This article delves deeper into the causal perspective of Necessary Condition Analysis (NCA). In contrast to traditional probabilistic sufficiency approaches in quantitative social science research about what will happen on average in a group of cases, NCA is interested in what will not occur in almost every case if a necessary condition is absent. Rooted in David Hume’s theory of causation NCA explores factors that act as ‘must-haves’ or bottlenecks for the outcome. Operating from this necessity perspective, NCA functions deterministically (without exceptions) or non-deterministically (allowing exceptions). NCA can enrich theories and models in social science research by identifying essential factors. The article contributes to the literature by precisely describing the necessity causal perspective that is used in NCA, and by explaining how this is different from causal perspectives that are commonly used in social science research.
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Professor at George Washington University
I am a Professor with Human-Technology Collaboration and Educational Technology programs at George Washington University in Washington DC. I have written 12 books and more than 100 articles, and I co-host of the Parsing Science podcast where scientists tell the stories behind their research. I am also the developer of the WeShareScience.com online platform for sharing research videos, and SciencePods.com where researchers can create free podcasts about their science. My research interests include human interactions with intelligent machines, needs, needs assessments, and instructional design.
Latest posts by Ryan Watkins (see all)
- The Essentials of AI for Life and Society: An AI Literacy Course for the University Community - January 14, 2025
- A Novel Approach to Scalable and Automatic Topic-Controlled Question Generation in Education - January 11, 2025
- Engineering of Inquiry: The “Transformation” of Social Science through Generative AI - January 10, 2025