How to sample in necessary condition analysis (NCA)

Necessary Condition Analysis (NCA) is a novel method that gained popularity in international business and management research in recent years. It examines cause-effect relationships in terms of necessity, where X is necessary for Y, expressed as 'if not X then not Y' in nearly all cases. This stands in contrast to conventional probabilistic causality which suggests 'if X then probably Y' in a group of cases. NCA accepts two sampling approaches: purposive sampling frequently employed in qualitative research, and probability sampling, commonly used (or assumed) in quantitative research. With dichotomous variables, purposive sampling of a small number of cases showing the outcome, can identify a necessary condition. To identify a necessary condition in a population, probability sampling and NCA's statistical test for estimating the p-value can be used. This allows conducting NCA's statistical power test to estimate the minimum required sample size for identifying a necessary condition when it exists.