An Empirical Investigation of Variance Design Parameters for Planning Cluster-Randomized Trials of Science Achievement

作者：Westine Carl D^*; Spybrook Jessaca; Taylor Joseph A

来源：Evaluation Review, 2013, 37(6): 490-519.

DOI：10.1177/0193841X14531584

• intraclass correlation
• science education
• design parameters
• cluster-randomized trials
• hierarchical linear models

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.

• 出版日期2013-12