Regression, ANOVA, and the General Linear Model

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.

• Chapter 1: IntroductionPart I: Foundations of the General Linear ModelChapter 2: Predicting Scores: The Mean and the Error of PredictionChapter 3: Bivariate RegressionChapter 4: Model Comparison: The Simplest Model Versus a Regression ModelPart II: Fundamental Statistical TestsChapter 5: Correlation: Traditional and Regression ApproachesChapter 6: T-test: Concepts and Traditional ApproachChapter 7: Oneway Analysis of Variance (ANOVA): Traditional ApproachChapter 8: T-test, ANOVA, and the Bivariate Regression ApproachPart III: Adding ComplexityChapter 9: Model Comparison II: Multiple RegressionChapter 10: Multiple Regression: When Predictors InteractChapter 11: Two-way ANOVA: Traditional ApproachChapter 12: Two-way ANOVA: Model Comparison ApproachChapter 13: One-way ANOVA with Three Groups: Traditional ApproachChapter 14: ANOVA with Three Groups: Model Comparison ApproachChapter 15: Two by Three ANOVA: Complex Categorical ModelsChapter 16: Two by Three ANOVA: Model Comparison ApproachChapter 17: Analysis of Covariance (ANCOVA): Continuous and Categorical PredictorsChapter 18: Repeated MeasuresChapter 19: Multiple Repeated MeasuresChapter 20: Mixed Between and Within DesignsAppendicesA: Research DesignsB: Variables, Distributions, & Statistical AssumptionsC: Sampling and Sample SizesD: Null Hypothesis, Statistical Decision-Making, & Statistical Power

“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.”