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In sociology and statistics research, snowball sampling [1] (or chain sampling, chain-referral sampling, referral sampling [2] [3]) is a nonprobability sampling technique where existing study subjects recruit future subjects from among their acquaintances. Thus the sample group is said to grow like a rolling snowball.
Convenience sampling can be used by almost anyone and has been around for generations. One of the reasons that it is most often used is due to the numerous advantages it provides. This method is extremely speedy, easy, readily available, and cost-effective, causing it to be an attractive option to most researchers.
Nonprobability sampling methods include convenience sampling, quota sampling, and purposive sampling. In addition, nonresponse effects may turn any probability design into a nonprobability design if the characteristics of nonresponse are not well understood, since nonresponse effectively modifies each element's probability of being sampled.
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Snowball sampling, involving the first respondent referring an acquaintance, and so on. Such samples are biased because they give people with more social connections an unknown but higher chance of selection, [10] but lead to higher response rates. Judgment sampling or purposive sampling, where the researcher chooses the sample based on who ...
This method can be subjected to researcher's biases and perception. [14] Snowball Samples: Often used when a target population is rare. Members of the target population recruit other members of the population for the survey. Quota Samples: The sample is designed to include a designated number of people with certain specified characteristics ...
The Debt Snowball Method, first popularized by personal finance expert Dave Ramsey, is one of these strategies. Find out if the Debt Snowball Method is the way forward for you.
This category is for techniques for statistical sampling from real-world populations, used in observational studies and surveys. For techniques for sampling random numbers from desired probability distributions, see category:Monte Carlo methods.