Fatih Unlu

Smoothing the Transition to Postsecondary Education: The Impact of the Early College Model

ABSTRACT Developed in response to concerns that too few students were enrolling and succeeding in postsecondary education, early college high schools are small schools that blur the line between high school and college. This article presents results from a longitudinal experimental study comparing outcomes for students accepted to an early college through a lottery process with outcomes for students who were not accepted through the lottery and enrolled in high school elsewhere. Results show that treatment students attained significantly more college credits while in high school, and graduated from high school, enrolled in postsecondary education, and received postsecondary credentials at higher rates. Results for subgroups are included.

Bias and Bias Correction in Multisite Instrumental Variables Analysis of Heterogeneous Mediator Effects.

We explore the use of instrumental variables (IV) analysis with a multisite randomized trial to estimate the effect of a mediating variable on an outcome in cases where it can be assumed that the observed mediator is the only mechanism linking treatment assignment to outcomes, an assumption known in the IV literature as the exclusion restriction. We use a random-coefficient IV model that allows both the impact of program assignment on the mediator (compliance with assignment) and the impact of the mediator on the outcome (the mediator effect) to vary across sites and to covary with one another. This extension of conventional fixed-coefficient IV analysis illuminates a potential bias in IV analysis which Reardon and Raudenbush refer to as “compliance-effect covariance bias.” We first derive an expression for this bias and then use simulations to investigate the sampling variance of the conventional fixed-coefficient two-stage least squares (2SLS) estimator in the presence of varying (and covarying) compliance and treatment effects. We next develop two alternate IV estimators that are less susceptible to compliance-effect covariance bias. We compare the bias, sampling variance, and root mean squared error of these “bias-corrected IV estimators” to those of 2SLS and ordinary least squares (OLS). We find that, when the first-stage F-statistic exceeds 10 (a commonly used threshold for instrument strength), the bias-corrected estimators typically perform better than 2SLS or OLS. In the last part of the article, we use both the new estimators and 2SLS to reanalyze data from two large multisite studies.

Estimating Causal Effects of Education Interventions Using a Two-Rating Regression Discontinuity Design: Lessons From a Simulation Study and an Application

ABSTRACT A valuable extension of the single-rating regression discontinuity design (RDD) is a multiple-rating RDD (MRRDD). To date, four main methods have been used to estimate average treatment effects at the multiple treatment frontiers of an MRRDD: the “surface” method, the “frontier” method, the “binding-score” method, and the “fuzzy instrumental variables” method. This article uses a series of simulations to evaluate the relative performance of each of these four methods under a variety of different data-generating models. Focusing on a two-rating RDD (2RRDD), we compare the methods in terms of their bias, precision, and mean squared error when implemented as they most likely would be in practice—using optimal bandwidth selection. We also apply the lessons learned from the simulations to a real-world example that uses data from a study of an English learner reclassification policy. Overall, this article makes valuable contributions to the literature on MRRDDs in that it makes concrete recommendations for choosing among MRRDD estimation methods, for implementing any chosen method using local linear regression, and for providing accurate statistical inferences.

Preparing Students for College: Lessons Learned from the Early College.

This article utilizes mixed methods—a lottery-based experimental design supplemented by qualitative data—to examine college readiness within an innovative high school setting: early college high schools. Early colleges are small schools that merge the high school and college experiences and are targeted at students underrepresented in college. Results show that early college students are more likely to have successfully completed the courses they need for entrance into college; early college students also graduated from high school at a higher rate. Interview and survey data show that early college students are generally considered similarly prepared to more traditional postsecondary students. The interview data also provide detailed descriptions of the kinds of strategies the schools use to support college readiness. The article concludes with lessons learned for secondary school principals.