Association for Public Policy Analysis & Management

High school dropout rates are a concerning trend for educators, administrators, education policy makers, and communities. A promising effort to address dropout rates is an empirical approach known as Early Warning Systems (EWS). The EWS is a prediction model that determines an individual student’s risk of dropping out of high school. The prediction model is designed to alert educators and parents to the risk at levels at an early stage. Doing so allows for earlier interventions in order to shift the trajectories of students displaying risks for dropping out. In this study, we produce a simulated sample of students consistent with the existing covariance matrix of variables from Delaware's EWS report (Merola & Fernandez, 2010). This work integrates extant research on EWS models and the educational imperative to predict a student’s likelihood of school dropout. Dropout rates have been shown to significantly impact minority populations; however, many statewide dropout models do not incorporate racial identity as a predictor. In this study, we conduct a simulation model with three continuous predictors (attendance rate, number of disciplinary referrals, and English Language Arts (ELA) final score). In the second model, we then include student race along with the three continuous predictors in order to determine whether the model fit is significantly different with inclusion of student race. The third model explores community-level factors (measures of income and education) influencing an individual student’s propensity to drop out of high school. Cases are randomly assigned to DE high schools, proportional to the amount of DE’s total 9^th grade students are enrolled at each school. American Community Survey data for the census block of each school linked with the students assigned to each school, as well as the three original continuous predictors and race. The interpretations of the 3 models reveal that the statistical significance of the number of disciplinary offenses disappears once race, and neighborhood measures of income and education are taken into account.