A promising methodology: Fuzzy set Qualitative Comparative Analysis for identifying Roads (less) Taken to STEM involvement

Abstract Information: In our evaluation world, as we seek to meet the demand for valid and reliable findings, diverse voices and perspectives can easily be lost in statistical analyses. For Roads Taken, an NSF funded follow-up study of intensive STEM youth programming that occurred 15 to 25 years ago for youth from under-represented populations (NSF #1906396), we adopted fuzzy set Qualitative Comparative Analysis (fsQCA) to identify pathways less traveled—to STEM careers, long-term STEM identity, Community-based STEM involvement, and Personal STEM involvement. In this demonstration session, evaluators will learn how fsQCA steps were applied to understanding pathways to long-term STEM outcomes. We include an overview of fsQCA, conceptual model development, condition calibration, truth table construction, definitions of consistency and coverage of necessary and sufficient conditions, meaning making, and reporting. Participants will gain appreciation for how fsQCA can merge quantitative and qualitative data; processes for determining necessary and sufficient conditions that lead to an outcome, and how the methodology can be helpful for determining causal pathways from small datasets. Evaluators interested in mixed methods will discover or learn more about this methodology's use for merging quantitative and qualitative data; evaluators interested in evaluating programs for their influence on pathways to STEM involvement will gain greater access into a useful new methodology. Whatever their entry into QCA, audience members will leave with a practical understanding of steps related to specifying models, creating, and calibrating conditions, and extracting narratives representing multiple perspectives.