  The coefficient of variation (CV) is commonly used to measure relative
dispersion. However, since it is based on the sample mean and standard
deviation, outliers can adversely affect the CV. Additionally, for skewed
distributions the mean and standard deviation do not have natural
interpretations and, consequently, neither does the CV. Here we investigate the
extent to which quantile-based measures of relative dispersion can provide
appropriate summary information as an alternative to the CV. In particular, we
investigate two measures, the first being the interquartile range (in lieu of
the standard deviation), divided by the median (in lieu of the mean), and the
second being the median absolute deviation (MAD), divided by the median, as
robust estimators of relative dispersion. In addition to comparing the
influence functions of the competing estimators and their asymptotic biases and
variances, we compare interval estimators using simulation studies to assess
coverage.
