Friday, November 9, 2012
Hanover B (Radisson Plaza Lord Baltimore Hotel)
*Names in bold indicate Presenter
In this paper we consider the challenges and implications of controlling for school contextual bias when modeling teacher preparation program effects. Because teachers from any one preparation program are hired in more than one school and teachers are not randomly distributed across schools, failing to account for contextual factors in achievement models could bias preparation program estimates. Including school fixed effects controls for school environment by relying on differences among student outcomes within the same schools to identify the program effects. However, the fixed effect specification may be unidentified, imprecise or biased if certain data requirements are not met. Using statewide data from Florida, we examine whether the inclusion of school fixed effects is feasible in this setting, the sensitivity of the estimates to assumptions underlying for fixed effects, and what their inclusion implies about the precision of the preparation program estimates. We also examine whether restricting the estimation sample to inexperienced teachers and whether shortening the data window impacts the magnitude and precision of preparation program effects. Finally, we compare the ranking of preparation programs based on models with no school controls, school covariates and school fixed effects. We find that some preparation program rankings are significantly affected by the model specification. We discuss the implications of these results for policymakers.