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.

中文翻译：

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捕捉小学三年一次的数学筛选变化的多种来源

贝叶斯潜在变化评分模型 (LCSM) 用于比较三年一次（秋季、冬季、春季）在基础数学计算和基于概念/应用程序课程的测量方面的变化模型。数据收集自 2-5 年级的小学生，每个年级大约 700 到 850 名学生（47%-54% 女性；78%-79% 白人，10%-11% 黑人，2%-4% 西班牙裔/拉丁裔, 2%–4% 亚裔，2–4% 美洲原住民或太平洋岛民；13%–14% 英语学习者；10%–14% 有特殊教育个性化教育计划）。结果与评估规范和先前独立发现的常见非线性增长模式相吻合。然而，贝叶斯 LCSM 捕获了先前研究中未观察到的实际相关的变化来源。多层支持系统中筛选和基于数据的决策的实际和方法学意义，