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.
