Why and How Epidemiologists Should Use Mixed Methods

Abstract

The field of epidemiology's current focus on causal inference follows a quantitative approach and limits research questions to those that are strictly quantifiable. How can epidemiologists study biosociocultural public health problems that they cannot easily quantify? The mixed-methods approach offers a possible solution by incorporating qualitative sociocultural factors as well as the perspective and context from the population under study into quantitative studies. After a pluralist perspective of causal inference, this article provides a guide for epidemiologists interested in applying mixed methods to their observational studies of causal identification and explanation. We begin by reviewing the current paradigms guiding quantitative, qualitative, and mixed methodologies. We then describe applications of convergent and sequential mixed-methods designs to epidemiologic concepts including confounding, mediation, effect modification, measurement, and selection bias. We provide concrete examples of how epidemiologists can use mixed methods to answer research questions of complex bio-socio-cultural health outcomes. We also include a case study of using mixed methods in an observational study design. We describe how mixed methods can enhance how epidemiologists define underlying causal structures. Our alignment of mixed-methods study designs with epidemiologic concepts addresses a major gap in current epidemiology education - how do epidemiologists systematically determine what goes into causal structures?