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André Ernst

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Claudia O'Donovan-Bellante
Tel: +49 621 1246-221

Introduction to Event History Analysis

Mannheim, B6 4-5
General Topics:
Course Level:
Software used:
Students: 300 €
Academics: 450 €
Commercial: 900 €
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Lecturer(s): Prof. Dr. Jan Skopek

About the lecturer - Prof. Dr. Jan Skopek

Course description

The workshop will provide a comprehensive methodological and practical introduction to event history analysis. Special attention will be devoted to applications in life course research being concerned with time-dynamic modeling of social processes. After clarifying basic concepts like states, time, event, and censoring, descriptive approaches like life-tables and Kaplan-Meier estimation are discussed. Both continuous-time and discrete-time methods as well as parametric and semi-parametric regression models are introduced. Accounting for time-dependent covariates and time-varying effects are major features of survival models and will be discussed in detail. In addition, the workshop will cover a series of advanced topics like statistical inference with survival methods and survey data, multi-episode data, competing risk models, multilevel survival analysis, comparison of effects across models and groups, as well as effective visualization of model results. Substantial applications from sociological and demographic research will be used for illustration. Moreover, the software package Stata will be used throughout the workshop and exercises will help to deepen the acquired knowledge. Moreover, participants are encouraged to discuss their own work.

Target group

Researchers working in quantitative social research, particularly, life course research.

Learning objectives

Participants will have a sound overview to basic and recent approaches regarding the analysis of event history data. Having attended the workshop, participants will be able to organize and prepare event history data as well as to carry out own analyses with event history methods.


Participants should have basic knowledge in quantitative data analysis.
Particularly, participants should be familiar with linear and logistic regression analysis.


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