Читать книгу The Wiley Blackwell Companion to Medical Sociology - Группа авторов - Страница 49

Many questions of interest to medical sociologists are descriptive in nature. Their answers provide descriptions of circumstances without necessarily providing a sense of how they might be altered. For instance, we might be interested in knowing the extent to which immigrants exhibit better health than non-immigrants, trends in life expectancy among members of different communities over time, or differences in health care expenditures across countries. Answers to such questions not only provide valuable information for medical sociologists but are also critical for policymakers and other constituencies that rely on data to inform decision-making.

In some cases descriptive research questions can be addressed in a straightforward manner through calculating simple statistics such as means and proportions. In other cases, finding answers to descriptive research questions requires the use of a statistical model that takes into account multiple factors. For instance, in estimating trends in death rates due to cancer for different communities over time we might want to adjust for different age structures across the communities. A statistical model provides the means to make such an adjustment.

Various forms of regression models are the primary statistical models used in quantitative medical sociology research (Gelman and Hill 2007; Kalbfleisch and Prentice 2011; Long 1997). Regression models take the form of regressing an outcome or dependent variable on a set of predictors or independent variables, one of which is often considered the focal independent variable. The estimates from fitting a regression model provide a means for assessing the relationship between a focal independent variable and an outcome (e.g. years of schooling and self-rated health) while adjusting or controlling for one or more other independent variables. The outcome variable may be continuous (e.g. health care expenditures in constant dollars), categorical (e.g. an indicator for smoking in the past month), or even unobserved (e.g. the risk of dying in a given year). The independent variables may take any level of measurement.