PD Presenters Week: M. H. Clark and Haiyan Bai on Using Propensity Score Adjustments

Hi! We are M. H. Clark and Haiyan Bai from the University of Central Florida in Orlando, Florida. Over the last several years propensity score adjustments (PSAs) have become increasingly popular; however, many evaluators are unsure of when to use them. A propensity score is the predicted probability of a participant selecting into a treatment program based on several covariates. Theses scores are used to make statistical adjustments (i.e., matching, weighting, stratification) to data from quasi-experiments to reduce selection bias.

Lesson Learned:

PSAs are not the magic bullet we had hoped they would be. Never underestimate the importance of a good design. Many researchers assume that they can fix poor designs with statistical adjustments (either with individual covariates or propensity scores). However, if you are able to randomly assign participants to treatment conditions or test several variations of your intervention, try that first. Propensity scores are meant to reduce selection bias due to non-random assignment, but can only do so much.

Hot Tip:

Plan ahead! If you know that you cannot randomly assign participants to conditions and you MUST use a quasi-experiment with propensity score adjustments, be sure that you measure covariates (individual characteristics) that are related to both the dependent variable and treatment choice. Ideally, you want to include all variables in your propensity score model that may contribute to selection bias. Many evaluators consider propensity score adjustments after they have collected data and cannot account for some critical factors that cause selection bias. In which case, treatment effects may still be biased even after PSAs.

Hot Tip:

Consider whether or not you need propensity scores to make your adjustments. If participants did not self-select into a treatment program, but were placed there because they met a certain criterion (i.e., having a test score above the 80th percentile), a traditional analysis of covariance used with regression discontinuity designs may be more efficient than PSAs. Likewise, if your participants are randomly assigned by pre-existing groups (like classrooms) using a mixed-model analysis of variance might be preferable.  On the other hand, sometimes random assignment does not achieve its goal in balancing all covariates between groups. If you find that the parameters of some of your covariates (i.e., average age) are different in each treatment condition even after randomly assigning your participants, PSAs may be a useful way of achieving the balance random assignment failed to provide.

Rad Resource:

William Holmes recently published a great introduction to using propensity scores and Haiyan Bai and Pan Wei have a book that will be published next year.

Want to learn more? Register for Propensity Score Matching: Theories and Applications at Evaluation 2014.

This week, we’re featuring posts by people who will be presenting Professional Development workshops at Evaluation 2014 in Denver, CO. Click here for a complete listing of Professional Development workshops offered at Evaluation 2014. Do you have questions, concerns, kudos, or content to extend this aea365 contribution? Please add them in the comments section for this post on the aea365 webpage so that we may enrich our community of practice. Would you like to submit an aea365 Tip? Please send a note of interest to aea365@eval.org. aea365 is sponsored by the American Evaluation Association and provides a Tip-a-Day by and for evaluators.


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