Date of Award
Doctor of Philosophy
Educational Leadership, Research and Technology
Dr. Brooks E. Applegate
Dr. Joseph Kretovics
Dr. Warren E. Lacefield
Often researchers face situations where comparative studies between two or more programs are necessary to make causal inferences for informed policy decision-making. Experimental designs employing randomization provide the strongest evidence for causal inferences. However, many pragmatic and ethical challenges may preclude the use of randomized designs. In such situations, subject matching provides an alternative design approach for conducting causal inference studies. This study examined various design conditions hypothesized to affect matching procedures’ bias recovery ability.
See attachment for full abstract.
Itang'ata, Mukaria J. J., "A Comparative Study of Exact Versus Propensity Matching Techniques Using Monte Carlo Simulation" (2013). Dissertations. 148.