An Empirical Investigation of Variance Design Parameters for Planning Cluster-Randomized Trials of Science Achievement
An Empirical Investigation of Variance Design Parameters for Planning Cluster-Randomized Trials of Science Achievement
复制标题
科学成果整群随机试验规划方差设计参数的实证研究
作者：
Carl D. Westine;Jessaca K. Spybrook;Joseph A. Taylor
背景：以前的研究主要集中在对数学和阅读成绩的整群随机试验(CRT)的设计参数进行经验性估计。关于设计参数在其他教育成果中的比较，我们知之甚少。目的：本文提出了可用于在科学教育中适当地为CRT供电的设计参数的经验估计，并将它们与使用数学和阅读的估计进行了比较。研究设计：对于科学成就的无条件两级(学校学生)和三级(地区学校学生)分层线性模型，计算组内相关性(ICCs)的估计。然后考虑相关的学生和学校水平的前测和人口统计协变量，并计算解释的方差估计。科目：德克萨斯州5年级、8年级、10年级和11年级连续五年的学生水平数据。衡量标准：由德克萨斯州知识与技能评估衡量的科学、数学和阅读成绩原始分数。结果：研究结果表明，不同年级的理科ICC在0.172到0.196之间，总体上高于可比统计数据，数学的ICC在.163-.172之间，阅读的ICC在.099-.156之间。如果可行，一年的滞后学生水平的科学预测测试解释了结果中的最大变异性。在没有一年滞后的学生水平的科学预测测试的情况下，一年落后的学校水平的科学预测测试是最好的选择。结论：科学教育研究人员应利用从科学成果中得出的设计参数。
Background: Prior research has focused primarily on empirically estimating design parameters for cluster-randomized trials (CRTs) of mathematics and reading achievement. Little is known about how design parameters compare across other educational outcomes. Objectives: This article presents empirical estimates of design parameters that can be used to appropriately power CRTs in science education and compares them to estimates using mathematics and reading. Research Design: Estimates of intraclass correlations (ICCs) are computed for unconditional two-level (students in schools) and three-level (students in schools in districts) hierarchical linear models of science achievement. Relevant student- and school-level pretest and demographic covariates are then considered, and estimates of variance explained are computed. Subjects: Five consecutive years of Texas student-level data for Grades 5, 8, 10, and 11. Measures: Science, mathematics, and reading achievement raw scores as measured by the Texas Assessment of Knowledge and Skills. Results: Findings show that ICCs in science range from .172 to .196 across grades and are generally higher than comparable statistics in mathematics, .163–.172, and reading, .099–.156. When available, a 1-year lagged student-level science pretest explains the most variability in the outcome. The 1-year lagged school-level science pretest is the best alternative in the absence of a 1-year lagged student-level science pretest. Conclusion: Science educational researchers should utilize design parameters derived from science achievement outcomes.