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.
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.
Timothy Z. Keith is Professor Emeritus at the University of Texas, Austin. Before retiring he was director of the graduate school psychology program (now school and clinical child psychology) in the Department of Educational Psychology. His research focused on the nature and measurement of intelligence, including the validity of tests of intelligence and the theories from which they are drawn. Matthew R. Reynolds is Professor of Educational Psychology at the University of Kansas. His research focuses on the measurement and structure of human cognitive abilities and on sex differences in cognitive abilities and academic achievement. Jacqueline M. Caemmerer is an Assistant Professor in the Department of Educational Psychology (school psychology graduate programs) at the University of Connecticut. Her research interests are in psychological assessment and validity issues. She is interested in better understanding what frequently used tests measure, their predictive validity, and developmental and cultural considerations of tests.
Inhaltsangabe
Preface Notes for the Fourth Edition Acknowledgments Part I: Multiple Regression Chapter 1: Simple bivariate regression Chapter 2: Multiple regression: Introduction Chapter 3: Multiple regression: More detail 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, common cause, and suppression Chapter 10: Multiple regression: Summary, assumptions, diagnostics, power, and problems Chapter 11: Related methods: Quantile regression, 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 Information Classification: General Chapter 18: Latent variable models II: Single indicators, correlated errors, 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 modeling in SEM Chapter 23: Summary: Path analysis, CFA, SEM, mean structures, and latent growth models Appendices Appendix A: Data files and statistical program notes Appendices B: Review of basic statistics concepts Appendix C: Partial and semipartial correlation Appendix D: Symbols used in this book Appendix E: Useful formulae Reference Author index Subject index
Preface Notes for the Fourth Edition Acknowledgments Part I: Multiple Regression Chapter 1: Simple bivariate regression Chapter 2: Multiple regression: Introduction Chapter 3: Multiple regression: More detail 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, common cause, and suppression Chapter 10: Multiple regression: Summary, assumptions, diagnostics, power, and problems Chapter 11: Related methods: Quantile regression, 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 Information Classification: General Chapter 18: Latent variable models II: Single indicators, correlated errors, 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 modeling in SEM Chapter 23: Summary: Path analysis, CFA, SEM, mean structures, and latent growth models Appendices Appendix A: Data files and statistical program notes Appendices B: Review of basic statistics concepts Appendix C: Partial and semipartial correlation Appendix D: Symbols used in this book Appendix E: Useful formulae Reference Author index Subject index
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