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.

Table of Contents:
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