| Sign In to gain access to subscriptions and/or personal tools. |
Propensity ScoresAn Introduction and Experimental TestUniversity of Memphis, jluellen{at}memphis.edu
University of California, Merced
Southern Illinois University Propensity score analysis is a relatively recent statistical innovation that is useful in the analysis of data from quasi-experiments. The goal of propensity score analysis is to balance two non-equivalent groups on observed covariates to get more accurate estimates of the effects of a treatment on which the two groups differ. This article presents a general introduction to propensity score analysis, provides an example using data from a quasi-experiment compared to a benchmark randomized experiment, offers practical advice about how to do such analyses, and discusses some limitations of the approach. It also presents the first detailed instructions to appear in the literature on how to use classification tree analysis and bagging for classification trees in the construction of propensity scores. The latter two examples serve as an introduction for researchers interested in computing propensity scores using more complex classification algorithms known as ensemble methods.
Key Words: propensity score • quasi-experiment • classification tree • bagging • ensemble methods
Evaluation Review, Vol. 29, No. 6,
530-558 (2005) This article has been cited by other articles:
|
|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
 |
|
|
 |
|
Sign In to gain access to subscriptions and/or personal tools.
