Regression, ANOVA, and the General Linear Model: A Statistics Primer

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Peter Vik’s Regression, ANOVA, and the General Linear Model: A Statistics Primer demonstrates basic statistical concepts from two different perspectives, giving the reader a conceptual understanding of how to interpret statistics and their use. The two perspectives are (1) a traditional focus on the t-test, correlation, and ANOVA, and (2) a model-comparison approach using General Linear Models (GLM). This book juxtaposes the two approaches by presenting a traditional approach in one chapter, followed by the same analysis demonstrated using GLM. By so doing, students will acquire a theoretical and conceptual appreciation for data analysis as well as an applied practical understanding as to how these two approaches are alike.
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Editorial Reviews

Jeffrey A. Ciesla
“I believe that when students are taught about statistics using the approach of this text, they have a MUCH deeper understanding and appreciation of the material. It is really fantastic.”
Alfred F. Mancuso
“The author does a really nice job of explaining the General Linear Model (GLM) by comparing it to hypothesis testing and showing [some of] its real-world applicability.”
Lela Rankin Williams
“The text includes simple descriptions of complex mathematical concepts that are the foundation of statistics in the social sciences.”
Manfred van Dulmen
“I think the book provides a nice step-by-step approach to understanding ANOVA and regression techniques. The author does an excellent job breaking down the different components of these statistical techniques while capturing the attention of the reader.”
Kamala London
“…the author really takes the readers step by step and makes the material easy to follow even for readers without extensive mathematics backgrounds.”
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Product Details

  • ISBN-13: 9781412997355
  • Publisher: SAGE Publications
  • Publication date: 2/12/2013
  • Edition description: New Edition
  • Pages: 344
  • Product dimensions: 7.30 (w) x 8.90 (h) x 0.90 (d)

Meet the Author

Peter Vik has a B.S. in Human Development from the University of California at Davis, an M.A. in General Psychology from San Diego State University and a M.A. and Ph.D. in Clinical Psychology from University of Colorado, Boulder. He completed a clinical internship and postdoctoral fellowship with the Department of Psychiatry at the University of California at San Diego. Currently, Dr. Vik is Professor of Psychology and Director of the University Honors Program at Idaho State University. He has authored or co-authored numerous research publications and book chapters. He lives with his wife in Pocatello, and they are celebrating their first two grandchildren who were born just after this book was finished.
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Table of Contents

Chapter 1: Introduction
Part I: Foundations of the General Linear Model
Chapter 2: Predicting Scores: The Mean and the Error of Prediction
Chapter 3: Bivariate Regression
Chapter 4: Model Comparison: The Simplest Model Versus a Regression Model
Part II: Fundamental Statistical Tests
Chapter 5: Correlation: Traditional and Regression Approaches
Chapter 6: T-test: Concepts and Traditional Approach
Chapter 7: Oneway Analysis of Variance (ANOVA): Traditional Approach
Chapter 8: T-test, ANOVA, and the Bivariate Regression Approach
Part III: Adding Complexity
Chapter 9: Model Comparison II: Multiple Regression
Chapter 10: Multiple Regression: When Predictors Interact
Chapter 11: Two-way ANOVA: Traditional Approach
Chapter 12: Two-way ANOVA: Model Comparison Approach
Chapter 13: One-way ANOVA with Three Groups: Traditional Approach
Chapter 14: ANOVA with Three Groups: Model Comparison Approach
Chapter 15: Two by Three ANOVA: Complex Categorical Models
Chapter 16: Two by Three ANOVA: Model Comparison Approach
Chapter 17: Analysis of Covariance (ANCOVA): Continuous and Categorical Predictors
Chapter 18: Repeated Measures
Chapter 19: Multiple Repeated Measures
Chapter 20: Mixed Between and Within Designs
A: Research Designs
B: Variables, Distributions, & Statistical Assumptions
C: Sampling and Sample Sizes
D: Null Hypothesis, Statistical Decision-Making, & Statistical Power
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