We used the Stanford education data archive (SEDA) data to examine the heterogeneity among urban school districts in the United States. The SEDA 2.1 includes data sets on students' mathematics (Math) and English language arts (ELA) achievement from 2008 to 2014 at the district level. Growth mixture modeling was used to uncover the underlying…

When the 2017 National Assessment of Educational Progress was released earlier this year, most states learned that their average scores in grades 4 and 8 reading and mathematics remained relatively flat since 2015. More troubling though, students performing at the lowest 10th and 25th percentiles saw a decline in their scores. To confront this…

To answer questions about how students' proficiencies are changing over time, educational researchers are looking for data sources that span many years. Clearly, for answering questions about student growth, a longitudinal study--in which a single sample is followed over many years--is preferable to repeated cross-sectional samples--in which a…