Event History Modeling : A Guide for Social Scientists.

By: Box-Steffensmeier, Janet MContributor(s): Jones, Bradford S | Alvarez, R. MichaelMaterial type: TextTextSeries: Analytical Methods for Social ResearchPublisher: Cambridge : Cambridge University Press, 2004Copyright date: ©2004Description: 1 online resource (234 pages)Content type: text Media type: computer Carrier type: online resourceISBN: 9780511194054Subject(s): Event history analysis--Computer simulationGenre/Form: Electronic books.Additional physical formats: Print version:: Event History Modeling : A Guide for Social ScientistsDDC classification: 300.72 LOC classification: H61 .B6366 2004Online resources: Click to View
Contents:
Cover -- Half-title -- Title -- Copyright -- Dedication -- Contents -- Figures -- Tables -- Preface -- CHAPTER 1 Event History and Social Science -- The Substantive Motivation for Event History Analysis -- An Implicit Interest in "Survival" -- An Implicit Interest in Risk -- Event History Analysis Is Comparative Analysis -- Growing Body of Longitudinal Data -- Conclusion -- CHAPTER 2 The Logic of Event History Analysis -- Event History Data Structures -- Mathematical Components of Event History Analysis -- Problems with Modeling Duration Data -- Censoring and Truncation -- Accounting for Censoring -- Time-Varying Covariates -- Conclusion -- CHAPTER 3 Parametric Models for Single-Spell Duration Data -- The Exponential Model -- The Weibull Model -- Example 3.1: Weibull Model of U.N. Peacekeeping Missions -- The Log-Logistic and Log-Normal Models -- Example 3.2: Models of Candidacy Winnowing -- The Gompertz Model -- Estimation of Parametric Models -- Choosing among Parametric Distributions -- Example 3.3: Generalized Gamma Model of Cabinet Duration -- Assessing Model Fit -- Example 3.4: The AIC and Models of Cabinet Duration -- Conclusion -- CHAPTER 4 The Cox Proportional Hazards Model -- Problems with Parameterizing the Baseline Hazard -- The Cox Model -- Example 4.1: A Cox Model of U.N. Peacekeeping Missions -- Partial Likelihood -- The Breslow Method -- The Efron Method -- Averaged Likelihood -- The Exact Discrete Method -- Example 4.2: Cox Models of Cabinet Durations -- Interpretation of Cox Model Estimates -- Retrieving the Baseline Hazard and Survivor Functions -- Example 4.3: Baseline Functions and Cabinet Durations -- Conclusion -- CHAPTER 5 Models for Discrete Data -- Discrete-Time Data -- S(t), f(t), and h(t) for the Discrete-Time Model -- Models for Discrete-Time Processes -- Incorporating Duration in the Discrete-Time Framework.
Temporal Dummy Variables and Transformations -- Smoothing Functions -- Interpretation of Discrete-Time Model Estimates -- Example 5.1: Discrete-Time Models of U.S. House Member Careers -- Conditional Logit and the Cox Model -- Example 5.2: Militarized Interventions -- Conclusion -- CHAPTER 6 Issues in Model Selection -- Advantages and Disadvantages of Modeling Strategies -- Parametric Models Revisited -- Discrete-Time Models Revisited -- The Cox Model Revisited -- Flexible Parametric Models -- Example 6.1: Adoption of Restrictive Abortion Legislation -- Do All Roads Lead to the Cox Model? -- Conclusion -- CHAPTER 7 Inclusion of Time-Varying Covariates -- Incorporating Exogenous TVCs into the Duration Model -- Counting Processes and Duration Data with TVCs -- TVCs and the Cox Model -- Example 7.1: Challenger Deterrence in U.S. House Elections -- TVCs and Parametric Models -- Example 7.2: Use of TVCs in a Weibull Model -- TVCs and Discrete-Time Models -- Example 7.3: House Careers and TVCs -- Temporal Ordering of TVCs and Events -- Endogeneity of TVCs -- Temporal Dependence among Observations -- Example 7.4: Robust Variance Estimation -- Conclusion -- CHAPTER 8 Diagnostic Methods for the Event History Model -- Residuals in Event History Models -- Cox-Snell Residuals -- Schoenfeld Residuals -- Martingale Residuals -- Deviance Residuals -- Score Residuals -- Residual Analysis and the Cox Model -- Adequacy of the Cox Model -- Example 8.1: Application Using Cox-Snell Residuals -- Functional Form of a Covariate -- Example 8.2: Application Using Martingale Residuals -- Influential Observations -- Example 8.3: Influence Diagnostics Using Score Residuals -- Poorly Predicted Observations -- Example 8.4: Assessing Poorly Predicted Observations -- The Adequacy of the Proportional Hazards Assumption -- Example 8.5: Testing the PH Assumption.
