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Informationen zum Autor 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. Klappentext 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. Zusammenfassung The author demonstrates basic statistical concepts from two different perspectives! giving the reader a conceptual understanding of how to interpret statistics and their use Inhaltsverzeichnis 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 ...
