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Title: How to sample in necessary condition analysis (NCA)

Authors: Jan Dul

Addresses: Rotterdam School of Management, Erasmus University Rotterdam, Rotterdam, the Netherlands

Abstract: 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.

Keywords: NCA; necessary condition analysis; purposive sampling; probability sampling; statistical power; case selection; sample size; qualitative research; quantitative research.

DOI: 10.1504/EJIM.2024.138446

European Journal of International Management, 2024 Vol.23 No.1, pp.1 - 12

Received: 06 Nov 2023
Accepted: 31 Jan 2024
Published online: 03 May 2024 *