Urban Achievement Performance: A Longitudinal Analysis Using Growth Mixture Modeling

• In Event: 31.016 - Reconsidering methodological and theoretical perspectives in research on students of color across diverse education contexts

Abstract

We used the Stanford Education Data Archive (SEDA) data to examine the heterogeneity among urban districts. The SEDA includes a set of publicly available data sets on students’ Math and ELA achievement from 2008 to 2014 at the district-level. Growth mixture modeling was used in this study to uncover the underlying growth trajectories for student achievement from the 3rd to the 8th grade. Two and three growth patterns were observed for ELA and Math achievement over time, respectively. Parent characteristics, race/ethnicity composition, the percentage of students who were English Language Learners, special education, free or reduced lunch etc. are included to explain the various growth patterns.

Authors

• Karen Ramlackhan, University of South Florida

• Yan Wang, University of Massachusetts - Lowell