Universität Wien FIND

230172 UE Introduction to Event History Analysis (2021W)

4.00 ECTS (2.00 SWS), SPL 23 - Soziologie
Continuous assessment of course work


Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).


max. 35 participants
Language: English


Classes (iCal) - next class is marked with N

Saturday 09.10. 09:30 - 10:30 Digital
Saturday 11.12. 09:30 - 16:15 Digital
Saturday 18.12. 09:30 - 16:15 Digital
Saturday 08.01. 09:30 - 16:15 Digital
Saturday 15.01. 09:30 - 16:15 Digital


Aims, contents and method of the course

This course offers an introduction to event history analysis (or survival analysis), including theoretical sessions and some applications. Event history analysis is a technique to analyze the occurrence and timing of events, such as births, divorce, employment, mortality, etc. The aim of the researcher when applying event history analysis is to examine how the rate of a particular event varies over time, and with other covariates of interest. The sessions will deal with the following topics:

1) Type of research questions that can be answered applying event history analysis, and the data need to apply event history analysis
2) Fundamentals of event history, descriptive methods (life tables, Kaplan Meier estimator)
3) Cox regression models
4) Parametric survival models

The learning objectives are: (1) to familiarize with time-to-event data and analyses (2) to be able to understand the scientific literature using event history analysis; (3) to be able to perform analyses of time-to-event data and correctly interpret the results.

Prerequisites for the course are: knowledge of descriptive and inferential statistics, knowledge of probability theory and multivariate data analyses.

For the applications, we will use R software.

The course will be held in digital format (platform Zoom and Moodle). The language of the course is English.

Assessment and permitted materials

The assessment of the course consists of three individual tasks (oral presentation, and two written assignments) based on the topics of the course and the applications performed by the students. For each task the student has to show the quantitative skills acquired (e.g., proper use of statistical concepts, understanding of scientific literature using event history analysis, analyze data correctly and interpret the results accordingly).

For the final grade: all tasks weight 30%, active participation and attendance 10%

Important Grading Information:
If not explicitly noted otherwise, all requirements mentioned in the grading scheme must be met.
If a required task is not fulfilled, this will be considered as a discontinuation of the course. In that case, the course will be graded as ‘fail’ (5), unless there is a major and unpredictable reason for not being able to fulfill the task on the student's side (e.g. a longer illness).
In such a case, the student may be de-registered from the course without grading.
Whether this exception applies is decided by the lecturer.
If any requirement of the course has been fulfilled by fraudulent means, be it for example by cheating at an exam, plagiarizing parts of a written assignment or by faking signatures on an attendance sheet, the student's participation in the course will be discontinued, the entire course will be graded as ‘not assessed’ and will be entered into the electronic exam record as ‘fraudulently obtained’.
The plagiarism-detection service (Turnitin in Moodle) can be used in course of the grading: Details will be announced by the lecturer.

Minimum requirements and assessment criteria

Each task is graded with a scale from 1 (excellent) to 5 (fail). Students have to respect the deadlines agreed for each task. If the student does not perform in one of the tasks according to the deadline, the course is considered failed. To have an overall positive evaluation, students need a grade of 4 or less for each task. Attendance at the sessions is expected, if a student misses more than one session, the course is considered failed.

Examination topics

Reading list

Göran Broström (2012) Event History Analysis with R, Taylor & Francis Group.

Other material will be indicated during the course.

Association in the course directory

Last modified: Th 23.03.2023 00:22