To familiarize the participants with Indirect Sampling and the Generalised Weight Share Method; apply these methods for surveying difficult to reach populations.
Benefits to participant
Participants will benefit from a thorough description of Indirect Sampling, together with its related weighting method: the Generalised Weight Share Method. The content is of current interest: we are more and more interested in producing statistics for populations for which there is no sampling frame, or where the development of a frame would be too expensive.
The emphasis will be put on Indirect sampling, which is a generalisation of well-known sampling methods for populations difficult to reach: Network Sampling, Adaptive Cluster Sampling and Snowball Sampling.
The course will involve the study of real problems to solve, in order to facilitate the understanding of the basic notions. Thus, by having discussions with the participants, and with the professor as a moderator (and a motivator!), the basic notions will become clearer for solving real needs in sampling. For the most complicated notions, teaching will be done in a classical way, with references to current surveys.
Employees who develop and implement complex sampling plans for surveying populations difficult to reach, either for the social or business sectors.
Indirect Sampling, The Generalised Weight Share Method (GWSM), Properties of the GWSM, Other generalisations of the GWSM, Fair Share Method, Ernst's (1989) contribution, Network Sampling, Adaptive Cluster Sampling, ‘Snowball’ Sampling
Advanced knowledge of mathematical statistics and basic knowledge in sampling theory.
STC0413 Statistical Sampling Theory