You are here

Regression, ANOVA, and the General Linear Model
Share
Share

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

  • Peter Vik - Pacific University, Forest Grove, OR, USA


April 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

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

This book provides a very clearly written step-by-step approach of GLM, without using too many statistical formulations.

Dr Elisabeth Dorant
Fac: Health, Medicine & Life Sciences, Maastricht University
December 16, 2013

Alternative way at looking at statistics compared to other texts. Use to show student the links between statistical tests and manage hand calculations

Dr Robert Hogg
Dept of Sport & Exercise Science, University of Sunderland
October 30, 2013

An indispensable reference that redefines the position of the linear model and clarifies statistical approaches in research. The text is engaging and provides relevance as both an introductory tome and dip-in reference.

Mr Philip Bright
Research Department, European School of Osteopathy
October 21, 2013

An extremely good book that breaks down the subject in to understandable pieces

Mr Joel Harris
Sports Therapy, University of Hertfordshire
July 22, 2013

Excellent book for anybody performing research in sports science.

Ms Bettina Karsten
Life and Sports Science, Greenwich University
June 17, 2013
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.

This title is also available on SAGE Research Methods, the ultimate digital methods library. If your library doesn’t have access, ask your librarian to start a trial.