  Snowball sampling is the common name for sampling designs on human
populations where respondents are requested to share the questionnaire among
their social ties. With some exceptions, estimates from snowball samplings are
considered biased. However, the magnitude of the bias is influenced by a
combination of elements of the sampling design and features of the target
population. Hybrid Probabilistic-Snowball Sampling Designs (HPSSD) aims to
reduce the main source of bias in the snowball sample through randomly
oversampling the first stage 0 of the snowball.
  To check the behaviour of HPSSD for applications, we developed an algorithm
that, by grafting the edges of a stochastic blockmodel into a graph of cliques,
simulates an assortative network of tobacco smokers. Different outcomes of the
HPSSD operations are simulated, too.
  Inference on 8,000 runs of the simulation leads to think that HPSSD does not
improve reliability of samples that are already representative. But if
homophily in the population is sufficiently low, even the unadjusted sample
mean of HPSSD has a slightly better performance than a random, but undersized,
sampling.
  De-biasing the estimates of HPSSD shows improvement in the performance, so an
adjusted HPSSD estimator is a desirable development.
