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The combination of probability and nonprobability sampling methods in multistage designs can be particularly useful for testing hypotheses about sociocultural influences on health. For example, my colleagues and I used a variant of cluster sampling that combines probability and nonprobability techniques in our work on skin color, social classification, and blood pressure in Puerto Rico (Gravlee et al. 2005). We identified clusters purposively to maximize contrasts in key explanatory variables – social class and skin color – and sampled randomly within clusters. This strategy, like all decisions in research design, involved trade-offs: Identifying clusters using nonprobability methods limited generalizability but probably made it more efficient to detect sociocultural processes related to class and color. Given limited resources, that’s a trade-off we were willing to make.

Sample size, Bernard (2018, p. 127) notes, is a function of four things: (1) how much variation exists in the population, (2) the number of subgroups you want to compare, (3) how big the differences are between subgroups, and (4) how precise your estimates need to be. These principles apply to studies large and small and are relevant to collecting either attribute or cultural data.