Propensity score matching is a relatively new technique used in observational studies to approximate data that have been randomly assigned to treatment. This technique assimilates the values of several covariates into a single propensity score that is used as a matching variable to create similar groups. This dissertation comprises two separate but related studies. The first is a simulation wherein generated data are analyzed to determine if one of three propensity score matching techniques performs more effectively in reducing sample bias and if those methods more accurately detect a simulated effect size than does a traditional analysis of covariance (ANCOVA). [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]