What are the necessary conditions of the phenomenon (outputs / outcome condition) you are studying in your research? Necessary Condition Analysis (NCA) helps exploring cause-effect relations in terms of "necessary but not sufficient". In the absence of the right level of the condition a certain effect cannot occur. This is independent of other causes, thus the necessary condition can be a bottleneck, critical factor, constraint, disqualifier and so on. In practice, in the absence of the right level of the necessary condition failure is guaranteed. Other causes cannot compensate for necessary conditions.
NCA is applicable to various disciplines, and can provide insightful results even when other analyses such as regression analysis show no or weak effects. By introducing a different logic and data analysis approach, NCA adds both rigor and relevance to theory, data analysis, and publications. NCA is a user-friendly method that requires no advanced statistical or methodological knowledge beforehand. It can be used in both quantitative research as well as in qualitative research.