Necessary Condition Analysis (NCA): Logic and Methodology of “Necessary but Not Sufficient” Causality
Theoretical “necessary but not sufficient” statements are common in the organizational sciences. Traditional data analyses approaches (e.g., correlation or multiple regression) are not appropriate for testing or inducing such statements. This article proposes necessary condition analysis (NCA) as a general and straightforward methodology for identifying necessary conditions in data sets. The article presents the logic and methodology of necessary but not sufficient contributions of organizational determinants (e.g., events, characteristics, resources, efforts) to a desired outcome (e.g., good performance). A necessary determinant must be present for achieving an outcome, but its presence is not sufficient to obtain that outcome. Without the necessary condition, there is guaranteed failure, which cannot be compensated by other determinants of the outcome. This logic and its related methodology are fundamentally different from the traditional sufficiency-based logic and methodology. Practical recommendations and free software are offered to support researchers to apply NCA.
|Keywords||data analysis, multi-causality, necessity, software, sufficiency|
|Persistent URL||dx.doi.org/10.1177/1094428115584005, hdl.handle.net/1765/90024|
|Series||ERIM Top-Core Articles|
|Journal||Organizational Research Methods|
Dul, J. (2016). Necessary Condition Analysis (NCA): Logic and Methodology of “Necessary but Not Sufficient” Causality. Organizational Research Methods, 19(1), 10–52. doi:10.1177/1094428115584005