Читать книгу A Companion to Medical Anthropology - Группа авторов - Страница 68

There are many options for probability and nonprobability sampling designs. Miles and Huberman (1994, p. 28) list 16 types of nonprobability sampling. Onwuegbuzie and Leech (2007) identify 24 sampling designs, and Teddlie and Tashakkori (2009, p. 170) delineate 26, including a mix of probability and nonprobability methods. These complex typologies build on a small set of basic sampling designs summarized in Table 4.1. For details about probability and nonprobability sampling in anthropology, see Bernard (2018), Guest (2015), Johnson (1990), and Schensul and LeCompte (2013, pp. 280–318).

Table 4.1 Basic probability and nonprobability sampling designs

Method	Purposes	Procedures
Probability methods
Simple random sampling	Generate representative sample when adequate sampling frame is available	List all members of population (sampling frame); select subset at random
Systematic random sampling	Generate representative sample; may not need to enumerate entire sampling frame	Select random starting point in sampling frame; select every Nth case
Stratified random sampling	Ensure that key subpopulations are represented; maximize between-group variation to increase precision	Divide population into subgroups; select random sample from each subgroup
Cluster sampling	Generate representative sample when no convenient sampling frame exists; sample dispersed populations efficiently	Divide population into clusters (e.g., neighborhoods, clinics); select random sample of clusters; sample within clusters
Nonprobability methods
Purposive sampling	Sample theoretically important dimensions of variation	Identify important theoretical criteria; select cases to satisfy criteria; multiple criteria-based methods are available
Quota sampling	Generate sample with fixed proportions of key subpopulations	Divide population into subgroups; purposively select cases to fill quotas
Chain referral (snowball, respondent-driven sampling [RDS])	Construct sample of hardto- find or hard-to-study populations	Snowball: ask seed informants to recommend others who might participate RDS: Use structured incentives to reduce bias in selection
Convenience (haphazard) sampling	Recruit participants when no other methods are feasible	Select groups or individuals that happen to be available and willing to participate

One of the most common nonprobability sampling designs is quota sampling. Quota sampling involves identifying relevant subgroups in a population and sampling fixed proportions from each subgroup. Schoenberg et al. (2005) used quota sampling to explore differences and similarities in lay knowledge about diabetes between African Americans, Mexican Americans, Great Lakes Indians, and rural Whites. They set a quota of 20 participants from each group. This design balanced a desire for larger subsample sizes against practical constraints on the number of time-intensive, in-depth interviews researchers could complete. Within each group, Schoenberg et al. selected respondents whose age, ethnicity, and residential area increased the likelihood of experiencing diabetes. This strategy reflects the theoretical purpose of sampling cultural knowledge rather than estimating individual attributes.

Many medical anthropologists also use purposive sampling techniques. The goal of purposive sampling is to represent important dimensions of variation relevant to the aims of research. There are many approaches to purposive sampling, including selection of extreme, typical, unique, or politically important cases; selection to maximize homogeneity or heterogeneity of the sample; and identification of critical cases who have specialized knowledge or experiences relevant to the subject of interest (Onwuegbuzie and Leech 2007). In ethnographic research, the selection of key informants is an example of critical-case sampling.

Medical anthropologists often combine the building blocks in Table 4.1 to construct complex, multistage sampling designs. Baer et al. (2003) used a two-stage sampling design in their study of cross-cultural differences and similarities in the meaning of the folk illness nervios in Mexico, Guatemala, and the United States. In each of four sites, they purposively selected clusters – “a village, neighborhood, or census tract” (p. 319) – based on differences in social class, ethnicity, and other factors. Then they randomly selected roughly 40 households from each site, for a total sample size of 158.

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.