6.2: Nonprobability Samples
- Page ID
- 124498
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Define nonprobability sampling and identify situations where it is the appropriate choice.
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Describe and distinguish between the four primary types of nonprobability samples.
Overview: Why Use Nonprobability Sampling?
Nonprobability sampling refers to sampling techniques for which a person’s (or event’s or researcher’s focus’s) likelihood of being selected for membership in the sample is unknown. Because we don’t know the likelihood of selection, we don’t know with nonprobability samples whether a sample represents a larger population or not.
Nonprobability sampling is used when representing a larger population is not the primary goal or is possible to get. Instead, researchers use these techniques to:
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Design and Pilot Research: These methods help "work out the kinks" in a survey or provide a quick "lay of the land" before a larger study.
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Achieve In-depth Understanding: Qualitative researchers often seek an idiographic (in-depth) understanding of a specific phenomenon or group rather than a broad, statistical overview.
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Contribute to Theory: By seeking out specific or even anomalous cases, researchers can modify, expand, or critique existing social theories.
Types of Nonprobability Samples
Researchers utilize four main strategies to select participants based on the specific needs of their project that table 6.1 illustrates:
Table 6.1 Nonprobability Sample Types
| Sample Type | Description |
| Purposive |
The researcher seeks out participants who meet specific criteria or cover a full range of perspectives based on their expert knowledge. |
| Snowball |
The researcher relies on initial participants to refer other potential subjects, creating a "chain referral." |
| Quota |
The researcher identifies important subgroups and decides in advance how many participants to recruit from each. |
| Convenience |
The researcher collects data from whoever is most easily accessible, often used in exploratory work. |
1. Purposive Sampling
For this type of sampling, the researcher is the expert and so then pulls subjects non-randomly to match the needs they have in mind.
For example, a study of student housing satisfaction could use purposive sampling if the researcher uses their expert knowledge to get subjects non-randomly to ensure residents from all types of dorms and apartments are represented.
2. Snowball Sampling
This is a "chain referral" method where the sample grows as participants refer others. It is highly effective when studying:
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Stigmatized Groups: Where participants may be hesitant to come forward publicly but trust a referral from someone they know.
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Rare Populations: Groups that are difficult to locate through traditional means. Researchers often use incentives to encourage initial participants to refer others who qualify.
An example of this type of study could be a researcher studying how people with a sensitive medical condition cope might interview one person they know, who then refers them to others in their support network.
3. Quota Sampling
Researchers identify categories important to the study and set a "quota" for how many people to include from each subgroup. This is done non-randomly. While this accounts for variation, it does not yield statistically representative findings.
For example, if you want to study housing satisfaction and believe gender and housing type are key, you might create four subgroups (men in apartments, women in apartments, men in dorms, and women in dorms) and select five people from each.
4. Convenience Sampling
This method focuses on collecting data from those most easily accessible. While it offers the benefit of speed and ease, researchers must be extremely cautious about generalizing these findings to a larger population.
For example, if you have ever seen brief interviews of people on the street on the news, you have seen a convenience sample being interviewed. While it offers the benefit of speed and ease, researchers must be extremely cautious about generalizing these findings to a larger population.
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Purpose over Representation: Nonprobability samples are ideal for exploratory research, evaluation, and building theoretical contributions where statistical representation is not the goal.
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Variety of Techniques: Depending on the study goals, researchers can choose between purposive, snowball, quota, or convenience sampling.
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Apply the Methods: If you were studying public park usage in your hometown, how would you design a sample using each of the four methods above?
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Evaluate the Methods: Based on the descriptions, which sampling technique do you believe is the most rigorous, and which is the most prone to bias? Explain your reasoning.