  Redshift-space clustering anisotropies caused by cosmic peculiar velocities
provide a powerful probe to test the gravity theory on large scales. However,
to extract unbiased physical constraints, the clustering pattern has to be
modelled accurately, taking into account the effects of non-linear dynamics at
small scales, and properly describing the link between the selected cosmic
tracers and the underlying dark matter field. We use a large hydrodynamic
simulation to investigate how the systematic error on the linear growth rate,
$f$, caused by model uncertainties, depends on sample selections and comoving
scales. Specifically, we measure the redshift-space two-point correlation
function of mock samples of galaxies, galaxy clusters and Active Galactic
Nuclei, extracted from the Magneticum simulation, in the redshift range 0.2 < z
< 2, and adopting different sample selections. We estimate $f\sigma_8$ by
modelling both the monopole and the full two-dimensional anisotropic
clustering, using the dispersion model. We find that the systematic error on
$f\sigma_8$ depends significantly on the range of scales considered for the
fit. If the latter is kept fixed, the error depends on both redshift and sample
selection, due to the scale-dependent impact of non-linearities, if not
properly modelled. On the other hand, we show that it is possible to get
unbiased constraints on $f\sigma_8$ provided that the analysis is restricted to
a proper range of scales, that depends non trivially on the properties of the
sample. This can have a strong impact on multiple tracers analyses, and when
combining catalogues selected at different redshifts.
