Capturing Multiple Sources of Change on Triannual Math Screeners in Elementary School

Abstract

Bayesian latent change score modeling (LCSM) was used to compare models of triannual (fall, winter, spring) change on elementary math computation and concepts/applications curriculum-based measures. Data were collected from elementary students in Grades 2–5, 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). Results converged with common nonlinear growth patterns from the assessment norms and prior independent findings. However, Bayesian LCSMs captured practically relevant sources of change not observed in prior studies. Practical and methodological implications for screening and data-based decision-making in multitiered systems of support, limitations, and future directions are discussed.