Timothy Z. Keith
Multiple Regression and Beyond
An Introduction to Multiple Regression and Structural Equation Modeling
Timothy Z. Keith
Multiple Regression and Beyond
An Introduction to Multiple Regression and Structural Equation Modeling
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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.
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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.
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Produktdetails
- Produktdetails
- Verlag: Routledge
- 3. Auflage
- Seitenzahl: 656
- Erscheinungstermin: 28. Januar 2019
- Englisch
- Abmessung: 260mm x 183mm x 39mm
- Gewicht: 1411g
- ISBN-13: 9781138061422
- ISBN-10: 1138061425
- Artikelnr.: 55156596
- Verlag: Routledge
- 3. Auflage
- Seitenzahl: 656
- Erscheinungstermin: 28. Januar 2019
- Englisch
- Abmessung: 260mm x 183mm x 39mm
- Gewicht: 1411g
- ISBN-13: 9781138061422
- ISBN-10: 1138061425
- Artikelnr.: 55156596
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.
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
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
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
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
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
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
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
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