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Thread: Correlations computed within subject vs collapsing across subjects

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    Correlations computed within subject vs collapsing across subjects

    Hi everyone,

    I have model predictions (x) that I want to test against subject ratings (y). There are 20 stimuli per subject and I have data from 30 subjects. What I did initially was to collapse all (x,y) pairs of values and compute the Pearson correlation for the 30x20=600-element vectors x and y.

    However, given the variance in the ratings is lower within- than between-subjects, I am wondering whether I should in fact first be computing the model-rating correlations subject-wise, and then report the confidence interval corresponding to the distribution of subject-wise correlation coefficients?

    Possibly relevant is the fact that both x and y have discrete values, with 20 and 7 values respectively, i.e. 20 model predictions for the stimuli, and a 7-step rating scale (see scatter plot below).

    I am of course open to suggestions in case a Pearson correlation is in fact not the most appopriate measure of assessing the model's prediction accuracy across subjects.

    Many thanks for any help.
    Last edited by wildetudor; 07-02-2016 at 05:39 AM.

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