BEYOND TEXT: USING ARRAYS TO REPRESENT AND ANALYZE ETHNOGRAPHIC DATA

Abstract

Recent methodological debates in sociology have focused on how data and analyses might be made more open and accessible, how the process of theorizing and knowledge production might be made more explicit, and how developing means of visualization can help address these issues. In ethnography, where scholars from various traditions do not necessarily share basic epistemological assumptions about the research enterprise with either their quantitative colleagues or one another, these issues are particularly complex. Nevertheless, ethnographers working within the field of sociology face a set of common pragmatic challenges related to managing, analyzing, and presenting the rich context-dependent data generated during fieldwork. Inspired by both ongoing discussions about how sociological research might be made more transparent, as well as innovations in other data-centered fields, the authors developed an interactive visual approach that provides tools for addressing these shared pragmatic challenges. They label the approach "ethnoarray" analysis. This article introduces this approach and explains how it can help scholars address widely shared logistical and technical complexities, while remaining sensitive to both ethnography's epistemic diversity and its practitioners shared commitment to depth, context, and interpretation. The authors use data from an ethnographic study of serious illness to construct a model of an ethnoarray and explain how such an array might be linked to data repositories to facilitate new forms of analysis, interpretation, and sharing within scholarly and lay communities. They conclude by discussing some potential implications of the ethnoarray and related approaches for the scope, practice, and forms of ethnography.

Keywords: computational ethnography; computational methods; data analysis; ethnography; mixed methods; representation; transparency; visualization.

Figure A1

An ethnoarray showing factors that affect trips to medical clinics. Data from The End Game: How Inequality Shapes Our Final Years (Abramson 2015).

Figure 1

Microarray based on gene expression profiling data from 337 breast samples (in columns; 320 tumors, 17 normal tissues) and approximately 1,900 genes (rows). Source: Prat and Perou (2011).

Figure 2

A single paragraph from the transcript of a Cancer Patient Deliberation Study patient interview, coded as an ATLAS.ti data set.

Figure 3

An ethnoarray based on data from 10 participants in the Cancer Patient Deliberation Study. The “Key to Figures 3–6” on p. 288 provides additional information regarding the domains, measures, and color assignment.

Figure 4

An ethnoarray based on data from 10 participants in the Cancer Patient Deliberation Study, with baseline (T1) and follow-up (T2) data. The “Key to Figures 3–6” on p. 288 provides additional information regarding the domains, measures, and color assignment.

Figure 5

A sorted ethnoarray based on data from 10 participants in the Cancer Patient Deliberation Study. The “Key to Figures 3–6” on p. 288 provides additional information regarding the domains, measures, and color assignment.

Figure 6

A sorted ethnoarray with selected quotations from linked data. The “Key to Figures 3–6” on p. 288 provides additional information regarding the domains, measures, and color assignment.

Key to Figures 3–6

Overview of domains, measures, and color assignment.