% The function compares the magnitude of correlations between a covariate "c" and
% the dependent variable "d" across the experimental conditions A and B.
% The difference between the two bootstrapped ("k" cycles) z-distributions of correlations in
% the conditions A and B is estimated as the effect size (Cohen's d).
% Input data should be the dependent variable (e.g., brain activity in a certain
% region, for each subject) and the associated covariate (e.g., reaction times
% during the experiment, for each subject).
% Suggestion: implement different k to ensure a costant value of es.
% INPUTS:
% dA, dB -> Dependent variable (column vectors)
% cA, cB -> Associated covariate (column vectors)
% OPTIONAL: k -> Number of cycles (1000 by default)
% OPTIONAL: do_fig -> Want the figure? 1=yes, 0=no=default
%
% OUTPUTS
% es -> effect size
% IF YOU USE THIS SCRIPT YOU MAY WANT TO LOOK AT / CITE:
% Di Plinio, S (2022). Testing the magnitude of correlations across experimental conditions. Frontiers in Psychology, 13:860213. https://doi.org/10.3389/fpsyg.2022.860213
인용 양식
Di Plinio, S (2022). Testing the magnitude of correlations across experimental conditions. Frontiers in Psychology, 13:860213. https://doi.org/10.3389/fpsyg.2022.860213
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