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.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Hans-Peter Blossfeld, Prof., Dr. rer. pol. Dr. h. c., is 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 Gwendolin J. Blossfeld is a Postdoc at the Faculty of Social Sciences, Economics and Business Administration at the University of Bamberg.
Inhaltsangabe
Start vii Preface ix 1 Introduction 1 1.1 Causal Modeling and Observation Plans 5 1.1.1 Cross-Sectional Data 6 1.1.2 Panel Data 14 1.1.3 Event History Data 21 1.2 Event History Analysis and Causal Modeling 23 1.2.1 Causal Explanations 23 1.2.2 Transition Rate Models 37 2 Event History Data Structures 47 2.1 Basic Terminology 47 2.2 Event History Data Organization 51 3 Nonparametric Descriptive Methods 70 3.1 Life Table Method 70 3.2 Product-Limit Estimation 84 3.3 Comparing Survivor Functions 88 4 Exponential Transition Rate Models 103 4.1 The Basic Exponential Model 104 4.1.1 Maximum Likelihood Estimation 105 4.1.2 Models without Covariates 108 4.1.3 Time-Constant Covariates 111 4.2 Models with Multiple Destinations 119 4.3 Models with Multiple Episodes 129 5 Piecewise Constant Exponential Models 135 5.1 The Basic Model 135 5.2 Models without Covariates 137 5.3 Models with Proportional Covariate Effects 143 5.4 Models with Period-Specific Effects 144 6 Exponential Models with Time-Dependent Covariates 149 6.1 Parallel and Interdependent Processes 149 6.2 Interdependent Processes: The System Approach 152 6.3 Interdependent Processes: The Causal Approach 156 6.4 Episode Splitting with Qualitative Covariates 158 6.5 Episode Splitting with Quantitative Covariates 172 6.6 Application Examples . . .v 178 vi contents 7 Parametric Models of Time Dependence 208 7.1 Interpretation of Time Dependence 209 7.2 Gompertz Models 212 7.3 Weibull Models 222 7.4 Log-Logistic Models 230 7.5 Log-Normal Models 236 8 Methods for Testing Parametric Assumptions 242 8.1 Simple Graphical Methods 242 8.2 Pseudoresiduals 244 9 Semiparametric Transition Rate Models 250 9.1 Partial Likelihood Estimation 251 9.2 Time-Dependent Covariates 256 9.3 The Proportionality Assumption 261 9.4 Stratification with Covariates and for Multiepisode Data 266 9.5 Baseline Rates and Survivor Functions 271 9.6 Application Example 274 10 Problems of Model Specification 278 10.1 Unobserved Heterogeneity 278 10.2 Models with a Mixture Distribution 284 10.2.1 Models with a Gamma Mixture 287 10.2.2 Exponential Models with a Gamma Mixture 290 10.2.3 Weibull Models with a Gamma Mixture 292 10.2.4 Random Effects for Multiepisode Data 296 10.3 Discussion 300 11 Sequence Analysis 305 Brendan Halpin 11.1 What is Sequence Analysis? 305 11.2 Defining Distances 307 11.3 Doing Sequence Analysis in Stata 310 11.4 Unary Summaries 313 11.5 Intersequence Distance 315 11.6 What to Do with Sequence Distances? 317 11.7 Optimal Matching Distance 321 11.8 Special Topics 322 11.9 Conclusion 333 Appendix: Exercises 335 References 348 About the Authors 380
