When researchers are unable to randomly assign students to treatment conditions, selection bias is introduced into the estimates of treatment effects. Random assignment to treatment conditions, which has historically been the scientific benchmark for causal inference, is often impossible or unethical to implement in educational systems. For example, researchers cannot deny services to those who stand to gain from participation in an academic program. Additionally, students select into a particular treatment group through processes that are impossible to control, such as those that result in a child dropping-out of high school or attending a resource-starved school. Propensity score methods provide valuable tools for removing the selection bias from quasi-experimental research designs and observational studies through modeling the treatment assignment mechanism. The utility of propensity scores has been validated for the purposes of removing selection bias when the observations are assumed to be independent; however, the ability of propensity scores to remove selection bias in a multilevel context, in which group membership plays a role in the treatment assignment, is relatively unknown. A central purpose of the current study was to begin filling in the gaps in knowledge regarding the performance of propensity scores for removing selection bias, as defined by covariate balance, in multilevel settings using a Monte Carlo simulation study. The performance of propensity scores were also examined using a large-scale national dataset. Results from this study provide support for the conclusion that multilevel characteristics of a sample have a bearing upon the performance of propensity scores to balance covariates between treatment and control groups. Findings suggest that propensity score estimation models should take into account the cluster-level effects when working with multilevel data; however, the numbers of treatment and control group individuals within each cluster must be sufficiently large to allow estimation of those effects. Propensity scores that take into account the cluster-level effects can have the added benefit of balancing covariates within each cluster as well as across the sample as a whole.

Increasing numbers of mathematics teachers must be recruited in coming years, because of a growing student population, teacher attrition, calls for smaller class size, and the need to replace out-of-subject teachers. Recruitment can be made more effective and efficient, if better information on career paths is provided to decision makers. This study attempts to analyze the academic decisions which lead to the outcome "becoming a mathematics teacher". Four groups were compared and contrasted: mathematics teachers, science teachers, other teachers, and non-teachers. Science teachers were removed from the "other teachers" category because of their many similarities to mathematics teachers on the variables examined. The question of whether these groups differ in ways that could help predict the outcome of interest was examined using the NCES dataset Baccalaureate &Beyond:93/97, which provides thousands of variables on academic path, demographics, and labor market histories for over 8,000 individuals. It was analyzed using the NCES online analytic tool DAS to generate tables showing percentage distribution of the four groups on variables organized according to the concepts demographics, family environment, academic path, and academic achievement. Further examination was conducted by entering the variables into a discriminant analysis. Mathematics teachers were found to differ from teachers of other K-12 fields on all of the four conceptual categories. However, only a few such differences were statistically significant. More significant differences were observed when the analyses were conducted separately for women and men. The trend observed was that those who became mathematics teachers were more likely to have attended public high schools and to have first attended two-year colleges; to have lower GPAs, more mathematics credits, and midrange CEE scores; and to be female.