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.
Participants will find the course useful if they
By the end of the course participants will
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.
One week courses:
For more information, please check the GESIS website.