Education research rarely lends itself to large scale experimental research and true randomization, leaving the researcher to quasi-experimental designs. The problem with quasi-experimental research is that underlying factors may impact group selection and lead to potentially biased results. One way to minimize the impact of non-randomization is through the use of propensity scores. First developed by Rosenbaum & Rubin (1983b), these scores allow researchers to balance non-equivalent groups though matching on a singular scalar variable. The present paper will present the theoretical framework behind propensity scores along with a heuristic data set to demonstrate propensity score calculation and evaluation. Appended is: "PASW (v17.0) Syntax for Propensity Score Matching using Matching within Calipers." (Contains 4 tables.)