Considerable interest lies in the growth in educational achievement that occurs over the course of a child's schooling. This paper demonstrates a simple but effective approach for the comparison of growth rates, drawing on a method first proposed some 80 years ago and applying it to data from the Australian National Assessment Program. The…

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…

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…