Sampling Methods Explained: 10 Types of Sampling Methods

Sampling methods are used in surveying and data research to find manageable cohorts of people who are representative of the population at large. Some sampling techniques seek to glean data about the whole population of a region; others seek sample selections that reflect a target population.

There are two main categories of sampling: probability sampling and non-probability sampling.

1. 1. Probability sampling: In this category of sampling, all members of the population have an equal chance of being selected for a study. In probability sampling, participants are chosen by random selection, which limits the potential for sampling bias and downstream sampling errors. Random sampling methods can be time-consuming and expensive, which leads some researchers to favor non-probability sampling methods.
2. 2. Non-probability sampling: In non-probability sampling methods, some members of a target population have a higher chance of being selected for a study. Instead of choosing respondents from a broader population or subgroup at random, researchers rely on their judgment to select respondents. Non-probability sampling methods tend to be faster and more cost-effective than probability-based methodologies. Their downside is their susceptibility to non-representative sample frames and sampling error.

Explore the ten main types of sampling methods that often factor into research design.

1. 1. Simple random sampling: This is the purest form of probability sampling. In simple random sampling, individuals are chosen from a whole population at random. These individuals could be assigned numbers and then a random number generator selects from among these numbers. This is effectively how telephone surveying works.
2. 2. Systematic sampling: Another type of probability sampling, systematic sampling picks respondents from a larger population by choosing them at regular intervals. This is the method of sampling used when a researcher picks every “nth” person in a group to be part of a study. By creating a consistent sampling interval (or studying every seventh person in a group, for example), the statisticians can get a manageable sample size that should still be representative of the entire population.
3. 3. Stratified sampling: This is a form of probability-based multistage sampling. The starting point of stratified sampling is separating a sampling frame into subsets called strata. Each stratum will be intentionally homogenous for a specific characteristic. For instance, at an international conference, this stratification could be based on nationality with all the Americans in one stratum, all the Canadians in a different stratum, and so forth. Then, within each stratum, respondents are chosen at random for individual study. Researchers use this type of sampling when weighting for population imbalance.
4. 4. Cluster sampling: Cluster sampling begins by dividing a total population into clusters that have similar characteristics. Researchers then pick a small number of randomly selected clusters to study further. For instance, if researchers using cluster sampling wanted to study elementary schoolers in a district, they could make each individual school a cluster and pick three of those schools to study. They can also enact multistage sampling where individual students from those schools get randomly selected for further analysis.
5. 5. Purposive sampling: Also known as subjective sampling, judgment sampling, expert sampling, or selective sampling, purposive sampling rests on the theory that sometimes researchers must pre-select subgroups from an entire population in order to create a case study or shape a grounded theory. It is a form of non-probability sampling that relies on inferences and hypotheses from researchers. Before they undertake a purposive sampling campaign, researchers make assumptions about a population and then intentionally reach out to a chosen subset of the population in order to test their theory.
6. 6. Typical case sampling: In this sampling design, which is a variation on purposive sampling, a researcher intentionally looks for what they consider a representative sample of the population being studied. They intentionally discard any subjects whom they deem not representative of the population. Researchers and statisticians often use typical case sampling to examine a phenomenon of interest within the general population.
7. 7. Critical case sampling: This is another purposive sampling method. In critical case sampling, subjects are selected based on researchers' inferences that they might represent a broader trend. Sometimes critical case sampling leads to the discovery of many more subjects who share the same traits with the respondents.
8. 8. Convenience sampling: The convenience sampling method is a non-probability sampling technique that draws data from respondents that are convenient for researchers to reach. Examples of convenience sampling include surveying friends and family, positioning yourself at a store entrance to conduct market research, or posting online surveys and questionnaires that people may fill out if they wish.
9. 9. Snowball sampling: In this variation on convenience sampling, respondents may be asked if they know other people who might qualify to be part of the sample frame. Researchers then contact people whose names were provided and try to recruit them to participate in survey research. The recruited respondents can then, in turn, help recruit even more respondents, creating a snowball effect. The idea is to build up a sample size filled with people that share common traits.
10. 10. Quota sampling: Quota sampling is a non-probability sampling technique that targets a population of interest. Quota sampling is a form of purposive sampling that seeks to replicate real-world population proportions. When researchers use quotas to sample respondents, they often try to scale their data to match the demographics of a real-world population.

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