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Sampling is the use of a subset of the population to represent the whole population. Probability sampling, or random sampling, is a sampling technique in which the probability of getting any particular sample may be calculated. Nonprobability sampling does not meet this criterion and should be used with caution. Nonprobability sampling techniques cannot be used to infer from the sample to the general population.
The advantage of nonprobability sampling is its lower cost compared to probability sampling. However, one can say much less on the basis of a nonprobability sample than on the basis of a probability sample. Of course, research practice appears to belie this claim, because many analysts draw generalizations (e.g., propose new theory, propose policy) from analyses of nonprobability sampled data. One must ask, however, whether those published works are publishable because tradition makes them so, or because there really are justifiable grounds for drawing generalizations from studies based on nonprobability samples.
Some embrace the latter claim, and assert that while probability methods are suitable for large scale studies concerned with representativeness, non-probability approaches are more suitable for in-depth qualitative research in which the focus is often to understand complex social phenomena (e.g., Marshall 1996; Small 2009). These assertions raise an interesting question—how can one understand a complex social phenomenon by drawing only the most convenient expressions of that phenomenon into consideration? What assumption about homogeneity in the world must one make to justify such assertions? Alas, research indicates only one situation in which a non-probability sample can be appropriate—if one is interested only in the specific cases studied (for example, if one is interested in the Battle of Gettysburg), one does not need to draw a probability sample from similar cases (Lucas 2013).
Still, some use non-probability sampling. Examples of nonprobability sampling include:
- Convenience, Haphazard or Accidental sampling - members of the population are chosen based on their relative ease of access. To sample friends, co-workers, or shoppers at a single mall, are all examples of convenience sampling. Such samples are biased because researchers may unconsciously approach some kinds of respondents and avoid others (Lucas 2013), and respondents who volunteer for a study may differ in unknown but important ways from others (Wiederman 1999).
- Snowball sampling - The first respondent refers a friend. The friend also refers a friend, and so on. Such samples are biased because they give people with more social connections an unknown but higher chance of selection (Berg 2006).
- Judgmental sampling or Purposive sampling - 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.
- Deviant Case - Get cases that substantially differ from the dominant pattern (a special type of purposive sample).
- Case study - The research is limited to one group, often with a similar characteristic or of small size.
- ad hoc quotas - A quota is established (say 65% women) and researchers are free to choose any respondent they wish as long as the quota is met.
Even studies intended to be probability studies sometimes end up being non-probability studies due to unintentional or unavoidable characteristics of the sampling method. In public opinion polling by private companies (or other organizations unable to require response), the sample can be self-selected rather than random. This often introduces an important type of error: self-selection bias. This error sometimes makes it unlikely that the sample will accurately represent the broader population. Volunteering for the sample may be determined by characteristics such as submissiveness or availability. The samples in such surveys should be treated as non-probability samples of the population, and the validity of the estimates of parameters based on them unknown.
- Sampling (statistics)
- Cluster sampling
- Judgment sample
- Multistage sampling
- Quota sampling
- Simple random sample
- Systematic sampling
- Stratified sampling
- Berg, Sven. (2006). "Snowball Sampling–I," pp. 7817–7821 in Encyclopedia of Statistical Sciences, edited by Samuel Kotz, Campbell Read, N. Balakrishnan, and Brani Vidakovic. Hoboken, NJ: John Wiley and Sons, Inc.
- Lucas, Samuel R. (2013). "Beyond the Existence Proof: Ontological Conditions, Epistemological Implications, and In-Depth Interview Research.", Quality & Quantity, doi:10.1007/s11135-012-9775-3.
- Marshall, Martin N. 1996. "Sampling for Qualitative Research." Family Practice 13: 522–526. doi:10.1093/fampra/13.6.522
- Small, Mario L. (2009). "‘How many cases do I need?’ On science and the logic of case selection in field-based research." Ethnography 10: 5–38. doi:10.1177/1466138108099586
- Wiederman, Michael W. (1999). "Volunteer bias in sexuality research using college student participants." Journal of Sex Research, 36: 59-66, doi:10.1080/00224499909551968.