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Generalizing the Regression Model
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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

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Paperback
ISBN: 9781506342092
$100.00