Abstract
With Necessary Condition Analysis (NCA), a necessity effect is estimated by calculating the amount of empty space in the upper-left corner in a plot with a predictor X and an outcome Y, and recently a method for testing the statistical significance of the necessity effect through permutation has been proposed. In the present simulation study, this method was found to give significant results already with a very weak true population necessity effect, i.e., exhibit high power, unless the sample size is very small. However, in some situations the significance of the necessity effect tends to increase with increased degree of sufficiency, which is paradoxical for a method whose objective is to find necessary but not sufficient conditions.
Highlights
Edited by: Enrique Bigne, University of Valencia, SpainReviewed by: Paul T
Different functions can be applied to so called ceiling points, but in the present study we will stick to a stepfunction named Ceiling Envelopment-Free Disposal Hull (CE-FDH), which has been proposed as the default technique for Necessary Condition Analysis (NCA)
We have shown previously that with a negatively skewed predictor X and a positively skewed outcome Y, NCA can give large necessity effects even if X and Y are unrelated
Summary
Edited by: Enrique Bigne, University of Valencia, SpainReviewed by: Paul T. Necessary Condition Analysis (NCA) has been developed to help researchers identify necessary but not sufficient conditions for an outcome Y of interest (Dul, 2016). The calculated necessity effect corresponds to the amount (percentage) of empty space in the upper-left corner when plotting outcome Y against a predictor X (Figure 1). CE-FDH values below 0.1 has been described as small, values between 0.1 and 0.3 as medium, values between 0.3 and 0.5 as large, and values above 0.5 as very large necessity effects (Dul, 2016).
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