Residual Analysis and Parametric Models -- Cox-Snell Residuals Applied to Parametric Models -- Example 8.6: Using Cox-Snell Residuals to Assess Parametric Forms -- Martingales, Deviance, and Score Residuals for Parametric Models -- Conclusion -- CHAPTER 9 Some Modeling Strategies for Unobserved Heterogeneity -- Heterogeneity -- Frailty Models -- Individual Frailty -- Example 9.1: Use of Frailty Model with Conflict Data -- Shared-Frailty Models -- Uses of Frailty Models -- The Split-Population Model -- Example 9.2: Split Population Model of PAC Contributions -- Conclusion -- CHAPTER 10 Models for Multiple Events -- Unordered Events of the Same Type -- Repeated Events Models -- Variance-Corrected Models for Repeated Events -- Example 10.1: A Repeated Events Model for Militarized Intervention Data -- Frailty Models and Repeated Events Data -- Competing Risks Models -- Latent Survivor Time Approach to Competing Risks -- Example 10.2: Competing Risks Model of Congressional Careers -- Multinomial Logit Approach to Competing Risks -- Example 10.3: MNL Competing Risks Model of Congressional Careers -- Stratified Cox Approach to Competing Risks -- Example 10.4: State Adoption of Restrictive Abortion Legislation Using a Stratified Cox Model -- Dependent Risks -- Conclusion -- CHAPTER 11 The Social Sciences and Event History -- Common Problems in the Analysis of Social Science Event History Data -- The "Patient Never Dies" -- Failure to Discriminate among Event Types -- Poor Measurement of Survival Times and TVCs -- The Meaning of Time Dependency -- What Should Social Scientists Do? -- Connecting Theory to Events -- Data Collection Efforts -- When does the "clock start ticking?" -- Which events are of interest? -- What is the process of interest? -- Are there different kinds of events that can occur? -- Are TVCs going to be used in subsequent analyses?.
Recommendations for Modeling Strategies -- Is duration dependency a "nuisance"? -- What issues emerge in the application of the Cox model? -- In what settings might one use parametric methods? -- What issues emerge in the application of parametric models? -- What about discrete-time data? -- What issues emerge in the application of "logit-type" models? -- What about complicated event structures? -- What about interpretation of event history results? -- Some Concluding Thoughts -- Conclusion -- Appendix Software for Event History Analysis -- References -- Index.
Summary: This 2004 book provides a guide to event history analysis for researchers and advanced students in the social sciences.
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Cover -- Half-title -- Title -- Copyright -- Dedication -- Contents -- Figures -- Tables -- Preface -- CHAPTER 1 Event History and Social Science -- The Substantive Motivation for Event History Analysis -- An Implicit Interest in "Survival" -- An Implicit Interest in Risk -- Event History Analysis Is Comparative Analysis -- Growing Body of Longitudinal Data -- Conclusion -- CHAPTER 2 The Logic of Event History Analysis -- Event History Data Structures -- Mathematical Components of Event History Analysis -- Problems with Modeling Duration Data -- Censoring and Truncation -- Accounting for Censoring -- Time-Varying Covariates -- Conclusion -- CHAPTER 3 Parametric Models for Single-Spell Duration Data -- The Exponential Model -- The Weibull Model -- Example 3.1: Weibull Model of U.N. Peacekeeping Missions -- The Log-Logistic and Log-Normal Models -- Example 3.2: Models of Candidacy Winnowing -- The Gompertz Model -- Estimation of Parametric Models -- Choosing among Parametric Distributions -- Example 3.3: Generalized Gamma Model of Cabinet Duration -- Assessing Model Fit -- Example 3.4: The AIC and Models of Cabinet Duration -- Conclusion -- CHAPTER 4 The Cox Proportional Hazards Model -- Problems with Parameterizing the Baseline Hazard -- The Cox Model -- Example 4.1: A Cox Model of U.N. Peacekeeping Missions -- Partial Likelihood -- The Breslow Method -- The Efron Method -- Averaged Likelihood -- The Exact Discrete Method -- Example 4.2: Cox Models of Cabinet Durations -- Interpretation of Cox Model Estimates -- Retrieving the Baseline Hazard and Survivor Functions -- Example 4.3: Baseline Functions and Cabinet Durations -- Conclusion -- CHAPTER 5 Models for Discrete Data -- Discrete-Time Data -- S(t), f(t), and h(t) for the Discrete-Time Model -- Models for Discrete-Time Processes -- Incorporating Duration in the Discrete-Time Framework.

