Читать книгу The Wiley Blackwell Companion to Medical Sociology - Группа авторов - Страница 48

Operationalization involves two independent stages, identifying (or designing) a valid measure of a concept and then determining how exactly to incorporate that measure into a statistical model. The first stage involves a good deal of perseverance and creativity from researchers. Operationalizing complex sociological concepts generally requires a detailed review of prior studies in light of one’s specific research question. The second stage is more circumscribed and can often depend on the availability of measures and the degree to which measurement models will help inform one’s research question.

Researchers often take one of the following approaches to incorporating measures into statistical models: (1) single variable, (2) multiple variables, or (3) latent variable (Bollen et al. 2001). In some cases, the second step in operationalization is relatively straightforward. For example, if we are interested in estimating the extent to which age is related to alcohol use in a population, then we would look for data that includes respondents’ age and some assessment of alcohol consumption. This would be a single variable approach because we only used one variable to represent each concept. We should note, however, that even in this simple example prior research offers multiple potential measures of age (e.g. in years, meaningful age ranges) and alcohol use (e.g. drinks on average, binge drinking, any drinking, etc.). In other cases, operationalization is less clear. Socioeconomic status (SES) is a recurring term in health disparities research, but despite its relative cohesiveness as a theoretical concept, researchers uses a wide variety of variables to operationalize SES. Generally, medical sociologists use a multiple variable approach to measuring SES by incorporating two or more variables related to educational, economic, and occupational attainments (Wolfe 2015). Although the single variable approach would be easier to interpret, the complexity of using multiple variables to represent SES is usually offset by the greater amount of information gained from results.

Working with latent variables requires an entirely different approach to measurement (Bollen 1989). For single and multi-variable operationalizations, researchers assume that sociological concepts can be directly observed. When we investigate variables such as years of age or schooling, we assume that people accurately know and report that information (with some random error being acceptable). Suppose, however, we’re interested in the amount of depression in the general population. Do people always know if they’re depressed? Does everyone have the same definition of depression? This is a trickier situation. Fortunately, the Center for Epidemiologic Studies Depression (CESD) scale, which originally included 20 items (Radloff 1977), is a well-established measure of depression. There are a number of statistical approaches to combining indicators of depression like the CES-D items into a single scale (e.g. Payton 2009; Perreira et al. 2005), but they all assume that we can create a latent variable for depression that avoids the measurement error we would encounter with a single question asking directly about depression.

Measurement concerns might seem pedantic at times but taking measurement issues seriously can generate important research. For example, Montez et al. (2012) evaluated models of US mortality with 13 different measures of education derived from Hummer and Lariscy (2011). The preferred functional form of education included a linear decline in mortality risk from 0 to 11 years, a notably larger reduction in mortality risk after high school completion, and a steep linear decline continuing after high school completion. Although their primary aims were methodological in nature, searching for the optimal form of education revealed an interesting theoretical insight – educational attainment benefits survival through both human capital accumulation and socially meaningful credentials (Collins 1979).

Research on health lifestyles offers another example of the importance of measurement (Cockerham 2005; Cockerham et al. 2020). Health lifestyles refer to meaningful combinations of health behaviors that people adopt. We can imagine a lifestyle involving regular exercise, a nutritious diet, and abstention from smoking and heavy drinking. Alternatively, we can also imagine a largely sedentary lifestyle with limited concerns about a nutritious diet. And one could continue with several other possible clusters of health behaviors that coalesce into recognizable health lifestyles. To investigate these potential lifestyles and their relationship to adult health, Cockerham et al. (2020) identified latent classes of different health lifestyles, and their results revealed a unique pattern of associations between health lifestyle and health status due to diagnosed conditions that affected lifestyles in middle adulthood.