Successive Student Cohorts and Longitudinal Growth Models: An Investigation of Elementary School Mathematics Performance

Mathematics achievement data from three longitudinally matched student cohorts were analyzed with multilevel growth models to investigate the viability of using status and growth-based indices of student achievement to examine the multi-year performance of schools. Elementary schools in a large southwestern school district were evaluated in terms of the mean achievement status and growth of students across cohorts as well as changes in the achievement status and growth of students between student cohorts. Results indicated that the cross and between-cohort performance of schools differed depending on whether the mean achievement status or growth of students was considered. Results also indicated that the cross-cohort indicators of school performance were more reliably estimated than their between-cohort counterparts. Further examination of the performance indices revealed that cross-cohort achievement status estimates were closely related to student demographics while between-cohort estimates were associated with cohort enrollment size and cohort initial performance status. Of the four school performance indices studied, only student growth in achievement (averaged across cohorts) provided a relatively reliable and unbiased indication of school performance. Implications for the No Child Left Behind school accountability framework are discussed

A Multilevel, Longitudinal Analysis of Middle School Math and Language Achievement

The performance of schools in a large urban school district was examined using achievement data from a longitudinally matched cohort of middle school students. Schools were evaluated in terms of the mean achievement and mean growth of students in mathematics and language arts. Application of multilevel, longitudinal models to student achievement data revealed that 1) school performance varied across both outcome measures in both subject areas, 2) significant proportions of variation were associated with school-to-school differences in performance, 3) evaluations of school performance differed depending on whether school mean achievement or school mean growth in achievement was examined, and 4) school mean achievement was a weak predictor of school mean growth. These results suggest that assessments of school performance depend on choices of how data are modeled and analyzed. In particular, the present study indicates that schools with low mean scores are not always “poor performing” schools. Use of student growth rates to evaluate school performance enables schools that would otherwise be deemed low performing to demonstrate positive effects on student achievement. Implications for state accountability systems are discussed