'Within-subjects association' is a tendency for a subject's personal highs in a variable Y to be associated with personal highs (or lows) in a variable X. We show that the within-subject Spearman correlations, individually of little reliability, can be aggregated across subjects in a repeated measures data set to effectively detect such an association. Observed significance levels can be assigned using an approximate randomization procedure even in small samples with variable numbers of observations per subject and ties in the ranks. We investigate the power of this method under the assumptions that the ranks within subjects follow a distribution proposed by Henze. On the basis of these results, we make recommendations for sample designs.