Temporal Dummy Variables and Transformations -- Smoothing Functions -- Interpretation of Discrete-Time Model Estimates -- Example 5.1: Discrete-Time Models of U.S. House Member Careers -- Conditional Logit and the Cox Model -- Example 5.2: Militarized Interventions -- Conclusion -- CHAPTER 6 Issues in Model Selection -- Advantages and Disadvantages of Modeling Strategies -- Parametric Models Revisited -- Discrete-Time Models Revisited -- The Cox Model Revisited -- Flexible Parametric Models -- Example 6.1: Adoption of Restrictive Abortion Legislation -- Do All Roads Lead to the Cox Model? -- Conclusion -- CHAPTER 7 Inclusion of Time-Varying Covariates -- Incorporating Exogenous TVCs into the Duration Model -- Counting Processes and Duration Data with TVCs -- TVCs and the Cox Model -- Example 7.1: Challenger Deterrence in U.S. House Elections -- TVCs and Parametric Models -- Example 7.2: Use of TVCs in a Weibull Model -- TVCs and Discrete-Time Models -- Example 7.3: House Careers and TVCs -- Temporal Ordering of TVCs and Events -- Endogeneity of TVCs -- Temporal Dependence among Observations -- Example 7.4: Robust Variance Estimation -- Conclusion -- CHAPTER 8 Diagnostic Methods for the Event History Model -- Residuals in Event History Models -- Cox-Snell Residuals -- Schoenfeld Residuals -- Martingale Residuals -- Deviance Residuals -- Score Residuals -- Residual Analysis and the Cox Model -- Adequacy of the Cox Model -- Example 8.1: Application Using Cox-Snell Residuals -- Functional Form of a Covariate -- Example 8.2: Application Using Martingale Residuals -- Influential Observations -- Example 8.3: Influence Diagnostics Using Score Residuals -- Poorly Predicted Observations -- Example 8.4: Assessing Poorly Predicted Observations -- The Adequacy of the Proportional Hazards Assumption -- Example 8.5: Testing the PH Assumption.

Residual Analysis and Parametric Models -- Cox-Snell Residuals Applied to Parametric Models -- Example 8.6: Using Cox-Snell Residuals to Assess Parametric Forms -- Martingales, Deviance, and Score Residuals for Parametric Models -- Conclusion -- CHAPTER 9 Some Modeling Strategies for Unobserved Heterogeneity -- Heterogeneity -- Frailty Models -- Individual Frailty -- Example 9.1: Use of Frailty Model with Conflict Data -- Shared-Frailty Models -- Uses of Frailty Models -- The Split-Population Model -- Example 9.2: Split Population Model of PAC Contributions -- Conclusion -- CHAPTER 10 Models for Multiple Events -- Unordered Events of the Same Type -- Repeated Events Models -- Variance-Corrected Models for Repeated Events -- Example 10.1: A Repeated Events Model for Militarized Intervention Data -- Frailty Models and Repeated Events Data -- Competing Risks Models -- Latent Survivor Time Approach to Competing Risks -- Example 10.2: Competing Risks Model of Congressional Careers -- Multinomial Logit Approach to Competing Risks -- Example 10.3: MNL Competing Risks Model of Congressional Careers -- Stratified Cox Approach to Competing Risks -- Example 10.4: State Adoption of Restrictive Abortion Legislation Using a Stratified Cox Model -- Dependent Risks -- Conclusion -- CHAPTER 11 The Social Sciences and Event History -- Common Problems in the Analysis of Social Science Event History Data -- The "Patient Never Dies" -- Failure to Discriminate among Event Types -- Poor Measurement of Survival Times and TVCs -- The Meaning of Time Dependency -- What Should Social Scientists Do? -- Connecting Theory to Events -- Data Collection Efforts -- When does the "clock start ticking?" -- Which events are of interest? -- What is the process of interest? -- Are there different kinds of events that can occur? -- Are TVCs going to be used in subsequent analyses?.

Recommendations for Modeling Strategies -- Is duration dependency a "nuisance"? -- What issues emerge in the application of the Cox model? -- In what settings might one use parametric methods? -- What issues emerge in the application of parametric models? -- What about discrete-time data? -- What issues emerge in the application of "logit-type" models? -- What about complicated event structures? -- What about interpretation of event history results? -- Some Concluding Thoughts -- Conclusion -- Appendix Software for Event History Analysis -- References -- Index.

This 2004 book provides a guide to event history analysis for researchers and advanced students in the social sciences.

Description based on publisher supplied metadata and other sources.

Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2018. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.

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