6533b7d5fe1ef96bd1263ee5
RESEARCH PRODUCT
Predicting the Significance of Necessity
Kimmo SorjonenBo Melinsubject
Populationlcsh:BF1-990significancepermutation050105 experimental psychologyPlot (graphics)power03 medical and health sciencesPermutation0302 clinical medicineStatistical significanceSignificance testingStatisticsPsychology0501 psychology and cognitive scienceseducationGeneral Psychologyalternative hypothesissignificance testingeducation.field_of_studytype I errorGeneral Commentary05 social sciencesNCAp-valuenull hypothesis testingsimulationOutcome (probability)lcsh:PsychologySample size determinationPerspectivesufficiencynecessary condition analysisPsychology030217 neurology & neurosurgerydescription
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.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2019-02-01 | Frontiers in Psychology |