Advancing Methodological Knowledge in STEM Education Research: An Empirical Investigation of Design Parameters for Planning Cluster Randomized Trials in Science Education

认识到需要强大的工具来加强科学教育的研究，该项目正在开发一套资源，以协助研究人员规划科学教育干预措施的集群随机试验。这个全面项目的目标是开发统计资源，以便在科学教育研究中设计和实施严格的集群随机试验（crt）；提高进行功率分析所需参数范围的准确性；并通过免费的功率分析软件包为科学研究界开发统计资源。来自生物科学课程研究中心和西密歇根大学的课程开发人员和研究人员合作，专门为科学教育研究人员和评估人员进行集群随机试验，以解决统计资源缺乏的问题。认识到功率分析估计的大部分工作是在数学和阅读中进行的，研究小组正在开发一套专门用于科学教育研究的参数估计，以提高crt的准确性和效率。科学教育干预研究的荟萃分析结果以及对班级内相关（ICC）和协变量结果相关（R^2）数据的多层次分析为建立功率分析的估计奠定了基础。这项对ICC、R^2和效应大小（ES）进行更准确估计的努力将提高科学教育研究中crt的内部效度。通过经验性地建立对进行crt功率分析所需的全部参数的估计，研究人员和评估人员将能够找到在科学教育中规划严格的crt所需的参数的估计。通过免费的功率分析软件包访问此统计资源，增加了在研究计划中使用所有三个参数（ICC, R^2， ES）的准确估计的可能性。提高幂方程的准确性对大规模科学教育研究的规划和实施具有广泛的影响。

Recognizing the need for powerful tools to enhance studies in science education, the project is developing a set of resources to assist researchers in the planning of cluster-randomized trials of science education interventions. The goals of this full-scale project are to develop the statistical resources to design and conduct rigorous cluster-randomized trials (CRTs) in science education research; increase the accuracy of the range of parameters needed to conduct power analyses; and develop statistical resources for the science research community available through a free power analysis software package. Curriculum developers and researchers from Biological Sciences Curriculum Study and Western Michigan University team-up to address this lack of statistical resources specifically for cluster-randomized trials for science education researchers and evaluators. Recognizing that the majority of the work on power analysis estimates has been conducted in mathematics and reading, the research team is developing of a set of estimates of parameters specifically for science education research that increases the accuracy and improves the efficiency of CRTs. Results from the meta-analysis of research on science education interventions and a multi-level analysis of intra-class correlations (ICC) and covariate outcome correlations (R^2) data provide a foundation for establishing estimates for power analysis. This effort to develop more accurate estimates of ICC, R^2, and effect size (ES) will improve the internal validity of CRTs in science education research. By empirically establishing estimates for the full range of parameters needed to conduct a power analysis for CRTs, researchers and evaluators will be able to find estimates of the parameters necessary to plan rigorous CRTs in science education. Access to this statistical resource through a free power analysis software package, increases the likelihood that accurate estimates of all three parameters (ICC, R^2, ES) will be used in research planning. Improving the accuracy of power equations has broad impact on the planning and conducting of large scale science education research.