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True (1996) noted that research design plays different roles in the training of medical anthropologists and epidemiologists. In epidemiology and allied fields, researchers recognize a set of basic research designs that have different strengths and weaknesses for answering particular types of questions. Medical anthropologists are generally not trained to think in these terms. However, familiarity with the range of research designs commonly used in other social and health sciences can enhance the validity of research in medical anthropology and increase our impact on neighboring disciplines and policymakers.

Figure 4.4 illustrates a typology of basic research designs. The first major distinction is between observational and experimental designs.

Figure 4.4 Basic research design options in medical anthropology and neighboring disciplines.

Experimental Designs Experimental designs are distinguished by two features: random allocation and manipulation of the key causal variables. In classic experiments, researchers randomly assign participants to either an intervention or control group and measure one or more dependent (outcome) variables in both groups. Participants in the intervention group are then exposed to a treatment designed to test the causal effect of an independent (explanatory) variable, and both groups are measured again on the dependent variable. Random allocation, when done well, makes groups comparable with respect to unmeasured variables, such that whatever differences emerge between groups after the intervention are likely to reflect the true causal effect of the intervention.

Experimental designs are not common in medical anthropology, but there are successful examples. Shain et al. (1999) combined ethnography and a randomized trial to test the effect of culture- and gender-specific interventions to prevent sexually transmitted infections in African-American and Mexican-American women in San Antonio, TX. They first collected ethnographic data (observations, 25 focus-group discussions, 102 in-depth interviews) on the cultural context of sexual behavior, perceptions of risk, and motivations for behavior change. They used this information to design culturally appropriate messages about recognizing risk, committing to change, and communicating about sex. Then, 424 Mexican-American and 193 African-American women were randomly assigned to receive either the culturally appropriate messages (intervention group) or standard counseling (control group). One year later, Shain et al. found that women in the intervention group were 49% less likely to have a sexually transmitted infection than were the controls.

Even if you never intend to run a randomized trial, being familiar with their strengths and weaknesses maximizes impact across disciplines. Smith-Morris and colleagues (2014) describe a collaboration in which ethnography was built into a randomized clinical trial. Their experience highlighted the complementarity of approaches and suggested a need for “more clinical and trial-based applications of medical anthropology” (p. 157).

Observational Designs Most research in medical anthropology, as in other health-related social sciences, is observational. Observational studies lack the defining features of experiments – random assignment to comparison groups and control over independent variables – and so are not well suited to demonstrate causal effects. But they are preferable to experiments for exploratory questions and have other advantages in confirmatory research, including higher external validity (generalizability), greater feasibility, and often fewer ethical objections.

We can distinguish three broad classes of observational designs: cross-sectional, longitudinal, and case-control studies (Figure 4.4). These three types of studies are recognized as the basic design options in epidemiology and biomedical sciences, but they are also used to varying degrees in medical anthropology. Cross-sectional studies, in which data are collected during one point in time, are the most common type. Although data collection often lasts for months or years, the study is still considered cross-sectional if the data are taken to represent one point in time. Thus, even long-term ethnographic research is usually cross-sectional in design (Gravlee et al. 2009).

In longitudinal designs, the data pertain to two or more points in time. In the last 50 years, longitudinal studies that incorporate panel data – repeated measures from the same units of observation at different points in time – have become increasingly important across the social, medical, and public-health sciences, but they remain relatively rare in anthropology. The lack of panel studies in anthropology represents a mismatch between theory and method, because panel data are particularly suited to the study of continuity and change – central areas of anthropological inquiry (Gravlee et al. 2009). For example, there is long-standing debate about the consequences of market integration and culture change for the health of Indigenous peoples. Most relevant studies, however, use cross-sectional designs, which cannot track the effects of market integration over time. Godoy et al. (2009) used panel data collected annually (2002–2006) from the Tsimane’ Amazonian Panel Study (TAPS) to fill this gap. Results suggest a general improvement in well-being over time, with the highest rate of change in villages closest to the market town. This longitudinal study provides a better test of theory than would a cross-sectional design.

Panel studies need not be multiyear designs. Wutich (2009) combined participant observation with a five-wave panel survey in a study of common pool water institutions in Cochabamba, Bolivia. The panel design consisted of a household survey (N = 72) in which Wutich and her assistants interviewed households every two months for 10 months. This design – spanning two wet seasons and one dry – made it possible to examine seasonal variability in climate and water security in a way that would not have been possible with a cross-sectional study.

Case-control studies (Figure 4.4) are relatively rare in anthropology, although they are among the most common designs in epidemiology. The classic example in medical anthropology is Rubel and colleagues’ (1984) study of susto, a folk illness reported in many parts of Latin America. Based on ethnographic accounts, Rubel et al. developed specific hypotheses about the sociocultural factors that shape susceptibility to susto. To test these hypotheses, they compared a sample of people who suffered from susto (cases) with people who did not (controls) in three communities with different histories, language, and cultures in the Oaxaca Valley of Mexico (Zapotec, Chinantec, and mestizo).

Cases and controls were matched to form pairs of people who differed in whether they reported susto but were similar in other respects: age, gender, community, and complaints of being sick. Rubel and colleagues then tested cases and controls for differences in social stress, psychiatric symptoms, and physical health problems. There were no significant associations between psychiatric symptoms and susto, but people suffering from susto did experience more social stress, including perceived difficulty in performing important social roles – a pattern Rubel et al. expected from ethnography. And this difference really made a difference: Seven years later, 17% of people who complained of susto had died, but all the controls were still alive.

Rubel and colleagues’ study also teaches a valuable lesson about research design in general. It is a model of good design, but it wasn’t perfect – things happen in the field. Rubel et al. are honest about this fact and acknowledge their uncertainty. They realized, for example, that the gendered stigma of susto may have resulted in fewer Chinantec being willing to label themselves with the illness. This pattern would have biased the researchers’ conclusions about gender differences in the experience of susto. One of hallmarks of well-designed research design is that it makes such problems public.