This course introduces tools for conducting empirical analysis of longitudinal data: data collected from the same entities at different points in time. Such data is commonly seen in health and social sciences research. Participants will obtain a good understanding of methods for analyzing longitudinal data including: pooled OLS, fixed effects estimation, random effects models, and fixed effects models for binary outcomes. There will be a mixture of theoretical and practical sessions using the statistical software package STATA.
- Features of longitudinal data
- Repeated Cross Sections and Difference-in-differences
- Fixed Effects Models
- Random Effects Model
- Correlated Random Effects Model
- Fixed Effects Logit
Who Should Attend
Researchers, academics, and students who plan to use repeated cross sectional data or longitudinal data in their research.
Participants are expected to have a good understanding of linear regression models and logit regression models and should have some prior experience using statistical software packages such as STATA, SAS or SPSS.