  The effects of noise and sampling on the ``Spectral Correlation Function''
(SCF) introduced by Rosolowsky et al. 1999 are studied using observational
data, numerical simulations of magneto-hydrodynamic turbulence, and simple
models of Gaussian spectral line profiles. The most significant innovations of
this paper are: i) the normalization of the SCF based on an analytic model for
the effect of noise; ii) the computation of the SCF as a function of the
spatial lag between spectra within a map. By computing of the dependence of the
SCF on the spatial lag, S_o(dr), we have been able to conclude that: 1) S_o(dr)
is a power law, with slope in the range of scales l_i-l_o. 2) The correlation
outer scale, l_o, is determined by the size of the map, and no evidence for a
true departure from self-similarity on large scales has been found. 3) The
correlation inner scale, l_i, is a true estimate of the smallest self-similar
scale in a map. 4) The spectral slope in a given region, is independent of
velocity resolution (above a minimum resolution threshold), spatial resolution,
and average spectrum quality. 5) Molecular transitions which trace higher gas
density yield larger values of the spectral slope than transitions tracing
lower gas density. 6) Nyquist sampling, bad pixels in detector arrays, and
reference sharing data acquisition need to be taken into account for a correct
determination of the SCF at lag dr=1. The value of the spectral slope, however,
can be computed correctly without a detailed knowledge of observational
procedures.
