Theoretical Sampling

Theoretical sampling is a tool that allows the researcher to generate theoretical insights by drawing on comparisons among samples of data. The data can include population, events, activities, or even time periods. Barney G. Glaser and Anselm Strauss first significantly explained the term in The Discovery of Grounded Theory (1967).

Data remain opaque if the researcher develops no sensitivity among the potential differences and similarities among a variety of classes or samples of data. More important, the choice of data samples allows the researcher to impute the theoretical aspects of the research. For instance, data generated in a study of horseback riding by the disabled might lack depth and understanding if the researcher chooses to ignore the kinds of participants involved in the many aspects of this form of horseback riding such as the disabled person (and this by age or gender), the parents or guardian of that person, the organizers of horseback riding events, and those responsible for dressing the horses. The researcher might also find it fruitful to conduct a theoretical sample of subgroups; namely, horseback riding of the disabled in rural, semirural, and urban settings. A theoretical sample would bring into relief a variety of experiences that can be compared to generate concepts and theory.

The typical basic research process often does not allow a researcher initially to set out the samples. Rather, as the researcher first deepens him- or herself in the field setting, the potentiality of creating theoretical samples becomes more obvious. The question, “How can I differentiate or compare data that would allow me to move my research to a more conceptual stage?” resembles a refinement of how a researcher can use data to advance conceptual thinking. Taking an example from thinking theoretically about research on a Florida retirement community, one would offhand think of interviewing active and inactive members of the community. However, within the cadre of active members, members of the entertainment committee constitute the hyperactive ones, while on the inactive side, one would be forced to collect data from “snowbirds” and “snowflakes” who are intermittent visitors from the north. Without such dimensionalizing of the data, it would be hard to theoretically advance the data about the retirement community.

The two above examples also illustrate the timing of introducing theoretical samples in one's research. In some cases, theoretical sampling involves further differentiations among classes of data whether they pertain to activities, events, documents, or time periods. Observing street-level activities in a village, a researcher might feel compelled to derive a theoretical sample based on times of day. This type of theorizing can yield clues about the shape of public life in that village and lead the researcher to generate insights about “compact” time and “diffuse” time.

The theoretical sample is a simple, but highly effective tool that can spark further insights because it can save time. Moreover, the use of theoretical sampling forces the researcher into new directions, stretching the diversity of data gathered for the purpose of developing concepts and theories.