Causal Analysis with Event History Data Using Stata

Hans-Peter Blossfeld (Bamberg University, Germany), Götz Rohwer, Gwendolin J. Blossfeld

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English

Routledge
28 July 2025

Research methods: general; Psychological methodology

This third edition of Causal Analysis with Event History Data Using Stata provides an updated introduction to event history modeling along with many instructive Stata examples. Using the latest Stata software, each of these practical examples develops a research question, points to useful contextual background information, gives a brief account of the underlying statistical concepts, describes the organization of input data and the application of Stata statistical procedures, and assists the reader in interpreting the content of the results obtained.

Emphasizing the strengths and limitations of continuous-time event history analysis in different fields of social science applications, this book demonstrates that event history models provide a useful approach to uncover causal relation- ships or to map a system of causal relationships. In particular, this book demonstrates how long-term processes can be studied, how different forms of duration dependencies can be estimated using nonparametric, parametric and semiparametric models, and how parallel and interdependent dynamic systems can be analyzed from a causal-analytical point of view using the method of episode splitting. The book also shows how changing contextual information at the micro, meso and macro levels can be easily integrated into a dynamic analysis of longitudinal data. Finally, the book addresses the problem of unobserved heterogeneity of time-constant and time-dependent omitted variables and makes suggestions for dealing with these sometimes difficult methodological problems.

Causal Analysis with Event History Data Using Stata is an invaluable resource for both novice and experienced researchers from a variety of fields (e.g. sociology, economics, political science, education, psychology, demography, epidemiology, management research and organizational studies, as well as medicine and clinical applications) who need an introductory textbook on continuous-time event history analysis and who are looking for a practical handbook for their longitudinal research.

By: Hans-Peter Blossfeld (Bamberg University Germany), Götz Rohwer, Gwendolin J. Blossfeld
Imprint: Routledge
Country of Publication: United Kingdom
Edition: 3rd edition
Dimensions: Height: 297mm, Width: 210mm,
Weight: 453g
ISBN: 9781032708096
ISBN 10: 1032708093
Pages: 240
Publication Date: 28 July 2025
Audience: College/higher education , Further / Higher Education
Format: Hardback
Publisher's Status: Forthcoming

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

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.