Longitudinal Analysis with Panel Data

Lecturer: Oliver Lipps & Ursina Kuhn

Modality: In presence

Week 2: 17-21 August 2026

Workshop contents and objectives

This workshop focuses on longitudinal data, which observe the same persons, firms or other entities at different points in time. Such data is crucial for studying stability, change, and causal mechanisms. During the course, participants will learn how to organize longitudinal data, measure change, and to analyse it using statistical methods.

The main focus for data analysis will be fixed effects models. While cross-sectional models compare different units (for example the happiness of married and unmarried people), fixed effects models analyse changes within units over time (for example happiness of a person before and after marriage). By implicitly controlling for unobserved time-constant variables, these models are well suited to identifying causality. Other models covered include pooled OLS, multilevel/random effects models, growth models and cross-lagged models for longitudinal data.

The focus will be on understanding the mechanics of the different models, so that participants will be able to choose suitable modelling strategies for their research questions and the characteristics of the data (e.g. number of waves, number of observations, type of units).

We will also address how data quality may impact statistical analysis (e.g. missing values, attrition). Using real data from the Swiss Household Panel, the course combines statistical tools with practical challenges such as limited statistical power due to small N, few time points, or attrition. 

Workshop design

The course consists of an equal share of lectures and discussion, as well as exercises using real data. These exercises consist of individual hands-on tasks and small group activities, in which the methods learned will be applied to real data. Groups will replicate and extend the analysis of recently published papers using panel data. Alternatively, participants may analyse and present their own data. Each group will present their findings in plenary sessions on the last day.

With two instructors available, there will be ample opportunity for individual counselling. Both have extensive practical experience in collecting, preparing and analysing panel data and are happy to share their knowledge and provide guidance on your current data analysis.

Detailed lecture plan (daily schedule)

Class materials

• Prepared student data sets with example syntax and exercises.
• Power Point presentations.
• A selection of application examples of longitudinal data analysis from scientific journals in the social sciences (sociology, psychology, economics and political science). We will replicate and expand the analysis of some of these papers in the group work.
• The compendium “Stata Data Management” - written by the instructors - provides an introduction into longitudinal data analysis based on data from the Swiss Household Panel.

Prerequisites

We will use the Stata software and assume some knowledge of Stata with cross-sectional data. Experienced R-users can conduct the exercises with R, making use of AI tools to translate Stata code to R.

Recommended readings or preliminary material

We will provide a comprehensive list of references for the different methods discussed in the workshop.

What our participants appreciated most

"Overall, | find it helpful to revise the knowledge | had and understand new concepts. What | find the most useful is the support from the instructors and the application part."