Purposive sampling

Purposive sampling—which may also be called judgmental, selective, or subjective sampling—describes population samples used for research that have been deliberately chosen to reflect a specific characteristic such as age, location, or even species. The term is meant to describe a premeditated, or purposive, intent in picking individuals who will comprise the study sample. This is in contrast to other methods that seek study participants who have been chosen at random so that no single shared characteristic has a heavier impact on the study. Purposive sampling is used in many different types of research, including medical studies, science, polling, and marketing.

Overview

Purposive sampling is a form of non-probability sampling. A non-probability sample is any survey in which the participants are deliberately chosen. This is in contrast to probability samples, which seek to select participants who are chosen at random. Probability samples are used to avoid creating a survey bias where a segment of a population, such as a single racial or age group, are overrepresented, thus potentially skewing the results.

Non-probability sampling may take many forms. For instance, discussion panels used to evaluate products that pay volunteers for their time or mall intercept studies in which shoppers are specifically approached by polling workers to participate in a survey are types of non-probability sampling. Such methods may be potentially flawed because they do not accurately represent all segments of the population as a whole and can therefore offer biased results.

Some types of purposive sampling may include typical case, extreme (or deviant) case, critical case, heterogeneous (or maximum variation), homogeneous, and theoretical sampling. A typical case sample is one wherein the surveyor wants to analyze a small population sample to create a base (or average) that may represent aspects of a larger population. These small samples are intended to be used as part of a broader analysis and may offer insight into a specific reference point that is found within the broader (or typical) population. This is in contrast to extreme case sampling, which seeks out outliers from the norm. For instance, in a study of income levels in a population, a typical case study will seek to determine the average income of a population using smaller samples. An extreme sampling might study people at either end of the income spectrum—that is, the very rich or very poor—to determine how they differ from the average. A critical case sampling studies only a single person or event. Critical case sampling is done when analyses of individuals may offer heightened insight into a broader topic or issue.

In heterogeneous/maximum variation sampling, researchers seek to include a varied cross-section of subjects representing a wide variety of characteristics. This is often done to understand and identify common traits or themes within a population. For example, a heterogeneous sampling in science may include a number of different plant species to see how they are collectively affected by drought. By contrast, a homogeneous sampling attempts to include populations with shared characteristics (such as individual species, income, or education), thus enabling researchers to focus on individual populations or aspects of a community. Theoretical sampling uses multiple studies over the course of ongoing research to uncover different subsets of a population to find and understand the interrelationships between these groups.

Bibliography

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