Although there are various indices available for calculating morphological integration, the integration coefficient of variation (ICV) is most suited for assessing magnitudes of integration within and between morphological variance/covariance (V/CV) matrices. However, it is currently not known what the effects of varying sample sizes are on the reliable estimation of distributions of ICV scores. In this regard, the effects of varying sample size on ICV was examined by simulating parameter V/CV matrices with varying underlying magnitudes of average trait correlation (r(2)). ICV distributions were generated using a trait resampling protocol for various sample sizes (11 through 150) within various parameter r(2)values. Next, empirical r(2)values were calculated based on data from 22 skeletal elements of 40Macaca fascicularisspecimens to examine whether the results from the simulation corresponded to real biological data. Mean ICV scores of various sample sizes were compared using Mann-Whitney U tests to examine which minimum sample sizes are required to reliably calculate mean ICV. Mann-Whitney U test results based on the simulated data showed that a sample size of 51 may be sufficient even for relatively low r(2)values of 0.05. The empirical macaque data showed that 30-40 individuals may be sufficient to reliably calculate mean ICV scores across skeletal elements. Our results correspond closely with previous assessments by Cheverud and colleagues that argued that a sample size of 40 is necessary to accurately estimate the structure of V/CV matrices.
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