Читать книгу A Companion to Medical Anthropology - Группа авторов - Страница 67

Some researchers collect data on an entire population, but most medical anthropologists work with samples, or subsets of the population in which they are interested. Thus, one of the key tasks of research design is to select an appropriate sampling strategy.

There are three steps in developing a sampling strategy: (1) defining the population, (2) identifying the unit of analysis (e.g., individual, household, clinic), and (3) selecting units of analysis for inclusion in the sample. The goal is to be able to say something about the units of analysis that were not selected for the sample. To meet this goal, it helps to be clear about what you’d like to say about your units of analysis. Do you want to estimate the average age in a population? Or, do you want to describe what it means to get older in that population? Medical anthropologists are likely to ask both types of questions, and they imply different types of sampling strategies.

We can describe this distinction in terms of “individual attribute” versus “cultural” data (Bernard 2018, p. 114). Attribute data refer to the characteristics of people (or other units of analysis) in a sample (e.g., age, income, blood pressure). Researchers in most health-related social sciences collect primarily attribute data of this type. Medical anthropologists collect attribute data, too, but many are also interested in questions such as “What does it mean to get older around here?” or “How important is income to a person’s status in this community?” or “How do you know if you have high blood pressure, and how is it distinct from other illnesses?” These questions elicit cultural data, because they capture shared and socially transmitted systems of meaning that organize how people make sense of the world.

Attribute data generally require probability samples; cultural data do not, because the shared and socially constructed nature of cultural phenomena violates the assumption of case independence in classical sampling theory (Gravlee 2005, p. 953). This assumption is often warranted with attribute data – your age is unrelated to mine. But if we participate in the same culture, your understanding of what it means to get older and mine are bound together, because people acquire cultural knowledge through social interaction. Thus, efficient ethnographic samples should select units of analysis to represent the range of variability in life experiences and social contexts related to the transmission of culture (Guest 2015; Johnson 1990). Probability samples are not necessary for achieving this aim, as probability and nonprobability samples have been shown to yield identical conclusions about cultural data (Handwerker and Wozniak 1997).