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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.
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 ...
In social science research, snowball sampling is a similar technique, where existing study subjects are used to recruit more subjects into the sample. Some variants of snowball sampling, such as respondent driven sampling, allow calculation of selection probabilities and are probability sampling methods under certain conditions.
The snowball and the avalanche can be great, ... The survey was administered using a nonprobability-based sample, and quotas were used to ensure the sample base represented the overall population ...
Sample mean and covariance – redirects to Sample mean and sample covariance; Sample mean and sample covariance; Sample maximum and minimum; Sample size determination; Sample space; Sample (statistics) Sample-continuous process; Sampling (statistics) Simple random sampling; Snowball sampling; Systematic sampling; Stratified sampling; Cluster ...
In social science research, snowball sampling, or "snowballing": a technique for developing a research sample; In researching a field, snowballing is another name for Pearl growing; In chemical and industrial engineering, snowballing is the second and last phase, after aggregation, of the pelletization process.
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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.