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.
The text describes the foundational logic of the unified model comparison framework. It then shows how this framework can be applied to increasingly complex models including multiple continuous and categorical predictors, as well as product predictors (i.e., interactions and nonlinear effects). The text also describes analyses of data that violate assumptions of independence, homogeneity, and normality. The analysis of nonindependent data is treated in some detail, covering standard repeated-measures analysis of variance and providing an integrated introduction to multilevel or hierarchical linear models and logistic regression.
Highlights of the fourth edition include:
-Expanded coverage of generalized linear models and logistic regression in particular
-A discussion of power and ethical statistical practice as it relates to the replication crisis
-An expanded collection of online resources such as PowerPoint slides and test bank for instructors, additional exercises and problem sets with answers, new data sets, practice questions, and R code
Clear and accessible, this text is intended for advanced undergraduate and graduate level courses in data analysis.
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
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