GESIS Training Courses

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Prof. Dr.
Reinhard Schunck
Tel: +49 221 47694-160

Administrative Coordination

Angelika Ruf
Tel: +49 221 47694-162

Week 1: Causal Analysis with Panel Data: Potentials and Limitations

Prof. Dr. Michael Windzio, , Jun Prof. Dr. Marco Giesselmann

Date: 06.03 - 10.03.2017 ics-file

Location: Cologne

About the lecturer - Prof. Dr. Michael Windzio

About the lecturer - Jun Prof. Dr. Marco Giesselmann

Course description

Longitudinal data is widely discussed as an important means to validate causal interpretations. This course introduces the basic methods suitable to exploit this potential of panel data. We start with methods for categorical independent variables. Here, we introduce the simple Life Event Design (LED) and explain how this is related to the Difference-in-Difference Estimator (DiD). If the independent variable is measured on a metric scale, social scientists usually employ regression techniques, which is also the case for longitudinal data. Therefore, we discuss extensions of the simple regression framework addressing the properties and potentials of longitudinal data. Concretely, we introduce Fixed Effects (FE), First Differences (FD), and Hybrid Regression Models (HM) and discuss the differences and assumptions of these techniques. For research questions with categorical dependent variables, we introduce two applications of logistic regression suitable for the analysis of longitudinal data: the Conditional Logistic Regression, which resembles the benefits of FE, and techniques of Event History Analysis (EHA), which are particularly suitable if the researcher explicitly focuses transitions of the dependent variable. In all parts of the course, we put a strong emphasize on the intuitive understanding of the methods employed. All exercises are based on the data from the Socio Economic Panel Study (SOEP), which will be introduced during the course.
Panel data analysis, fixed effects regression, event history analysis, hybrid regression, conditional logistic regression.

Target group

Participants will find the course useful if they
  • have basic quantitative skills and want to add an extra qualification to their methodological knowledge,
  • are planning empirical projects on the basis of longitudinal data,
  • are pursuing a career as empirical social scientist and have not yet learned how to analyze panel data.

Learning objectives

By the end of the course participants will  
  • be familiar the potential and limitations of longitudinal data to improve causal interpretations,  
  • have the ability to make accurate methodological choices in particular research situations involving longitudinal data,  
  • know how to apply the most important longitudinal techniques in theory and practice (on the basis of the statistical software stata, have a good overview on the analytical potential for longitudinal analysis of the German Socio Economic Panel (SOEP).


  • This course addresses beginners of panel data analysis. However, participants should have a solid knowledge of OLS and logistic regression techniques.  
  • We will use the software program Stata in the exercises. Participants be familiar with the basics of Stata, its commands to manage data, and know how to produce descriptive and multivariate statistics. 


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