Explore the critical role of calibration in fuzzy-set Qualitative Comparative Analysis (fsQCA) to understand how raw data transforms into meaningful insights. This video delves into the nuances of using Normalized General Continuous Linguistic Variables (NGCLV) to achieve high consistency and coverage in social research. Discover how the NGCLV's single parameter reshapes data interpretation, offering clear, actionable rules. With examples from a dataset of twenty countries, see how different calibration methods impact the clarity and utility of fsQCA findings. Source: Measuring social emergencies through fuzzy qualitative comparative analysis: a calibration framework using normalized general continuous linguistic variables — Noel Batista Hernandez, Maikel Yelandi Leyva Vazquez, Anabel Mariela Abarca Cruz, Lissette Amelia Alvarado Ajila. frontiersin.org. https://doi.org/10.3389/fsoc.2026.1824793 Chapters 00:00 A Perfect Score That Covers Almost Nothing 00:42 Where Numbers Become Meanings 02:07 One Curve, One Parameter, Three Words 03:40 Letting the Data Choose the Shape 04:14 Honest Limits #fsQCA #SocialResearch #DataAnalysis
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Basada en: Measuring social emergencies through fuzzy qualitative comparative analysis: a calibration framework using normalized general continuous linguistic variables — Noel Batista Hernandez, Maikel Yelandi Leyva Vazquez, Anabel Mariela Abarca Cruz, Lissette Amelia Alvarado Ajila. Una publicación de frontiersin.org. DOI 10.3389/fsoc.2026.1824793.