the theories from which they are drawn. His research has been recognized with awards from the three major journals in school psychology, and he was awarded the senior scientist distinction by the School Psychology division of APA.
Inhaltsangabe
Preface Part I: Multiple Regression Chapter 1: Simple Bivariate Regression Chapter 2: Multiple Regression: Introduction Chapter 3: Multiple Regression: More Depth Chapter 4: Three and More Independent Variables and Related Issues Chapter 5: Three Types of Multiple Regression Chapter 6: Analysis of Categorical Variables Chapter 7: Regression with Categorical and Continuous Variables Chapter 8: Testing for Interactions and Curves with Continuous Variables Chapter 9: Mediation, Moderation, and Common Cause Chapter 10: Multiple Regression: Summary, Assumptions, Diagnostics, Power, and Problems Chapter 11: Related Methods: Logistic Regression and Multilevel Modeling Part II: Beyond Multiple Regression: Structural Equation Modeling Chapter 12: Path Modeling: Structural Equation Modeling with Measured Variables Chapter 13: Path Analysis: Assumptions and Dangers Chapter 14: Analyzing Path Models Using SEM Programs Chapter 15: Error: The Scourge of Research Chapter 16: Confirmatory Factor Analysis I Chapter 17: Putting It All Together: Introduction to Latent Variable SEM Chapter 18: Latent Variable Models II: Multigroup Models, Panel Models, Dangers & Assumptions Chapter 19: Latent Means In SEM Chapter 20: Confirmatory Factor Analysis II: Invariance and Latent Means Chapter 21: Latent Growth Models Chapter 22: Latent Variable Interactions and Multilevel Models In SEM Chapter 23: Summary: Path Analysis, CFA, SEM, Mean Structures, and Latent Growth Models Appendices Appendix A: Data Files. Appendix B: Review of Basic Statistics Concepts Appendix C: Partial and Semipartial Correlation Appendix D: Symbols Used in This Book Appendix E: Useful Formulae
Preface
Part I: Multiple Regression
Chapter 1: Simple Bivariate Regression
Chapter 2: Multiple Regression: Introduction
Chapter 3: Multiple Regression: More Depth
Chapter 4: Three and More Independent Variables and Related Issues
Chapter 5: Three Types of Multiple Regression
Chapter 6: Analysis of Categorical Variables
Chapter 7: Regression with Categorical and Continuous Variables
Chapter 8: Testing for Interactions and Curves with Continuous Variables
Chapter 9: Mediation, Moderation, and Common Cause
Chapter 10: Multiple Regression: Summary, Assumptions, Diagnostics, Power, and Problems
Chapter 11: Related Methods: Logistic Regression and Multilevel Modeling
Part II: Beyond Multiple Regression: Structural Equation Modeling
Chapter 12: Path Modeling: Structural Equation Modeling with Measured Variables
Chapter 13: Path Analysis: Assumptions and Dangers
Chapter 14: Analyzing Path Models Using SEM Programs
Chapter 15: Error: The Scourge of Research
Chapter 16: Confirmatory Factor Analysis I
Chapter 17: Putting It All Together: Introduction to Latent Variable SEM
Preface Part I: Multiple Regression Chapter 1: Simple Bivariate Regression Chapter 2: Multiple Regression: Introduction Chapter 3: Multiple Regression: More Depth Chapter 4: Three and More Independent Variables and Related Issues Chapter 5: Three Types of Multiple Regression Chapter 6: Analysis of Categorical Variables Chapter 7: Regression with Categorical and Continuous Variables Chapter 8: Testing for Interactions and Curves with Continuous Variables Chapter 9: Mediation, Moderation, and Common Cause Chapter 10: Multiple Regression: Summary, Assumptions, Diagnostics, Power, and Problems Chapter 11: Related Methods: Logistic Regression and Multilevel Modeling Part II: Beyond Multiple Regression: Structural Equation Modeling Chapter 12: Path Modeling: Structural Equation Modeling with Measured Variables Chapter 13: Path Analysis: Assumptions and Dangers Chapter 14: Analyzing Path Models Using SEM Programs Chapter 15: Error: The Scourge of Research Chapter 16: Confirmatory Factor Analysis I Chapter 17: Putting It All Together: Introduction to Latent Variable SEM Chapter 18: Latent Variable Models II: Multigroup Models, Panel Models, Dangers & Assumptions Chapter 19: Latent Means In SEM Chapter 20: Confirmatory Factor Analysis II: Invariance and Latent Means Chapter 21: Latent Growth Models Chapter 22: Latent Variable Interactions and Multilevel Models In SEM Chapter 23: Summary: Path Analysis, CFA, SEM, Mean Structures, and Latent Growth Models Appendices Appendix A: Data Files. Appendix B: Review of Basic Statistics Concepts Appendix C: Partial and Semipartial Correlation Appendix D: Symbols Used in This Book Appendix E: Useful Formulae
Preface
Part I: Multiple Regression
Chapter 1: Simple Bivariate Regression
Chapter 2: Multiple Regression: Introduction
Chapter 3: Multiple Regression: More Depth
Chapter 4: Three and More Independent Variables and Related Issues
Chapter 5: Three Types of Multiple Regression
Chapter 6: Analysis of Categorical Variables
Chapter 7: Regression with Categorical and Continuous Variables
Chapter 8: Testing for Interactions and Curves with Continuous Variables
Chapter 9: Mediation, Moderation, and Common Cause
Chapter 10: Multiple Regression: Summary, Assumptions, Diagnostics, Power, and Problems
Chapter 11: Related Methods: Logistic Regression and Multilevel Modeling
Part II: Beyond Multiple Regression: Structural Equation Modeling
Chapter 12: Path Modeling: Structural Equation Modeling with Measured Variables
Chapter 13: Path Analysis: Assumptions and Dangers
Chapter 14: Analyzing Path Models Using SEM Programs
Chapter 15: Error: The Scourge of Research
Chapter 16: Confirmatory Factor Analysis I
Chapter 17: Putting It All Together: Introduction to Latent Variable SEM
