Читать книгу The Wiley Blackwell Companion to Medical Sociology - Группа авторов - Страница 44

Assuming you have a health-related question inspired by sociological theory, how do you investigate it empirically? You need data. Without data – and good data at that – you’ll never be able to empirically answer your questions. Unfortunately, data collection is one of the most challenging aspects of conducting original research in medical sociology. Researchers are often forced to make sacrifices as they move from the limitless world of ideas to the restrictive nature of rigorous, scientific observation. All research is pressed on either side by efficiency and bias. Normally these are statistical terms, but we think efficiency and bias are conceptually useful here, too. On the one hand, researchers must collect enough data to accurately answer their question but not so much that they waste precious resources, time and money, collecting extraneous information (we’ll call this inefficiency). On the other hand, if researchers fail to collect enough information to sufficiently observe a sociological phenomenon, they will likely get incorrect (i.e. biased) answers to their questions. We review several of the challenges of collecting data and discuss why addressing them carefully is important for correctly answering research questions.

First and foremost, sociological phenomena are difficult to observe and even harder to measure consistently. This makes conceptualization a critical part of any sociological endeavor, and most researchers spend a large part of their time reviewing prior research in an effort to clearly define the unique pieces of their research questions. Human capital, for example, is a widely referenced concept in medical sociology, especially among scholars studying health behaviors (Mirowsky and Ross 1998). The basic idea is straightforward – human capital improves the constellation of behaviors that maintain health (e.g. avoiding sugar, staying physically active, etc.). Although years of schooling offers a sensible measure of human capital in the US, educational attainment is only an indirect measure of the wide array of cognitive and non-cognitive abilities (e.g. intelligence and self-discipline) that could potentially provide health-related advantages. By clearly conceptualizing these two distinct components of human capital, Herd (2010) found that cognitive human capital played a greater role than non-cognitive capital in mediating education and health among older adults in the Wisconsin Longitudinal Sample.

This brings us to the next challenge, choosing a level of analysis. Social action can be observed at micro, meso, and macro levels (Collins 1981; Fine 1991). Although some sociological theories lend themselves more clearly to specific levels of analysis, most of the major theoretical frameworks in medical sociology can be examined at multiple levels. For our investigation of education and health, should we collect rich descriptions of human experiences with health problems through a few dozen in-depth interviews from people with different educational backgrounds? Or, should we collect less detailed information from a sample of people who represent the entire US population? The answer usually depends on your research question. For example, the question “is the rate of college graduates in US cities associated with cardiovascular mortality rates?” would require a macro-level assessment of city demographics, whereas the question “how does physician-perceived human capital among patients affect patient-physician interactions in free health clinics?” would require micro-level data based on observations of social interactions. Both of these questions are inspired by sociological work on education and health, but each requires a much different approach to data collection. Studies of micro-level sociological phenomena often collect data on social interactions, attitudes, cognition, emotions, and decision-making (e.g. Spencer 2018). Studies of meso-level phenomena generally collect data on individuals, social groups, and social networks (e.g. Hallgren et al. 2020). Studies of macro-level phenomenon often consider country-level data on population trends, institutional and cultural change, laws, and health inequalities (e.g. Bakhtiari et al. 2018).