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.
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.
So you can’t randomize people into THAT condition? Now what?
Let’s say you’re investigating the impact of smoking on social outcomes like depression, poverty, or quality of life. Your IRB, with good reason, won’t allow random assignment of smoking status to your participants.
But how can you begin to overcome the self selected nature of smoking among the study participants? What if self-selection is driving differences in outcomes? Well, one way is to use propensity score matching and analysis as a framework for your investigation.
The propensity score is the probability of group assignment conditional on observed baseline characteristics. In this way, the propensity score is a balancing score: conditional on the propensity score, the distribution of observed baseline covariates will be similar between treated and untreated subjects.
In this webinar, we’ll describe broadly what this method is and discuss different matching methods that can be used to create balanced samples of “treated” and “non-treated” participants. Finally, we’ll discuss some specific software resources that can be found to perform these analyses.
Note: This training is an exclusive benefit to members of the Statistically Speaking Membership Program and part of the Stat’s Amore Trainings Series. Each Stat’s Amore Training is approximately 90 minutes long.