Convenience Sampling

Convenience sampling (also known as availability sampling) is a method where the selection of participants (or other units of analysis) is based on their ready availability. This availability is usually in terms of geographical proximity (e.g., students in the researcher’s own college or in neighboring colleges) but may involve other types of accessibility, such as known contacts.

As sample selection is based on the researcher’s choice, convenience sampling is a form of nonprobability sampling distinct from forms of probability sampling such as (stratified) random sampling or cluster sampling. Convenience sampling differs from quota sampling—another form of nonprobability sampling, in which selection is based on certain identified characteristics—in not specifically seeking representativeness.

Like other nonprobability sampling methods, convenience sampling has certain practical advantages. It does not require an exhaustive list of the study population, which is needed for random sampling, and has clear logistical and resource benefits in terms of travel, cost, and time expenditure. However, these advantages are at the price of certain [Page 403]biases, such as sampling error and undercoverage. Sampling error means that the sampling method provides a sample whose characteristics (e.g., participants’ age, educational level, or socioeconomic status) differ systematically from those of the population of interest. Undercoverage means that certain individuals in the population of interest are excluded by the sampling method (e.g., the researcher’s interest is in staff in community colleges, liberal arts colleges, and universities, but a convenience sample only accesses staff in community or liberal arts colleges).

If quantitative data are collected, a convenience sample’s lack of assured representativeness causes difficulties at the data analysis stage. As the sample is not representative in the way that a probability sample is, using a sample statistic (e.g., a sample proportion) to estimate a population parameter (e.g., a population proportion) is inadvisable, as such an estimate is likely to be biased. Furthermore, using statistical hypothesis tests is questionable, as these assume random sampling. Inferential statistics applied to convenience samples therefore make an assumption that the sample is comparable to a random sample from the same population (an assumption that is normally untestable). In qualitative research, however, this strict empirical representativeness is not normally at issue. What matters here is that members of the sample are relevant to the aims of the study—this is more a notion of theoretical than of statistical generalization and does not require the same concern for empirical representativeness.

• Although convenience sampling has methodological shortcomings, these can be mitigated by:
• describing the demographic and other characteristics of the sample in detail, and if possible, comparing these with those of the relevant population, so that readers of the study can evaluate its representativeness;
• making efforts to gain the participation of all intended participants, so that response bias or
• self-selection does not compound a lack of representativeness; and
• ensuring that the participants recruited are theoretically relevant to the study, so that selection is not based solely on convenience.