Tool for researchers and data analysts:
NCA is an approach and tool for researchers who wish to build or test theory with empirical data and write impactful publications, and for data analysts in general
- To gain new substantive insights by expressing a phenomena in terms of necessity rather than probability.
- To contribute to an academic field by expressing these new insights with academic rigor and practical relevance.
- To apply a method that does not have the complexities of current regression based methods (e.g., endogeneity, multicollinearity) because the necessary condition operates in isolation from other variables (i.e., it is always present in the successful outcome)
- To give a clear practical meaning to the results, because identified necessary condition must always be put and kept in place in practice;
Complementing current methods:
NCA can be used as a separate technique, or in combination with existing techniques. Researchers who use regression-bases analyses (e.g., multiple regression, structural equation modelling) can complement their analyses with NCA, to better explain the (lack of) outcome of interest. Researchers who use configurational analyses (e.g., fsQCA) can complement their analyses with NCA, to identify necessary conditions with more detail. NCA can be used for any research design. Case study researchers can use NCA for identifying a necessary condition as a common characteristic of successful cases (cases where the outcome is present or high). Researchers who use experiments can consider to complement a sufficiency experiment (adding a condition in cases without the outcome and checking whether the outcome increases) with a necessity experiment (removing a condition in cases with the outcome, and checking whether the maximum outcome decreases). Approaches for sampling and measurement are not different for NCA than for data analysis methods.