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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
April 2013 | 344 pages | SAGE Publications, Inc
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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.



 
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

This is an excellent and unique statistics text that bridges the gap between linear modelling approaches and the traditional test-focussed perspective. Worthy of comparison with Rich Zeller and Ed Carmines classic text, explaining the essential underpinning concepts rather than trying to teach statistical tests by rote.

Professor Brian Taylor
Social Work , University of Ulster
June 13, 2013
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Not what I had anticipated. Had hoped for something that also incorporated SPSS.

Dr Helen Scott
Psyc, Staffordshire University
June 7, 2013
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Easy to read even for the undegraduates with limited knowledge of statistics as provides a step-by-step approach to understanding ANOVA and regression techniques.

Miss Magdalena Marczak
Faculty of Health & Life Sciences, Coventry University
May 12, 2013
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A useful text for students completing a Masters programme and doing a research dissertation. Good level of detail included and step by step process in various statistical tests is easy to follow.

In my opinion slightly too detailed for undergraduates

Dr Pauline Meskell
School of Nursing and Midwifery, National University of Ireland, Galway
May 8, 2013
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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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