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

• DOI: 10.1111/ldrp.12296
• Publication Date: 2022-11-14T21:57:00Z
• Authors: Garret J. Hall; David Kaplan; Craig A. Albers
• Keywords: 4. Education; 0101 mathematics; 01 natural sciences

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