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Predicting the Significance of Necessity
1Karolinska Institute, Solna, Sweden.
Frontiers in Psychology
|March 1, 2019
Summary
Necessary Condition Analysis (NCA) permutation testing shows high power for detecting necessity effects, even with weak true effects. However, it can be paradoxically influenced by sufficiency effects, especially with small sample sizes.
Area of Science:
- Social Sciences
- Management Science
- Psychology
Background:
- Necessary Condition Analysis (NCA) quantifies necessity effects by measuring empty space in predictor-outcome plots.
- A recent permutation-based method aims to test the statistical significance of these necessity effects.
Purpose of the Study:
- To evaluate the statistical significance testing method for necessity effects in NCA through a simulation study.
- To assess the power and potential biases of the permutation test, particularly concerning sufficiency effects.
Main Methods:
- Conducted a simulation study to assess the performance of the permutation-based significance test for NCA.
- Varied sample sizes and the strength of true population necessity and sufficiency effects.
Main Results:
- The permutation test demonstrated high statistical power, detecting weak necessity effects effectively, except in very small sample sizes.
- A paradoxical finding emerged: significance levels sometimes increased with greater sufficiency, contrary to NCA's goal.
Conclusions:
- The permutation test for NCA is generally powerful but requires careful interpretation due to potential inflation by sufficiency.
- Researchers should be cautious about interpreting significance, especially when sufficiency is high and sample sizes are small.
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