Data Analysis: A Model Comparison Approach to Regression, ANOVA, and Beyond [Hardcover]

Section A: Statistical Machinery? 1. Introduction to Data Analysis 2. Simple Models: Definitions of Error and Parameter Estimates 3. Simple Models: Models of Error and Sampling Distributions 4. Simple Models: Statistical Inferences about Parameter Estimates 5. Statistical Power: Power, Effect Sizes, and Confidence Intervals? Section B: Increasingly Complex Models? 6. Simple Regression: Models with a Single Continuous Predictor 7. Multiple Regression: Models with Multiple Continuous Predictors 8. Moderated and Nonlinear Multiple Regression models 9. One-Way ANOVA: Models with a Single Categorical Predictor 10. Factorial ANOVA: Models with Multiple Categorical Predictors and Product Terms 11. ANCOVA: Models with Continuous and Categorical Predictors? Section C: Violations of Assumptions About Error? 12. Repeated-Measures ANOVA: Models with Nonindependent Errors 13. Incorporating Continuous Predictors with Nonindependent Data: Towards Mixed Models 14. Outliers and Ill-Mannered Error 15. Logistic Regression: Dependent Categorical Variables

This essential textbook provides an integrated treatment of data analysis for the social and behavioral sciences. It covers all the key statistical models in an integrated manner that relies on the comparison of models of data estimated under the rubric of the general linear model.

Most introductory statistics texts teach students how to apply specific tests in specific circumstances, with little room for generalizing knowledge to new settings.?Data Analysis?instead teaches students how to think like scientists, always framing hypotheses as formal comparisons between competing explanations. The first three editions were ahead of their time in their philosophical approach to data analysis, and this new edition retains and expands their unifying framework.

Kristopher J. Preacher,?Vanderbilt University, USA