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Generalizing the Regression Model
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Generalizing the Regression Model
Techniques for Longitudinal and Contextual Analysis



December 2020 | 688 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. Generalizing the Regression Model provides students with a bridge from the classroom to actual research practice and application.

A website for the book at https://edge.sagepub.com/wheaton1e (coming soon!) includes resources for instructors and students.


 
Reviewer Acknowledgements
 
Preface
 
About the Authors
 
Chapter 1: A Review of Correlation and Regression
Introduction

 
1.1 Association in a Bivariate Table

 
1.2 Correlation as a Measure of Association

 
1.3 Bivariate Regression Theory

 
1.4 Partitioning of Variance in Bivariate Regression

 
1.5 Bivariate Regression Example

 
1.6 Assumptions of the Regression Model

 
1.7 Multiple Regression

 
1.8 A Multiple Regression Example: The Gender Pay Gap

 
1.9 Dummy Variables

 
Concluding Words

 
Practice Questions

 
 
Chapter 2: Generalizations of Regression 1: Testing and Interpreting Interactions
2.0.1 Limitations of the Additive Model

 
2.1 Interactions in Multiple Regression

 
2.2 A Three-Way Interaction Between Education, Race, and Gender

 
2.3 Interactions Involving Continuous Variables

 
2.4 Interactions Between Categorical Variables: The N-Way Analysis of Variance

 
2.5 Cautions In Studying Interactions

 
2.6 Published Examples

 
Concluding Words

 
Practice Questions

 
 
Chapter 3: Generalizations of Regression 2: Nonlinear Regression
Introduction

 
3.1 A simple example of a quadratic relationship

 
3.2 Estimating Higher-Order Relationships

 
3.3 Basic Math for nonlinear models

 
3.4 Interpretation of Nonlinear Functions

 
3.5 An Alternative Approach Using Dummy Variables

 
3.6 Spline Regression

 
3.7 Published Examples

 
Concluding Words

 
Practice Questions

 
 
Chapter 4: Generalizations of Regression 3: Logistic Regression
4.1 A First Take: The Linear Probability Model

 
4.2 The logistic Regression MODEL

 
4.3 Interpreting Logistic Models

 
4.4 Running a Logistic Regression in Statistical Software

 
4.5 Multinomial Logistic Regression

 
4.6 The Ordinal Logit Model

 
4.7 Estimation of Logistic Models

 
4.8 Tests for Logistic Regression

 
4.9 Published Examples

 
Concluding Words

 
Practice Questions

 
 
Chapter 5: Generalizations of Regression 4: The Generalized Linear Model
5.1 The Poisson Regression Model

 
5.2 The Complementary Log-Mog Model

 
5.3 Published Examples

 
Concluding Words

 
Practice Questions

 
 
Chapter 6: From Equations to Models: The Process of Explanation
6.1 What is Wrong With Equations?

 
6.2 Equations versus Models: Some Examples

 
6.3 Why Causality?

 
6.4 Criteria For Causality

 
6.5 The analytical roles of Variables in causal models

 
6.6 Interpretating an association using controls and mediators

 
6.7 Special Cases

 
6.8 From Recursive to Non-Recursive Models: What to do about reciprocal  Causation

 
6.9 Published Examples

 
Concluding Words

 
Practice Questions

 
 
Chapter 7: An Introduction to Structural Equation Models
7.1 Latent Variables

 
7.2 Identifying the Factor analysis Model

 
7.3 The Full Sem model

 
7.4 Published Examples

 
Concluding Words

 
Practice Question

 
 
Chapter 8: Identification and Testing of Models
8.1 Identification

 
8.2 Testing And Fitting Models

 
8.3 Published Examples

 
Concluding Words

 
Practice Questions

 
 
Chapter 9: Variations and Extensions of SEM
9.1 The Comparative SEM framework

 
9.2 A Multiple Group Example

 
9.3 SEM for Nonnormal and Ordinal Data

 
9.4 Nonlinear Effects in SEM Models

 
Concluding Words

 
 
Chapter 10: An Introduction to Hierarchical Linear Models
10.1 Introduction to the Model

 
10.2 A Formal Statement of a Two-Level HLM Model

 
10.3 Sub-Models of the Full HLM Model

 
10.4 The Three-Level Hierarchical Linear Model

 
10.5 Implications of Centering Level-1 Variables

 
10.6 Sample Size Consideations

 
10.7 Estimating Multilevel Models IN SAS and STATA

 
10.8 Estimating a Three-Level Model

 
10.9 Published Examples

 
Concluding Words

 
Practice Questions

 
 
Chapter 11: The Generalized Hierarchical Linear Model
11.1 Multilevel Logistic Regression

 
11.2 Running the Generalized HLM in SAS

 
11.3 Multilevel Poisson Regression

 
11.4 Published Example

 
Concluding Words

 
 
Chapter 12: Growth Curve Models
12.1 Deriving the Structure of Growth Models

 
12.2 Running Growth Models in SAS

 
12.3 Modeling The Trajectory of Net Worth From Early to Mid-Adulthood

 
12.4 Modeling the Trajectory of Internalizing Problems over Adolescence

 
12.5 Published Examples

 
Concluding Words

 
Practice Questions

 
 
Chapter 13: Introduction to Regression for Panel Data
13.1 The Generalized Panel Regression Model

 
13.2 Examples of Panel Eegression

 
13.3 Published Examples

 
Concluding Words

 
Practice Questions

 
 
Chapter 14: Variations and Extensions of Panel Regression
14.1 Models for the Effects of events between Waves

 
14.2 Dynamic Panel Models

 
14.3 Fixed Effect Methods For Logistic Regression

 
14.4 Fixed-Effects Methods For Structural Equation Models

 
14.5 Published Example

 
Concluding Words

 
 
Chapter 15: Event History Analysis in Discrete Time
15.1 Overview of Concepts and Models

 
15.2 The Discrete-Time Event History Model

 
15.3 Basic Concepts

 
15.4 Creating and Analyzing A Person-Period Data Set

 
15.5 Studying Women’s Entry into the Work Role After Having a First Child

 
15.6 The Competing Risks Model

 
15.7 Repeated Events: The Multiple

 
15.8 Published Example

 
Concluding Words

 
Practice Questions

 
 
Chapter 16: The Continuous Time Event History Model
16.1 The Proportional Hazards Model

 
16.2 The Complementary Log-Log Model

 
Concluding Words

 
 
References

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

This is an excellent textbook, but more appropriate for a different course design. We will consider it for FTEC 6319 in the future, but before then, we will see how "Data Analysis for Business, Economics, and Policy" works out.

Professor Robert Kieschnick
School Of Management, University Of Texas At Dallas
December 8, 2020

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