Home Events Addressing Small Sample Sizes in NSF S‑STEM Evaluations

Addressing Small Sample Sizes in NSF S‑STEM Evaluations

Are you a PI, Co-PI, evaluator, or STEM researcher working with small cohort samples of Scholars in your NSF S-STEM programs? These sessions are for you!

Join us for a practical, two-part webinar series:

Part I: August 26 at 3 p.m. ET

Part II: September 3 at 3:30 p.m. ET

These sessions are designed to help you better prepare, analyze, and interpret your data using Hedges’ g — a powerful statistical tool for small sample contexts.

Led by Daniel Bernal, Senior Associate at MN Associates, Inc., this series will show you how to move beyond traditional data analysis methods to uncover credible, measurable impacts in your Track 1, Track 2, and even Track 3 S-STEM programs.

Part I of the webinar series, “Understanding Small Sample Challenges and Introduction to Hedges’ g,” introduces the statistical method designed for small sample contexts.

Part II of the series, “Applying Hedges’ g in Practice with Practice Data Sets,” focuses on the practical application of Hedges’ g within NSF S‑STEM evaluations.

Participants will learn how to implement this technique effectively and interpret results in ways that highlight program impacts using practice data sets.

Speakers

Headshot of Daniel Bernal.

Daniel Bernal

Senior Associate, MN Associates, Inc.

Daniel Bernal is a Senior Associate at MN Associates, Inc., specializing in data analysis, evaluation design, and statistical methods tailored for education and workforce development programs. He holds a B.A. in Mathematics from Carleton College, a M.Ed. in Learning, Design & Technology from Saint Mary’s University of Minnesota, and a Certification in Data Analytics & Visualization from the University of Minnesota – Twin Cities. Daniel’s expertise spans quantitative and qualitative analysis, survey design, and data visualization, with a focus on making evaluation findings accessible and meaningful to stakeholders. He has worked with various STEM programs, guiding them to adopt rigorous yet practical approaches to evaluation, particularly when dealing with small sample sizes and limited data sets.

FAQs

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