´╗┐´╗┐ GESIS Training Courses

Administrative Coordination

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

PIAAC-L Workshop B: Analyzing PIAAC and PIAAC-L Data with Stata

GESIS Mannheim
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Lecturer(s): Prof. Dr. Simon Wiederhold, Prof. Dr. Guido Schwerdt

About the lecturer - Prof. Dr. Simon Wiederhold

About the lecturer - Prof. Dr. Guido Schwerdt

Course description

The first part of the workshop will lay the conceptual foundation for the analysis of PIAAC and its longitudinal extension, PIAAC-L. In particular, participants will gain insights into how the data can be used to improve knowledge about the determinants and consequences of cognitive skills. Special emphasis will be on the possibilities of estimating causal effects. After this general discussion of the analysis potential of PIAAC and PIAAC-L, workshop participants will develop their methodological skills in analyzing the data in various applied sessions. Participants will acquire knowledge in both cross-sectional and panel data analyses. It is also discussed how regression results can be depicted graphically. In the second part of the workshop, participants can present their own research or research ideas with PIAAC/ PIAAC-L data and receive detailed feedback on how to improve the analysis (optional).
Data: PIAAC Public Use Files; German PIAAC Scientific Use File (doi: 10.4232/1.12660); PIAAC-L Scientific Use Files (doi: 10.4232/1.12925).

Target group

The PIAAC workshops welcome researchers from different disciplines interested to work or already working with PIAAC data. The workshop comprises lectures and practical sessions covering the following elements: (a) Theoretical and methodological input from the lecturers (see description of contents above); (b) Opportunity for participants to present their own research or research ideas with PIAAC and/ or PIAAC-L data; (c) Discussion of the questions outlined in the workshop regarding the data used and methods as well as specific feedback from the lecturers.


It is expected that the participants have good empirical knowledge and experience in the respective statistical software.


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