What is NCA?

Necessary Condition Analysis (NCA) is an approach and tool for identifying necessary conditions in data sets. A necessary condition is a critical factor of an outcome: if the condition is not in place the outcome will not occur. For example, a student will not be admitted to a PhD program when the GMAT score is too low, creativity will not exist without intelligence, AIDS will not develop without HIV, and organizational change will not occur without management commitment. Such single conditions can be a bottleneck for the outcome. If the necessary condition is not in place there is guaranteed failure, and this cannot be compensated by other determinants. But when the condition is in place there is no guaranteed success. In this case the condition is necessary but not sufficient. To prevent failure, each single necessary condition must be in place. NCA provides the logic and a methodological tool for finding or testing necessary conditions in existing or new data sets.

Several short videos introduce NCA (presentation by Jan Dul at Chalmers University, Sweden, recorded by Cecilia Berlin):

  1. Motivation for NCA
  2. The logic of NCA
  3. NCA in Practice
  4. NCA in Academia
  5. NCA's hypothesis testing
  6. Example of NCA in case study research
  7. NCA in quantitative research: example
  8. The difference between NCA and regression: example