Companion Website materials: https://tzkeith.com/ 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. By focusing on the concepts and purposes of MR and related methods, rather than the derivation and calculation of formulae, this book introduces material to students more clearly, and in a less threatening way. In addition to illuminating content necessary for coursework, the accessibility of this approach means students are more likely to be able to conduct research using MR or SEM--and more likely to use the methods wisely. This book: * Covers both MR and SEM, while explaining their relevance to one another * Includes path analysis, confirmatory factor analysis, and latent growth modeling * Makes extensive use of real-world research examples in the chapters and in the end-of-chapter exercises * Extensive use of figures and tables providing examples and illustrating key concepts and techniques New to this edition: * New chapter on mediation, moderation, and common cause * New chapter on the analysis of interactions with latent variables and multilevel SEM * Expanded coverage of advanced SEM techniques in chapters 18 through 22 * International case studies and examples * Updated instructor and student online resources

Companion Website materials: https://tzkeith.com

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. By focusing on the concepts and purposes of MR and related methods, rather than the derivation and calculation of formulae, this book introduces material to students more clearly, and in a less threatening way. In addition to illuminating content necessary for coursework, the accessibility of this approach means students are more likely to be able to conduct research using MR or SEM--and more likely to use the methods wisely.

This book:

• Covers both MR and SEM, while explaining their relevance to one another

• Includes path analysis, confirmatory factor analysis, and latent growth modeling

• Makes extensive use of real-world research examples in the chapters and in the end-of-chapter exercises

• Extensive use of figures and tables providing examples and illustrating key concepts and techniques

New to this edition:

• New chapter on mediation, moderation, and common cause

• New chapter on the analysis of interactions with latent variables and multilevel SEM

• Expanded coverage of advanced SEM techniques in chapters 18 through 22

• International case studies and examples

• Updated instructor and student online resources

Table of Contents

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