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
This book is designed to provide a conceptually-oriented introduction to multiple regression.
Beyond Multiple Linear Regression: Applied Generalized Linear Models and Multilevel Models in R is designed for undergraduate students who have successfully completed a multiple linear regression course, helping them develop an expanded ...
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.
Berry and Feldman provide a systematic treatment of many of the major problems encountered in using regression analysis.
This essential book: Demonstrates the power of mixed modelling in a wide range ofdisciplines, including industrial research, social sciences,genetics, clinical research, ecology and agriculturalresearch.
Hillsdale, NJ: Lawrence Erlbaum. Cohen, J., Cohen, P., West, S., & Aiken, L. S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences. Mahwah, NJ: Lawrence Erlbaum. DeGroot, A. D., & Spiekerman, J. A. ...
Noted for its model-comparison approach and unified framework based on the general linear model (GLM), this classic text provides readers with a greater understanding of a variety of statistical procedures including analysis of variance ...
Neyman, J., & Pearson, E. S. (1928). On the use and interpretation of certain test criteria for purposes ... New York, NY: Pearson. Norusis, M. J. (2012). ... Olkin, E., & Pratt, J. W. (1958). Unbiased estimation of certain correlation ...
The material covered by this book consists of regression models that go beyond linear regression, including models for right-skewed, categorical and hierarchical observations.
This book considers regression models that are appropriate when the dependent variable is censored, truncated, binary, ordinal, nominal, or count. I refer to these variables as categorical and limited dependent variables (hereafter CLDVs).