A Statistical Significance Test for Necessary Condition Analysis

By Jan Dul, E. van der Laan, Roelof Kuik. 2020

552 citations recorded by the source corpus.

In this article, we present a statistical significance test for necessary conditions. This is an elaboration of necessary condition analysis (NCA), which is a data analysis approach that estimates the necessity effect size of a condition X for an outcome Y. NCA puts a ceiling on the data, representing the level of X that is necessary (but not sufficient) for a given level of Y. The empty space above the ceiling relative to the total empirical space characterizes the necessity effect size. We propose a statistical significance test that evaluates the evidence against the null hypothesis of an effect being due to chance. Such a randomness test helps protect researchers from making Type 1 errors and drawing false positive conclusions. The test is an “approximate permutation test.” The test is available in NCA software for R. We provide suggestions for further statistical development of NCA.

Subfields: Artificial Intelligence, Food Science, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/45d97737e232081a76dbfc95fce62eb305d9face

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In Sociology and Political Science

1. R. G. Swab
2. Erik T. Markin
3. Clay Dibrell
4. Ways Forward
5. Chelsea Sherlock

Citations in the corpus

• “SEW” What Do We Know and Where Do We Go? A Review of Socioemotional Wealth and a Way Forward (2020, background): “…analysis are constrained by necessary condition(s), future studies may wish to apply statistical significance tests on the necessary conditions (e.g., Dul et al., 2020) outlined in our discussion to explore the extent of the constraint family control and renewal of family bonds pose on SEW.”