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Event History Analysis With Stata, Second Edition

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eBook not available for this title

Authors:	Hans-Peter Blossfeld, Götz Rohwer, and Thorsten Schneider
Publisher:	CRC Press
Copyright:	2019
ISBN-13:	978-1-1380-7085-1
Pages:	352; paperback
Price:	$44.25

Authors:	Hans-Peter Blossfeld, Götz Rohwer, and Thorsten Schneider
Publisher:	CRC Press
Copyright:	2019
ISBN-13:
Pages:	352; eBook
Price:	$0.00

Authors:	Hans-Peter Blossfeld, Götz Rohwer, and Thorsten Schneider
Publisher:	CRC Press
Copyright:	2019
ISBN-13:
Pages:	352; Kindle
Price:	$

Comment from the Stata technical group

Event History Analysis with Stata, by Hans-Peter Blossfeld, Katrin Golsch, and Götz Rohwer, presents survival analysis from a social science perspective. Introducing the mathematics and statistics of survival analysis, along with substantive discussions of social science data issues, the authors give examples throughout using Stata (version 15) and data from the German Life History Study. The text covers both basic and advanced topics, from an introduction to life tables to fitting parametric models with unobserved heterogeneity. The authors aptly illustrate the entire research path required in applying event history analysis, from the initial problems of recording event-oriented data to data organization, software applications, and interpreting results.

After a substantive discussion of event history data, the authors explain, throughout the book, the theory and application of nonparametric methods, parametric regression, and finally the Cox model. The chapters on parametric analysis contain an instructive and detailed discussion on time-dependent covariates and practical situations where these might be present.

The book provides a link to the authors' site, where readers can find the dataset and the code used in the examples. New in this edition is an Appendix that will allow readers to consolidate the concepts just learned by applying them in practical exercises using data from the German National Educational Panel Study.

Event History Analysis with Stata is aimed at the professional social scientist but could also serve as a graduate-level text.

View table of contents >>

Preface

Acknowledgements

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

2.2.1 Using event history data files with Stata
2.2.2 Executing Stata with a do-file
2.2.3 Single episode data
2.2.4 Multiepisode data

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 decisions
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
7.6 Conclusion: Estimating time-dependent models with Stata

8 Methods to check parametric assumptions

8.1 Simple graphic 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 Introduction to sequence analysis by
Brendan Halpin

11.1 Defining distances
11.2 Doing sequence analysis in Stata
11.3 Unary summaries
11.4 Intersequence distance
11.5 What to do with sequence distances?
11.6 Optimal matching distance
11.7 Special topics
11.8 Conclusion

Appendix: exercises

References

About the Authors

Index