Lori G. Boyland, Jeff Swensson, John G. Ellis, Lauren L. Coleman and Margaret I. Boyland
School principals should lead for social change, particularly in support of vulnerable or marginalized students. An important social justice issue in which principals must provide…
Abstract
School principals should lead for social change, particularly in support of vulnerable or marginalized students. An important social justice issue in which principals must provide strong leadership, but may not be adequately prepared in university training, is creating positive and inclusive school environments for lesbian, gay, transgender, bisexual, and questioning (LGBTQ) students. Research reveals that LGBTQ students experience high rates of discrimination, bullying, and physical assault due to their sexual orientation or gender expression. This Application Brief describes how faculty members at a Midwest university developed curriculum and pedagogy for their principal preparation program with the goal of promoting the knowledge and skills that future school leaders need to provide effective leadership for protection, acceptance, and affirmation of LGBTQ students.
Applied econometric analysis is often performed using data collected from large-scale surveys. These surveys use complex sampling plans in order to reduce costs and increase the…
Abstract
Applied econometric analysis is often performed using data collected from large-scale surveys. These surveys use complex sampling plans in order to reduce costs and increase the estimation efficiency for subgroups of the population. These sampling plans result in unequal inclusion probabilities across units in the population. The purpose of this paper is to derive the asymptotic properties of a design-based nonparametric regression estimator under a combined inference framework. The nonparametric regression estimator considered is the local constant estimator. This work contributes to the literature in two ways. First, it derives the asymptotic properties for the multivariate mixed-data case, including the asymptotic normality of the estimator. Second, I use least squares cross-validation for selecting the bandwidths for both continuous and discrete variables. I run Monte Carlo simulations designed to assess the finite-sample performance of the design-based local constant estimator versus the traditional local constant estimator for three sampling methods, namely, simple random sampling, exogenous stratification and endogenous stratification. Simulation results show that the estimator is consistent and that efficiency gains can be achieved by weighting observations by the inverse of their inclusion probabilities if the sampling is endogenous.