The project is supported through the EHR Core Research: Building Capacity in STEM Education Research (ECR: BCSER) competition that is designed to build individual capacity to carry out high quality fundamental STEM education research in STEM learning and learning environments, broadening participation in STEM fields, and STEM workforce development.
The goal of the Modern Meta-Analysis Research Institute (MMARI) is to improve the quality of meta-analyses conducted in STEM education by increasing the capacity of STEM researchers to conduct high quality meta-analyses. The training will enable graduate students and early career scholars to broaden their expertise and skills to conduct rigorous research on STEM. The training Institute will focus on the recruitment of scholars from underrepresented minorities in STEM. Participants will learn to conduct a high-quality meta-analysis, developing the requisite background to continue to grow their knowledge as new methods are developed. Increasing the capacity of the field to conduct high quality meta-analyses is essential to foster new and more effective interventions in STEM education through understanding what interventions work for whom and under what conditions.
The 5-day workshop will provide STEM researchers with a comprehensive, introductory meta-analysis Research Institute focused on state-of-the-art methods including use of the program R. This Research Institute is specifically targeted to early-career STEM education researchers with no previous experience with meta-analysis. Unlike other introductory workshops, the content of this workshop will provide data analysis skills in R to implement best-practice statistical methods. At the conclusion of the workshop, participants should be able to: use R for meta- analysis; understand differences between effect sizes and compute effect sizes from the most common types of data reported in studies; specify an appropriate meta-analysis model; estimate and report both an average effect size and the extent of variation in effect sizes; explore and interpret heterogeneity of effect sizes using meta-regression models, and conduct appropriate publication bias analyses and interpret the effect of possible bias on findings.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
