You are here

Expedited access to textbooks and digital content

Instructors: Due to the COVID-19 pandemic and in support of your transition to online learning, requests for complimentary review copies of our textbooks will be fulfilled through our eBooks partner, VitalSource. By providing you with a digital review copy of the requested textbook(s), we can ensure you have expedited access to our content. If you require special assistance, please contact us at (800) 818-7243 ext. 6140 or at

Generalizing the Regression Model

Generalizing the Regression Model
Statistics for Longitudinal and Contextual Analysis

November 2020 | 592 pages | SAGE Publications, Inc

This comprehensive text introduces regression, the general linear model, structural equation modeling, the hierarchical linear model, growth curve models, panel data, and event history models, and includes discussion of published implementations of each technique showing how it was used to address substantive and interesting research questions. It takes a step-by-step approach in the presentation of each topic, using mathematical derivations where necessary, but primarily emphasizing how the methods involved can be implemented, are used in addressing representative substantive problems than span a number of disciplines, and can be interpreted in words. The book demonstrates the analyses in STATA and SAS.

Chapter 1: A Review of Correlation and Regression
Chapter 2: Generalizations of Regression 1: Testing and Interpreting Interactions
Chapter 3: Generalizations of Regression 2: Nonlinear Regression
Chapter 4: Generalizations of Regression 3: Logistic Regression
Chapter 5: Generalizations of Regression 4: The Generalized Linear Model
Chapter 6: From Equations to Models: The Process of Explanation
Chapter 7: An Introduction to Structural Equation Models
Chapter 8: Identification and Testing of Models
Chapter 9: Variations and Extensions of SEM
Chapter 10: An Introduction to Hierarchical Linear Models
Chapter 11: The Generalized Hierarchical Linear Model
Chapter 12: Growth Curve Models
Chapter 13: Introduction to Regression for Panel Data
Chapter 14: Variations and Extensions of Panel Regression
Chapter 15: Event History Analysis in Discrete Time
Chapter 16: The Continuous Time Event History Model

Quantitative analyses are so often relegated to OLS techniques when they should not be. The authors more than adequately demonstrate the why, what, and how other procedures (GMM, SEM, panel regression, event history analysis to name a few) are far superior to the OLS approaches widely but inappropriately found in published research or used in practice. Kudos to them.

Dane Joseph
George Fox University

Generalizing the Regression Model is a highly accessible textbook that covers a remarkable array of complex material with ease. Its applications and examples make the material intuitive and interesting for students to learn.

Jennifer Hayes Clark
University of Houston

Preview this book

For instructors

Coming soon!

Upon publication of this title, a complimentary digital review copy will be available through our eBooks partner, VitalSource.

To pre order your digital review copy now, please contact us at (800) 818-7243 ext. 6140 or at

Select a Purchasing Option

ISBN: 9781506342092