2018
DOI: 10.1177/1094428118795272
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A Statistical Significance Test for Necessary Condition Analysis

Abstract: 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 statisti… Show more

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Cited by 272 publications
(216 citation statements)
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“…Based on Dul's recommendations (2019), 0 < d ≤ 0.1 is considered a small effect, 0.1 ≤ d ≤ 0.3 a medium effect, 0.3 ≤ d ≤ 0.5 a large effect, and d ≥ 0.5 is considered a very large effect. A significance test is then performed, by comparing the effect size of the observed sample to the effect sizes of samples where X and Y are unrelated, using an approximate permutation test (for detailed explanations, see Dul et al, 2020).…”
Section: Necessary Condition Analysismentioning
confidence: 99%
“…Based on Dul's recommendations (2019), 0 < d ≤ 0.1 is considered a small effect, 0.1 ≤ d ≤ 0.3 a medium effect, 0.3 ≤ d ≤ 0.5 a large effect, and d ≥ 0.5 is considered a very large effect. A significance test is then performed, by comparing the effect size of the observed sample to the effect sizes of samples where X and Y are unrelated, using an approximate permutation test (for detailed explanations, see Dul et al, 2020).…”
Section: Necessary Condition Analysismentioning
confidence: 99%
“…We reply to Sorjonen and Melin (2019) article “Predicting the significance of necessity” that is a comment on a recently proposed statistical test for Necessary Condition Analysis (Dul et al, in press). Necessary Condition Analysis (NCA) is a method that draws a ceiling line on top of the data in an XY scatter plot (Dul, 2016).…”
mentioning
confidence: 93%
“…It is a permutation test 2 that produces an estimate of the p -value and “…is intended to answer the question: ‘Can the observed effect size be the result of random chance?' by responding: ‘Yes, but with probability smaller than p .”' (Dul et al, in press, p. 2). Dul et al (in press) show by simulations and by referring to a mathematical proof that the test is valid for identifying randomness, hence for helping researchers to avoid type I error (rejecting the null hypothesis when the null is true).…”
mentioning
confidence: 98%
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“…Thus, we first elaborate the particularities of sufficiency and necessity logics. Then, we suggest using a new approach and analysis technique, that is, necessary condition analysis (NCA; Dul, 2016; Dul, van der Laan, & Kuik, 2018), which is able to identify necessary conditions in empirical data. We will demonstrate the differences between the sufficiency and necessity logics with an illustrative example based on data on the relationship between high‐performance work practices (HPWPs; Appelbaum, Bailey, Berg, & Kalleberg, 2000) and employee performance.…”
Section: Introductionmentioning
confidence: 99%