Date of publication: 2017-08-26 12:06
Suppose you wanted to study dance club and bar employees in NYC with a sample of n = 655. Yet there is no list of these employees from which to draw a simple random sample. Suppose you obtained a list of all bars/clubs in NYC. One way to get this would be to randomly sample 855 bars and then randomly sample 7 employees within each bars/club. This is an example of cluster sampling. Here the unit of analysis (employee) is different from the primary sampling unit (the bar/club).
Thanks to Eric Mitchell, Chris Rovner, Vlad Tarko, Gordon Worley, and Gregg for catching errors in the text. Eliezer Yudkowsky's work is supported by the Machine Intelligence Research Institute. If you've found Yudkowsky's pages on rationality useful, please consider donating to the Machine Intelligence Research Institute.
Purposive sampling is a sampling method in which elements are chosen based on purpose of the study. Purposive sampling may involve studying the entire population of some limited group (sociology faculty at Columbia) or a subset of a population (Columbia faculty who have won Nobel Prizes). As with other non-probability sampling methods, purposive sampling does not produce a sample that is representative of a larger population, but it can be exactly what is needed in some cases - study of organization, community, or some other clearly defined and relatively limited group.
Snowball sampling is a method in which a researcher identifies one member of some population of interest, speaks to him/her, then asks that person to identify others in the population that the researcher might speak to. This person is then asked to refer the researcher to yet another person, and so on.
The most widely known type of a random sample is the simple random sample (SRS). This is characterized by the fact that the probability of selection is the same for every case in the population. Simple random sampling is a method of selecting n units from a population of size N such that every possible sample of size an has equal chance of being drawn.
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Rather than taking a simple random sample from the firm's population at large, in a stratified sampling design, we ensure that appropriate numbers of elements are drawn from each racial group in proportion to the percentage of the population as a whole. Say we want a sample of 6555 employees - we would stratify the sample by race (group of White employees, group of African American employees, etc.), then randomly draw out 755 employees from the White group, 95 from the African American, 655 from the Asian, and 65 from the Latino. This yields a sample that is proportionately representative of the firm as a whole.
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More generally, suppose that the N units in the population are ranked 6 to N in some order (., alphabetic). To select a sample of n units, we take a unit at random, from the 6st k units and take every k-th unit thereafter.