We thank Howard J. Klein, Raymond A. Noe, Chongwei Wang, Donald E. Powers, and Donald A. Rock for generously sharing their data with us for illustrating propensity scoring analyses.
Balancing Treatment and Control Groups in Quasi-Experiments: An Introduction to Propensity Scoring
Version of Record online: 15 JAN 2013
© 2013 Wiley Periodicals, Inc.
Volume 66, Issue 2, pages 407–442, Summer 2013
How to Cite
Connelly, B. S., Sackett, P. R. and Waters, S. D. (2013), Balancing Treatment and Control Groups in Quasi-Experiments: An Introduction to Propensity Scoring. Personnel Psychology, 66: 407–442. doi: 10.1111/peps.12020
- Issue online: 20 MAY 2013
- Version of Record online: 15 JAN 2013
- Accepted manuscript online: 3 NOV 2012 10:51AM EST
Organizational and applied sciences have long struggled with improving causal inference in quasi-experiments. We introduce organizational researchers to propensity scoring, a statistical technique that has become popular in other applied sciences as a means for improving internal validity. Propensity scoring statistically models how individuals in a quasi-experiment have been assigned to conditions in order to estimate treatment effects among individuals with approximately equal probabilities of receiving the treatment. If propensity scores are created from relevant covariates, matching on the propensity score makes treatment assignment ignorable and approximates a true experimental design. We illustrate how matching on the propensity score can be applied by using 2 examples: examining the effects of online instruction and estimating the benefits of preparatory coaching for the SAT. In both cases, propensity-scoring methods effectively reduced inequivalence between treatment and control groups on many variables. Propensity scoring stands out as a valuable technique capable of improving causal inference from many of organizational research's quasi-experiments.