Capturing multiple sources of change on triannual math screeners in elementary school

Abstract

In this study, we used Bayesian latent change score modeling (LCSM) to compare models of triannual (fall, winter, spring) change on elementary math computation and concepts/applications curriculum-based measures. We used data from elementary students in Grades 2–5 with approximately 700 to 850 students in each grade (47%–54% female; 78%–79% White, 10%–11% Black, 2%–4% Hispanic/Latino, 2%–4% Asian, 2–4% Native American or Pacific Islander; 13%–14% English learner; 10%–14% had special education individualized education plans). Our results converged with common nonlinear growth patterns from the assessment norms and prior independent findings. However, Bayesian LCMSs captured practically-relevant sources of change not observed in prior studies. Practical and methodological implications for screening and data-based decision-making in multi-tiered systems of support, limitations, and future directions are discussed.