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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.
Stratified purposive sampling is a type of typical case sampling, and is used to get a sample of cases that are "average", "above average", and "below average" on a particular variable; this approach generates three strata, or levels, each of which is relatively homogeneous, or alike. [1]
A visual representation of the sampling process. In statistics, quality assurance, and survey methodology, sampling is the selection of a subset or a statistical sample (termed sample for short) of individuals from within a statistical population to estimate characteristics of the whole population. The subset is meant to reflect the whole ...
According to Sandelowski in 1995, although theoretical sampling is often misconstrued as purposive sampling, the uses of theoretical sampling vary to a large extent. Also, the selection criteria of participants for theoretical sampling changes according to the needs and changes that occur in the theoretical study at the given time.
Any criteria unless the condition or intervention is specific to the criterion, or the criterion has a direct bearing on condition/intervention/results. Strongly Justified Reasons for Exclusion: [1] Unable to provide informed consent; Placebo or intervention would be harmful; Lack of equipoise (intervention harmful)
The sample data is a random sampling from a fixed distribution or population where every collection of members of the population of the given sample size has an equal probability of selection. Variants of the test have been developed for complex samples, such as where the data is weighted. Other forms can be used such as purposive sampling. [8]
Proportionate allocation uses a sampling fraction in each of the strata that are proportional to that of the total population. For instance, if the population consists of n total individuals, m of which are male and f female (and where m + f = n), then the relative size of the two samples (x 1 = m/n males, x 2 = f/n females) should reflect this proportion.
In the design of experiments, consecutive sampling, also known as total enumerative sampling, [1] is a sampling technique in which every subject meeting the criteria of inclusion is selected until the required sample size is achieved. [2]