Description

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

A Comprehensive Guide to Data Analysis for the Social and Behavioral Sciences

This essential textbook offers an integrated approach to statistical data analysis, helping students and researchers understand how statistical models connect within a unified framework. Rather than treating methods as isolated techniques, it presents data analysis through the comparison of models estimated under the principles of the general linear model.

Beginning with the fundamental ideas behind unified model comparison, the book builds a strong foundation before moving into increasingly advanced applications. Readers learn how to analyze complex research questions involving multiple continuous and categorical predictors, interaction effects, and nonlinear relationships.

The text also addresses real-world challenges where traditional statistical assumptions are not met. It provides detailed coverage of analyzing data with violations of independence, equal variance, and normality assumptions, including methods for handling repeated measurements, correlated observations, and hierarchical data structures.

Key topics include:

• General linear models and their applications in social and behavioral research.
• Multiple predictors, categorical variables, interactions, and nonlinear effects.
• Repeated measures analysis of variance.
• Multilevel and hierarchical linear models.
• Logistic regression and other generalized linear models.
• Strategies for analyzing complex and non-independent data.

New Features in the Fourth Edition

• Expanded discussion of generalized linear models, with additional focus on logistic regression.
• New coverage of statistical power, research transparency, and ethical practices in response to concerns surrounding the replication crisis.
• Enhanced online resources, including instructor slides, test banks, additional exercises, solutions, new datasets, practice materials, and R programming support.

Clear, structured, and accessible, this textbook is designed for advanced undergraduate and graduate students studying data analysis, statistics, psychology, education, sociology, and related social science disciplines.

It provides the practical knowledge and statistical reasoning needed to confidently analyze data and interpret research findings in modern behavioral and social science research.