The Mechanics of Longitudinal Data Analysis
Oliver Lipps, FORS – Swiss Centre of Expertise in the Social Sciences
9.00-12.00 July 17th 2017
I will first give a very brief refresher of linear regression and will then introduce panel data and the structure necessary to conduct longitudinal analysis before we study the concept of causality based on the counterfactual approach. Then I will explain the idea of fixed effect (FE) models and its advantage over usual linear models to estimate unbiased (in fact, less biased) coefficients using a small N example. We will start with a (pooled) OLS model, then control for a confounding time-constant variable in order to reduce omitted variable bias and finally a FE model in order to eliminate bias from any omitted time-constant variable. I will explain in each step how we come closer to an unbiased regression coefficient.
After introducing the random effect (RE) estimator in the next step, I will then give some (typical) examples of pooled OLS, FE, and RE estimators using data from a large N panel survey. We will finally discuss the Hausman test as a formal tool to decide whether RE models or FE models must be used.
The core of the course is to understand how the FE model works “mechanically”, i.e. how can within-individual transformations be visualized using a small-N example. For the small-N application examples I will provide the corresponding Stata syntax. In addition, participants will learn more about the pros and cons of different longitudinal linear models, including the FE model, the RE model, and the pooled OLS model. Some familiarity with panel data and regression models is assumed.
About the instructor:
Oliver Lipps is senior researcher and head of the methodological research program at FORS – Swiss Centre of Expertise in the Social Sciences – Lausanne and member of the Swiss Household Panel (SHP) team. In addition, he is lecturer in survey methodology and survey research at the Institute of Sociology at the University of Bern (Switzerland) and at the Swiss Summer School in Lugano. His research interests focus on unit and item nonresponse in cross-sectional and especially longitudinal designs, effects due to interviewers, incentives, mode, and language proficiency/acculturation issues, and income imputation methods. He is also interested in substantive issues such as health and subjective well-being.