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Judgment sampling or purposive sampling, where the researcher chooses the sample based on who they think would be appropriate for the study. This is used primarily when there is a limited number of people that have expertise in the area being researched, or when the interest of the research is on a specific field or a small group.
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.
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.
Citation-based plagiarism detection (CbPD) [36] relies on citation analysis, and is the only approach to plagiarism detection that does not rely on the textual similarity. [37] CbPD examines the citation and reference information in texts to identify similar patterns in the citation sequences. As such, this approach is suitable for scientific ...
This page was last edited on 28 April 2009, at 20:53 (UTC).; Text is available under the Creative Commons Attribution-ShareAlike 4.0 License; additional terms may ...
A key result in Efron's seminal paper that introduced the bootstrap [4] is the favorable performance of bootstrap methods using sampling with replacement compared to prior methods like the jackknife that sample without replacement. However, since its introduction, numerous variants on the bootstrap have been proposed, including methods that ...
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.
In one-dimensional systematic sampling, progression through the list is treated circularly, with a return to the top once the list ends. The sampling starts by selecting an element from the list at random and then every k th element in the frame is selected, where k, is the sampling interval (sometimes known as the skip): this is calculated as: [3]
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