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

  • Peter Vik - Pacific University, Forest Grove, OR, USA, Idaho State University, Pocatello, USA


January 2013 | 344 pages | SAGE Publications, Inc
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.

 
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
 
Appendices
 
A: Research Designs
 
B: Variables, Distributions, & Statistical Assumptions
 
C: Sampling and Sample Sizes
 
D: Null Hypothesis, Statistical Decision-Making, & Statistical Power

Supplements

Student Study Site
A selection of diverse conceptual and computational practice problems is provided for each chapter of the book, minus the introduction. Students are encouraged to use them as a study aid and instructors are encouraged to use them as a homework and/or testing tool.

“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.”

Jeffrey A. Ciesla
Kent State University

“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.”

Alfred F. Mancuso
Georgian Court University

“The text includes simple descriptions of complex mathematical concepts that are the foundation of statistics in the social sciences.”

Lela Rankin Williams
Arizona State University

“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.”

Manfred van Dulmen
Kent State University

“…the author really takes the readers step by step and makes the material easy to follow even for readers without extensive mathematics backgrounds.”

Kamala London
University of Toledo

The book stands out with its clarity and structure which allows students to easily orientate themselves in elementary methods of empirical research. The genral approach enables student's to derive further methods and adopt knowledge to various kinds of statistical software.

Mr Joachim Müller
Faculty 2: Educational Sciences, University of Essen
February 16, 2016

A very useful book which focuses on some of the more commonly used tests for biological sciences

Dr Emma Coulthard
All Saints Campus, Manchester Metropolitan University
October 15, 2015

Vik covers clearly and simply the mathematical concepts underlying vast tools in statistical methods. The examples are very helpful and are well explained in that a student with no statistical background can grasp the concepts therein without much difficulty. I would recommend students to supplement this text with their primary text when they take courses in statistics.

Dr Lorenz Neuwirth
Psychology Dept, Cuny College Of Staten Island
February 15, 2015

Although I really like this book (and recommend it as a supplemental text for my students), I did not adopt it. The book has very little support material (e.g., PowerPoint slide, images, homework and test problems, sample data sets), which makes it very difficult for me to choose it over other texts that have this material -- note: this is especially true for the data sets, which are difficult to create. Also, book does not have much coverage for follow-up analyses for interactions in multiple regression or factorial ANOVA. Although this is a complex topics and somewhat beyond the scope of the book, it is one that I have to cover in my class.

Mr Keith Donohue
Psychology Dept, North Dakota State University
October 29, 2014

Clealry laid out text with good examples. Stress free reading !

Mrs CHRIS DEPLACIDO
SPEECH AND HEARING SCIENCES, Queen Margaret University College, Edinburgh
April 29, 2014
Key features

KEY FEATURES:

  • A demonstration of statistical analysis using both traditional and GLM approaches as offers a path to conceptual understanding of data analysis as well as a practical applied knowledge.
  • A single data set was used throughout the book to the extent possible, so as to demonstrate model-building by creating successively enhanced models based on the same data set.

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