Panel data, also known as longitudinal data, consists of multiple observations of the same subject over time. Many disciplines will analyze such data using a random intercept or random coefficients model. In this workshop, another way of modeling such data will be discussed: using fixed effects.
Fixed effects methods can be used in longitudinal studies to model only within-subject variation. By doing so, fixed effects methods allow for the researcher to control for all time invariant characteristics. This is one reason why fixed effect methods are a popular tool in the analysis of non experimental longitudinal data, and are widely used in econometric research.
In this workshop, we will cover the fundamentals of the fixed effects methods, compare and contrast the fixed effects methods to the mixed model approach for analyzing longitudinal data, and discuss a way to blend the two methods together using the Hybrid model (also known as the between-within model).
The workshop is intended for participants who have had some experience with multilevel modeling.