Qual and quant are so divided these days—by academic discipline, language, communities of practice, job titles. Too often, quantitative research is conflated with data science (or vice versa), and data science with optimization algorithms or simply engineering. In many organizations, being “data-driven” tends to define “data” with a narrow conception of enumeration and (mis-) conceptions about the kind of evidence that is suitable to act on.
This tutorial critically examines this territory and move beyond it, empowering ethnographers to develop more interdisciplinary programs of inquiry. First the instructors review fundamentals of quantitative research and provide tools ethnographers can use to evaluate its quality and validity. Then they examine constraints and barriers to quant/qual collaboration, including time, funding, values, epistemological conflicts, organizational silos, and more. Finally, using core principles that underlie...
This paper argues that ethnographers can gain increased agency in data-driven corporate environments by increasing their quantitative literacy: their ability to create, understand, and strategically use quantitative data to shape organizations. Drawing on the author's experience conducting strategic user research at a technology company, the paper explores how the ability to engage with quantitative data can increase ethnographers’ independence and autonomy within organizations, and can also up-level the role and value of qualitative research. The paper also explores how a deep familiarity with quantitative data can enable ethnographers to imbue quantitative data itself with new forms of agency, and can ultimately give ethnographers the tools to change institutions from within. With a greater understanding of how quantitative data is made and used, ethnographers can ensure that data is collected in representative ways, point out the limitations of existing metrics, and argue for new ways of measuring and...