  We have studied the galaxy-group cross-correlations in redshift space for the
Galaxy And Mass Assembly (GAMA) Survey. We use a set of mock GAMA galaxy and
group catalogues to develop and test a novel 'halo streaming' model for
redshift-space distortions. This treats 2-halo correlations via the streaming
model, plus an empirical 1-halo term derived from the mocks, allowing accurate
modelling into the nonlinear regime. In order to probe the robustness of the
growth rate inferred from redshift-space distortions, we divide galaxies by
colour, and divide groups according to their total stellar mass, calibrated to
total mass via gravitational lensing. We fit our model to correlation data, to
obtain estimates of the perturbation growth rate, $f\sigma_8$, validating
parameter errors via the dispersion between different mock realizations. In
both mocks and real data, we demonstrate that the results are closely
consistent between different subsets of the group and galaxy populations,
considering the use of correlation data down to some minimum projected radius,
$r_{\rm min}$. For the mock data, we can use the halo streaming model to below
$r_{\rm min} = 5h^{-1}$ Mpc, finding that all subsets yield growth rates within
about 3% of each other, and consistent with the true value. For the actual GAMA
data, the results are limited by cosmic variance: $f\sigma_8=0.29\pm 0.10$ at
an effective redshift of 0.20; but there is every reason to expect that this
method will yield precise constraints from larger datasets of the same type,
such as the DESI bright galaxy survey.
