Description
Taylor and Francis Multiple Regression And Beyond An Introduction To Multiple Regression And Structural Equation Modeling 3Rd Edition 2019 Edition by Timothy Z. Keith
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 techniquesNew 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 PrefacePart I: Multiple RegressionChapter 1: Simple Bivariate RegressionChapter 2: Multiple Regression: IntroductionChapter 3: Multiple Regression: More DepthChapter 4: Three and More Independent Variables and Related IssuesChapter 5: Three Types of Multiple RegressionChapter 6: Analysis of Categorical VariablesChapter 7: Regression with Categorical and Continuous VariablesChapter 8: Testing for Interactions and Curves with Continuous VariablesChapter 9: Mediation, Moderation, and Common CauseChapter 10: Multiple Regression: Summary, Assumptions, Diagnostics, Power, and ProblemsChapter 11: Related Methods: Logistic Regression and Multilevel ModelingPart II: Beyond Multiple Regression: Structural Equation ModelingChapter 12: Path Modeling: Structural Equation Modeling with Measured VariablesChapter 13: Path Analysis: Assumptions and DangersChapter 14: Analyzing Path Models Using SEM ProgramsChapter 15: Error: The Scourge of ResearchChapter 16: Confirmatory Factor Analysis IChapter 17: Putting It All Together: Introduction to Latent Variable SEMChapter 18: Latent Variable Models II: Multigroup Models, Panel Models, Dangers & AssumptionsChapter 19: Latent Means In SEMChapter 20: Confirmatory Factor Analysis II: Invariance and Latent MeansChapter 21: Latent Growth ModelsChapter 22: Latent Variable Interactions and Multilevel Models In SEMChapter 23: Summary: Path Analysis, CFA, SEM, Mean Structures, and Latent Growth ModelsAppendicesAppendix A: Data Files. Appendix B: Review of Basic Statistics ConceptsAppendix C: Partial and Semipartial CorrelationAppendix D: Symbols Used in This BookAppendix E: Useful Formulae