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The measures of central tendency calculated from a data set may not reveal much about its intrinsic distribution. If a plot is made of the data set’s values, the mean and the median may not only differ, but also the plot may have more values on one side of the central tendencies. Such a data set is said to be skewed towards that side.
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Necessity as a Function of Skewness.

Kimmo Sorjonen1, Jenny Wikström Alex1, Bo Melin1

  • 1Division of Psychology, Department of Clinical Neuroscience, Karolinska Institute, Solna, Sweden.

Frontiers in Psychology
|January 10, 2018
PubMed
Summary

Necessary Condition Analysis (NCA) estimates necessity effects by examining predictor-outcome relationships. Simulation results show that predictor and outcome skewness, along with sample size, significantly influence these effects and their confidence intervals.

Keywords:
creativityintelligencenecessitysimulationskewness

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Area of Science:

  • Social Sciences
  • Quantitative Research Methods

Background:

  • Necessary Condition Analysis (NCA) is a method to assess the necessity of a predictor (X) for an outcome (Y).
  • Estimating the necessity effect involves quantifying the 'empty space' in the upper-left quadrant of a predictor-outcome plot.

Purpose of the Study:

  • To investigate the association between predictor and outcome skewness and the estimated necessity effect in simulation studies.
  • To examine the influence of skewness and sample size on the standard error of the necessity effect.
  • To present a method for calculating confidence intervals for the necessity effect.

Main Methods:

  • A simulation study was conducted to analyze the behavior of Necessary Condition Analysis (NCA).
  • The study focused on the relationship between predictor (X) and outcome (Y) variables.
  • Statistical associations and the influence of variable skewness and sample size were evaluated.

Main Results:

  • A negative association was observed between the skewness of the predictor and the calculated necessity effect.
  • A positive association was found between the skewness of the outcome and the calculated necessity effect.
  • Skewness of both predictor and outcome, along with sample size, influenced the standard error of the necessity effect.

Conclusions:

  • The findings suggest that the skewness of predictor and outcome variables can substantially explain the observed necessity effects.
  • A method for calculating confidence intervals for the necessity effect was developed and presented.
  • The study highlights the importance of considering variable skewness in NCA.