Multiple Regression and Beyond
An Introduction to Multiple Regression and Structural Equation Modeling
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Multiple Regression and Beyond provides a conceptually oriented introduction to multiple regression (MR) analysis and structural equation modeling (SEM), along with related analyses. By emphasizing the concepts and purposes of MR rather than the derivation and calculation of formulas, this book presents the material in a clearer and more accessible way. This approach not only covers essential coursework but also makes it more approachable for students, increasing the likelihood that they will conduct research using MR or SEM effectively and wisely.This book covers both MR and SEM, explaining t...
Multiple Regression and Beyond provides a conceptually oriented introduction to multiple regression (MR) analysis and structural equation modeling (SEM), along with related analyses. By emphasizing the concepts and purposes of MR rather than the derivation and calculation of formulas, this book presents the material in a clearer and more accessible way. This approach not only covers essential coursework but also makes it more approachable for students, increasing the likelihood that they will conduct research using MR or SEM effectively and wisely.
This book covers both MR and SEM, explaining their relevance to each other. It also includes path analysis, confirmatory factor analysis, and latent growth modeling, incorporating real-world research examples throughout the chapters and end-of-chapter exercises. Figures and tables are used extensively to illustrate key concepts and techniques.
This new edition includes:
New sections on quantile regression, statistical suppression, contrast coding, and random intercept panel modelsSupport for the statistical program R and the R package lavaan in the text and on the website (www.tzkeith.com)New examples and exercisesUpdated instructor and student online resources (www.tzkeith.com)
This book covers both MR and SEM, explaining their relevance to each other. It also includes path analysis, confirmatory factor analysis, and latent growth modeling, incorporating real-world research examples throughout the chapters and end-of-chapter exercises. Figures and tables are used extensively to illustrate key concepts and techniques.
This new edition includes:
New sections on quantile regression, statistical suppression, contrast coding, and random intercept panel modelsSupport for the statistical program R and the R package lavaan in the text and on the website (www.tzkeith.com)New examples and exercisesUpdated instructor and student online resources (www.tzkeith.com)
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