The objective for quasi-experimental designs is to establish cause and effect relationships between the dependent and independent variables. However, they have one big challenge in achieving this objective: lack of an established control group.
There are ways, though, to create a post-hoc control group. One way is to match non-treated subjects with treated subjects.
The most common matching method is Propensity Score Matching. Gaining popularity as a matching method is Coarsened Exact Matching. How are these matching methods different?
To understand the differences, this Stats Amore Training explores the following:
- A discussion on why and when to match data
- How propensity score matching is constructed
- How coarsened exact matching is constructed
- Advantages and disadvantages of both approaches
Note: This training is a benefit to members of the Statistically Speaking Membership Program and part of the Stat’s Amore Trainings Series.
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.
You'll get access to this training webinar, 130+ other stats trainings, a pathway to work through the trainings that you need — plus the expert guidance you need to build statistical skill with live Q&A sessions and an ask-a-mentor forum.