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 s...

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Hauptverfasser:	Wheaton, Blair (VerfasserIn), Young, Marisa (VerfasserIn)
Format:	Buch
Sprache:	English
Veröffentlicht:	Los Angeles SAGE [2021]
Zusammenfassung:	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
Beschreibung:	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
Beschreibung:	xxix, 658 Seiten Diagramme 1060 grams
ISBN:	9781506342092