The great majority of all regression modeling explores and tests the association between independent and dependent variables. We are not able to claim the independent variable(s) has a causal relationship with the dependent variable. There are five specific model types that allow us to test for causality. Difference in differences models are one of the five.
To use a Difference in Differences (DiD) model your data must contain:
- measurements of the dependent variable
- measurements of the dependent variable before and after a specific event
- measurements for a treated and untreated group
This training focuses on the following topics:
- Examples of fields of studies utilizing this statistical approach
- The design of the DiD model
- Assumptions of DiD models
- Strengths and limitations
- Role of covariates
- Variation in treatment timing
About the Instructor
Jeff Meyer is a statistical consultant with The Analysis Factor, a stats mentor for Statistically Speaking membership, and a workshop instructor. Read more about Jeff here.