3rd Edition
Causal Analysis with Event History Data Using Stata
1 Introduction 1
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?
11.1.1 Sequence Data
11.1.2 The Value of a Holistic View
11.2 Defining Distances
11.2.1 Hamming Distance
11.2.2 Optimal Matching Distance
11.2.3 Other Distances
11.2.4 Determining State Distances
11.3 Doing Sequence Analysis in Stata .
11.3.1 Example Data
11.3.2 A First Look at the Data
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
11.8.1 Other Distance Measures
11.8.2 Ideal Types
11.8.3 Multichannel Sequence Analysis
11.8.4 Dyadic Analysis
11.9 Conclusion
Appendix: Exercises
References
About the Authors
Biography
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.
