Generalizing the regression model : techniques for longitudinal and contextual analysis /: techniques for longitudinal and contextual analysis. (2020)

• Record Type: Book
• Further Information: Note: Blair Wheaton, Marisa Young.
• Authors: Wheaton, Blair; Young, Marisa
• 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;
• Edition: 1st
• Publisher Details: Los Angeles : SAGE
• Publication Date: 2020
• Extent: 1 online resource
• Subjects: 519.536; Regression analysis
• Languages: English
• ISBNs: 9781506342115
• Notes: Note: Includes bibliographical references.; Note: Description based on CIP data; resource not viewed.
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