  The network scale-up method enables researchers to estimate the size of
hidden populations, such as drug injectors and sex workers, using sampled
social network data. The basic scale-up estimator offers advantages over other
size estimation techniques, but it depends on problematic modeling assumptions.
We propose a new generalized scale-up estimator that can be used in settings
with non-random social mixing and imperfect awareness about membership in the
hidden population. Further, the new estimator can be used when data are
collected via complex sample designs and from incomplete sampling frames.
However, the generalized scale-up estimator also requires data from two
samples: one from the frame population and one from the hidden population. In
some situations these data from the hidden population can be collected by
adding a small number of questions to already planned studies. For other
situations, we develop interpretable adjustment factors that can be applied to
the basic scale-up estimator. We conclude with practical recommendations for
the design and analysis of future studies.
