  The Rousseeuw-Croux $S_n$, $Q_n$ scale estimators and the median absolute
deviation $\operatorname{MAD}_n$ can be used as consistent estimators for the
standard deviation under normality. All of them are highly robust: the
breakdown point of all three estimators is $50\%$. However, $S_n$ and $Q_n$ are
much more efficient than\ $\operatorname{MAD}_n$: their asymptotic Gaussian
efficiency values are $58\%$ and $82\%$ respectively compared to $37\%$ for\
$\operatorname{MAD}_n$. Although these values look impressive, they are only
asymptotic values. The actual Gaussian efficiency of $S_n$ and $Q_n$ for small
sample sizes is noticeable lower than in the asymptotic case.
  The original work by Rousseeuw and Croux (1993) provides only rough
approximations of the finite-sample bias-correction factors for $S_n$, $Q_n$
and brief notes on their finite-sample efficiency values. In this paper, we
perform extensive Monte-Carlo simulations in order to obtain refined values of
the finite-sample properties of the Rousseeuw-Croux scale estimators. We
present accurate values of the bias-correction factors and Gaussian efficiency
for small samples ($n \leq 100$) and prediction equations for samples of larger
sizes.
