To the Editor:

Mixed methods is an underutilized and hidden tool in the causal inference toolbox. Mixed methods is poised to help study complex bio–socio–cultural health outcomes,1 yet most epidemiologists only use quantitative methods to address their research questions. We have previously discussed why it is worth taking a mixed-methods approach and explained how mixed methods can help strengthen various aspects of epidemiologic studies.2 To complement this publication, we created EMICS (Epidemiology and Mixed Methods Informing Causal Studies), an online tool (https://cumc.co1.qualtrics.com/jfe/form/SV_578kLV1tRlkxfTw) to assist epidemiologists in designing mixed-methods observational studies. The web tool is hosted on our website, https://www.irvinginstitute.columbia.edu/mixed-methods, which has accompanying information.

While the tool’s name is an acronym, it is also in reference to a central facet of anthropology – the emic view, which signifies the insider, on-the-ground perspective of those under study.3 Often times epidemiologists bring their etic – or outsider – view to a study and what is often missing is the perspective and context of the population under study. Integrating qualitative methods into quantitative epidemiologic studies helps balance the etic and emic view, ultimately strengthening causal inference.2

The EMICS tool prompts responses that follow an underlying algorithm (Figure 1) to recommend the best-fitting mixed-methods design based on our prior framework.2 The tool uses existing questionnaire-based software to create a user-friendly point-and-click interface; thus, no programming is necessary. The questions fall into three sections:

FIGURE 1.:

Underlying EMIC Tool logic.

Thinking through causal structure: The users input their research question and exposure and outcome variables. Then, either they can design a mixed-methods study to build the rest of their causal diagram or they can input other key variables such as confounders, mediators, and modifiers from an existing causal diagram. If users choose the former, they are fast-tracked to the exploratory sequential design, followed with an option to design a mixed-methods study with an existing causal diagram. Improving measurement: Users drag and drop from a list of quantitative and qualitative methods to measure each variable in their causal diagram. Users can measure each variable using either quantitative and qualitative methods or both. Establishing order: The tool asks the user to think through the order in which to measure each variable in their study design. For studies when variables can be measured at the same time, a concurrent mixed-methods design is needed. The tool helps the user determine if they need an embedded or convergent concurrent design by asking when they will mix their quantitative and qualitative data. If they mix during analysis and interpretation, then they will need a convergent design. If they will only mix during interpretation, then they will need an embedded design.4 If the variables are not measured at the same time, then the tool asks if the user intends to use qualitative or quantitative methods first. This is a key question that helps determine whether the user needs explanatory or exploratory sequential study design.4,5

This letter officially launches EMICS version 1.0, which we plan to further develop and evaluate. Because the tool uses survey software, we can collect responses from users on the backend and use these data to inform future iterations of the tool. We provide fast-track options for epidemiologists choosing to build new causal diagrams and to develop a survey since we assume these are the most common needs. Over time, we will supply more shortcuts based on the most common uses. We do not intend for the tool to train epidemiologists in qualitative methods. Rather, we envision this tool helping epidemiologists to design mixed-methods studies and then convene teams with the relative quantitative, qualitative, and content expertise to conduct them. Diverse teams make better science6 and we hope this tool can spur the creation of more interdisciplinary teams to address complex bio–socio–cultural public health problems.

REFERENCES 1. Ben-Shlomo Y, Kuh D. A life course approach to chronic disease epidemiology: conceptual models, empirical challenges and interdisciplinary perspectives. Int J Epidemiol. 2002;31:285–293. 2. Houghton LC, Paniagua-Avila A. Why and how epidemiologists should use mixed-methods. Epidemiology. 2023 March 1;34(2): 175–185 3. Headland TN, Pike KL, Harris M (eds). Emics and Etics: The Insider/Outsider Debate. Sage Publications, Inc; 1990. 4. Zhang W, Creswell J. The use of “mixing” procedure of mixed methods in health services research. Med Care. 2013;51:e51–e57. 5. Creswell JW, Plano Clark VL. Designing and Conducting Mixed Methods Research. 3rd ed. SAGE Publications; 2018. 6. Hofstra B, Kulkarni VV, Munoz-Najar Galvez S, He B, Jurafsky D, McFarland DA. The Diversity-Innovation Paradox in Science. Proc Natl Acad Sci U S A. 2020 Apr 28;117(17):9284–9291.