January Stacy-Ann A, Van Norman Ethan R, Christ Theodore J, Ardoin Scott P, Eckert Tanya L, White Mary Jane
Department of Psychology.
Department of Education and Human Services.
Sch Psychol Q. 2019 Jan;34(1):119-127. doi: 10.1037/spq0000274. Epub 2018 Oct 4.
School-based professionals often use curriculum-based measurement of reading (CBM-R) to monitor the progress of students with reading difficulties. Much of the extant CBM-R progress monitoring research has focused on its use for making group-level decisions, and less is known about using CBM-R to make decisions at the individual level. To inform the administration and use of CBM-R progress monitoring data, the current study evaluated the utility of 4 progress monitoring schedules that differed in frequency (once or twice weekly) and density (1 or 3 probes). Participants included 79 students (43% female; 51% White, 25% Hispanic or Latino, 11% Black or African American, 1% other, 12% unknown) in Grades 2 ( = 45) and 4 ( = 34) who were monitored across 10 weeks (February to May). Consistent with a focus on individual-level decision making, we used regression and mixed-factorial analysis of variances (ANOVAs) to evaluate the effect of progress monitoring schedule frequency, schedule density, grade level, and their interaction effects on CBM-R intercept, slope, of the slope and of the estimate Results revealed that (a) progress monitoring schedule frequency and density influenced the magnitude of (b) density had a significant but negligible impact on and (c) grade level had a significant effect on slope and intercept. None of the interaction effects were statistically significant. Findings from this study have implications for practitioners and researchers aiming to monitor students' progress with CBM-R. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
以学校为基础的专业人员经常使用基于课程的阅读测量(CBM-R)来监测有阅读困难学生的进步情况。现有的许多CBM-R进步监测研究都集中在其用于做出群体层面决策的用途上,而对于使用CBM-R做出个体层面的决策则了解较少。为了为CBM-R进步监测数据的管理和使用提供信息,本研究评估了4种在频率(每周一次或两次)和密度(1次或3次探测)上不同的进步监测时间表的效用。参与者包括79名二、四年级的学生(43%为女生;51%为白人,25%为西班牙裔或拉丁裔,11%为黑人或非裔美国人,1%为其他,12%信息不明),在10周内(2月至5月)接受监测。与关注个体层面决策一致,我们使用回归和混合因子方差分析(ANOVA)来评估进步监测时间表频率、时间表密度、年级水平及其交互作用对CBM-R截距、斜率、斜率的标准差和估计值的标准差的影响。结果显示:(a)进步监测时间表的频率和密度影响了标准差的大小;(b)密度对标准差有显著但可忽略不计的影响;(c)年级水平对斜率和截距有显著影响。没有任何交互作用具有统计学意义。本研究的结果对旨在使用CBM-R监测学生进步的从业者和研究人员具有启示意义。(《心理学文摘数据库记录》(c)2019美国心理学会,保留所有权利)