How to sample in necessary condition analysis (NCA)

Jan Dul*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

2 Citations (Scopus)
7 Downloads (Pure)


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.

Original languageEnglish
Pages (from-to)1-12
Number of pages12
JournalEuropean Journal of International Management
Issue number1
Publication statusE-pub ahead of print - 3 May 2024

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