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Hidden populations are hard to access due to secrecy. Respondent driven sampling (RDS) happens to be a promising technique since it consists of probabilistic sampling.RDS enables to produce credible estimations than using non-probabilistic techniques. Yet the method consumes a considerable amount of resources which restricts the researchers to use RDS in its full potential. To better understanding of the usage of the package refer to Perera, A., & Ramanayake, A. (2019). Assessing the effects of respondent driven sampling estimators on population characteristics. Proceeding of Asia International Conference on Multidisciplinary Research
The function population could be used if the researcher
has some understanding of the population—such as the distribution of
degrees, whether certain parameters are high or low, or the relationship
between responses and other variables—they can use this package to
simulate a similar population. This allows the researcher to determine
the optimal combination of seeds, coupons, and estimators for their
study before conducting the actual empirical research.
If the researcher lacks prior knowledge about the population of interest, they can gain insight through a pilot study. The then generate populations that mirror the characteristics identified in the pilot study, helping to refine the choice of key variables and sampling parameters.
The researcher can use the specific number of seeds and coupons used during the sample collection to recreate the population. This allows for testing various estimators on the simulated population to identify the most effective one.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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