Reviewer Acknowledgements
Preface
About the Authors
Chapter 1: A Review of Correlation and Regression
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
1.8 A Multiple Regression Example: The Gender Pay Gap
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
Chapter 3: Generalizations of Regression 2: Nonlinear Regression
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
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
Chapter 5: Generalizations of Regression 4: The Generalized Linear Model
5.1 The Poisson Regression Model
5.2 The Complementary Log-Mog Model
Chapter 6: From Equations to Models: The Process of Explanation
6.1 What is Wrong With Equations?
6.2 Equations versus Models: Some Examples
6.4 Criteria For Causality
6.5 The analytical roles of Variables in causal models
6.6 Interpretating an association using controls and mediators
6.8 From Recursive to Non-Recursive Models: What to do about reciprocal Causation
Chapter 7: An Introduction to Structural Equation Models
7.2 Identifying the Factor analysis Model
Chapter 8: Identification and Testing of Models
8.2 Testing And Fitting Models
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
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
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
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
Chapter 13: Introduction to Regression for Panel Data
13.1 The Generalized Panel Regression Model
13.2 Examples of Panel Eegression
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
Chapter 15: Event History Analysis in Discrete Time
15.1 Overview of Concepts and Models
15.2 The Discrete-Time Event History Model
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
Chapter 16: The Continuous Time Event History Model
16.1 The Proportional Hazards Model
16.2 The Complementary Log-Log Model
References