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…

Sources of longitudinal achievement data are increasing thanks partially to the expansion of available interim assessments. These tests are often used to monitor the progress of students, classrooms, and schools within and across school years. Yet, few statistical models equipped to approximate the distinctly seasonal patterns in the data exist,…

Our purpose was to examine different approaches to modeling the time-varying nature of exceptionality classification. Using longitudinal data from one state's mathematics achievement test for 28,829 students in Grades 3 to 8, we describe the reclassification rate within special education and between general and special education, and compare four…

The 2001 authorization of the No Child Left Behind Act and its standards and accountability requirements generated interest among state education agencies in Florida, Mississippi, and North Carolina, which are served by the Regional Educational Laboratory Southeast, in monitoring changes in student reading and math proficiency at the school level.…

Alternate assessments based on alternate achievement standards (AA-AAS) are designed to measure the academic achievement of students with the most significant cognitive disabilities (SWSCDs). AA-AAS present unique measurement challenges because of the inherent need for individualization in item presentation and response, combined with expectations…