It is an invaluable resource for both novice and experienced researchers from sociology, economics, political science, education, psychology, demography, epidemiology, management research and organizational studies, as well as medicine and clinical applications) who need an introductory text- book on continuous-time event history analysis.
It is an invaluable resource for both novice and experienced researchers from sociology, economics, political science, education, psychology, demography, epidemiology, management research and organizational studies, as well as medicine and clinical applications) who need an introductory text- book on continuous-time event history analysis.
Hans-Peter Blossfeld, Prof., Dr. rer. pol. Dr. h. c., has been Emeritus of Excellence at the Graduate Centre Trimberg Research Academy (TRAc) at the University of Bamberg in Germany since April 2020. He held the Chair of Sociology I at the Faculty of Social Sciences, Economics and Business Administration at the University of Bamberg and was Professor of Sociology at the European University Institute in Florence, Italy. Götz Rohwer was Professor Emeritus of Methods of Social Research and Statistics at Ruhr-University Bochum in Germany. He passed away in March 2021. Gwendolin J. Blossfeld is a Postdoc at the Faculty of Social Sciences, Economics and Business Administration at the University of Bamberg in Germany.
Inhaltsangabe
Start v Preface 1 Introduction 1.1 Causal Modeling and Observation Plans 1.1.1 Cross-Sectional Data 1.1.2 Panel Data 1.1.3 Event History Data 1.2 Event History Analysis and Causal Modeling 1.2.1 Causal Explanations 1.2.2 Transition Rate Models 2 Event History Data Structures 2.1 Basic Terminology 2.2 Event History Data Organization 3 Nonparametric Descriptive Methods 3.1 Life Table Method 3.2 Product-Limit Estimation 3.3 Comparing Survivor Functions 4 Exponential Transition Rate Models 4.1 The Basic Exponential Model 4.1.1 Maximum Likelihood Estimation 4.1.2 Models without Covariates 4.1.3 Time-Constant Covariates 4.2 Models with Multiple Destinations 4.3 Models with Multiple Episodes 5 Piecewise Constant Exponential Models 5.1 The Basic Model 5.2 Models without Covariates 5.3 Models with Proportional Covariate Effects 5.4 Models with Period-Specific Effects 6 Exponential Models with Time-Dependent Covariates 6.1 Parallel and Interdependent Processes 6.2 Interdependent Processes: The System Approach 6.3 Interdependent Processes: The Causal Approach 6.4 Episode Splitting with Qualitative Covariates 6.5 Episode Splitting with Quantitative Covariates 6.6 Application Examples 7 Parametric Models of Time Dependence 7.1 Interpretation of Time Dependence 7.2 Gompertz Models 7.3 Weibull Models 7.4 Log-Logistic Models 7.5 Log-Normal Models 8 Methods for Testing Parametric Assumptions 8.1 Simple Graphical Methods 8.2 Pseudoresiduals 9 Semiparametric Transition Rate Models 9.1 Partial Likelihood Estimation 9.2 Time-Dependent Covariates 9.3 The Proportionality Assumption 9.4 Stratification with Covariates and for Multiepisode Data 9.5 Baseline Rates and Survivor Functions 9.6 Application Example 10 Problems of Model Specification 10.1 Unobserved Heterogeneity 10.2 Models with a Mixture Distribution 10.2.1 Models with a Gamma Mixture 10.2.2 Exponential Models with a Gamma Mixture 10.2.3 Weibull Models with a Gamma Mixture 10.2.4 Random Effects for Multiepisode Data 10.3 Discussion 11 Sequence Analysis Brendan Halpin 11.1 What is Sequence Analysis? 11.2 Defining Distances 11.3 Doing Sequence Analysis in Stata 11.4 Unary Summaries 11.5 Intersequence Distance 11.6 What to Do with Sequence Distances? 11.7 Optimal Matching Distance 11.8 Special Topics 11.9 Conclusion Appendix: Exercises References About the Authors
Start v Preface 1 Introduction 1.1 Causal Modeling and Observation Plans 1.1.1 Cross-Sectional Data 1.1.2 Panel Data 1.1.3 Event History Data 1.2 Event History Analysis and Causal Modeling 1.2.1 Causal Explanations 1.2.2 Transition Rate Models 2 Event History Data Structures 2.1 Basic Terminology 2.2 Event History Data Organization 3 Nonparametric Descriptive Methods 3.1 Life Table Method 3.2 Product-Limit Estimation 3.3 Comparing Survivor Functions 4 Exponential Transition Rate Models 4.1 The Basic Exponential Model 4.1.1 Maximum Likelihood Estimation 4.1.2 Models without Covariates 4.1.3 Time-Constant Covariates 4.2 Models with Multiple Destinations 4.3 Models with Multiple Episodes 5 Piecewise Constant Exponential Models 5.1 The Basic Model 5.2 Models without Covariates 5.3 Models with Proportional Covariate Effects 5.4 Models with Period-Specific Effects 6 Exponential Models with Time-Dependent Covariates 6.1 Parallel and Interdependent Processes 6.2 Interdependent Processes: The System Approach 6.3 Interdependent Processes: The Causal Approach 6.4 Episode Splitting with Qualitative Covariates 6.5 Episode Splitting with Quantitative Covariates 6.6 Application Examples 7 Parametric Models of Time Dependence 7.1 Interpretation of Time Dependence 7.2 Gompertz Models 7.3 Weibull Models 7.4 Log-Logistic Models 7.5 Log-Normal Models 8 Methods for Testing Parametric Assumptions 8.1 Simple Graphical Methods 8.2 Pseudoresiduals 9 Semiparametric Transition Rate Models 9.1 Partial Likelihood Estimation 9.2 Time-Dependent Covariates 9.3 The Proportionality Assumption 9.4 Stratification with Covariates and for Multiepisode Data 9.5 Baseline Rates and Survivor Functions 9.6 Application Example 10 Problems of Model Specification 10.1 Unobserved Heterogeneity 10.2 Models with a Mixture Distribution 10.2.1 Models with a Gamma Mixture 10.2.2 Exponential Models with a Gamma Mixture 10.2.3 Weibull Models with a Gamma Mixture 10.2.4 Random Effects for Multiepisode Data 10.3 Discussion 11 Sequence Analysis Brendan Halpin 11.1 What is Sequence Analysis? 11.2 Defining Distances 11.3 Doing Sequence Analysis in Stata 11.4 Unary Summaries 11.5 Intersequence Distance 11.6 What to Do with Sequence Distances? 11.7 Optimal Matching Distance 11.8 Special Topics 11.9 Conclusion Appendix: Exercises References About the Authors
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