Description
Data Analysis: A Model Comparison Approach to Regression, ANOVA, and Beyond is an integrated treatment of data analysis for the social and behavioral sciences. It covers all of the statistical models normally used in such analyses, such as multiple regression and analysis of variance, but it does so in an integrated manner that relies on the comparison of models of data estimated under the rubric of the general linear model.
Data Analysis also describes how the model comparison approach and uniform framework can be applied to models that include product predictors (i.e., interactions and nonlinear effects) and to observations that are nonindependent. Indeed, the analysis of nonindependent observations is treated in some detail, including models of nonindependent data with continuously varying predictors as well as standard repeated measures analysis of variance. This approach also provides an integrated introduction to multilevel or hierarchical linear models and logistic regression. Finally, Data Analysis provides guidance for the treatment of outliers and other problematic aspects of data analysis. It is intended for advanced undergraduate and graduate level courses in data analysis and offers an integrated approach that is very accessible and easy to teach.
Highlights of the third edition include:
- a new chapter on logistic regression;
- expanded treatment of mixed models for data with multiple random factors;
- updated examples;
an enhanced website with PowerPoint presentations and other tools that demonstrate the concepts in the book; exercises for each chapter that highlight research findings from the literature; data sets, R code, and SAS output for all analyses; additional examples and problem sets; and test questions.
Table of Contents
Preface 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 Values 5. Simple Regression: Estimating Models with a Single Continuous Predictor 6. Multiple Regression: Models with Multiple Continuous Predictors 7. Moderated and Nonlinear Regression Models 8. One-Way ANOVA: Models with a Single Categorical Predictor 9. Factorial ANOVA: Models with Multiple Categorical Predictors and Product Terms 10. ANCOVA: Models with Continuous and Categorical Predictors 11. Repeated-Measures ANOVA: Models with Nonindependent Errors 12. Incorporating Continuous Predictors with Nonindependent Data: Towards Mixed Models 13. Outliers and Ill-Mannered Error 14. Logistic Regression: Dependent Categorical Variables References Appendix Author Index Subject Index
