Reanalysis, collaboration, data transparency and sharing, and online communities
Murphy et al. (2021) describe two challenges to ethnography. The first challenge concerns the ubiquitous presence of new technologies, such as smartphones and social media platforms. These technologies have opened new ways of data recording and collection, and they have raised new questions about data protection and privacy. Moreover, since significant social interactions are happening on social media platforms, the platforms are instrumental to understanding many modern social phenomena.
The second challenge concerns the growing demand for qualitative research to uphold their standards of rigor, of the sort that used to be demanded from quantitative methods, i.e., to demonstrate representativeness and replicability of qualitative research and to assure data transparency. Some scholars recognize that meeting this challenge is essential for cross disciplinary collaboration and for the capacity of their research to contribute to the larger body of knowledge. Others are wary that such demands of representativeness and replicability are contrary to the essence of ethnographic methodology, which is interpretive in its nature, and in which the researcher acts as an instrument of inquiry. Additionally, demands of transparency raise concerns about confidentiality of data.
Murphy et al. (2021) argue that, although ethnographic data cannot be replicated, transparency of data and their availability for reanalysis should be viewed as equivalent to standards by which we judge a) rigor of ethnographic research and b) its potential to contribute “to theory building and the accumulation of empirical knowledge about the social world.”
The authors suggest mechanisms and describe barriers to transparency of data in four stages of ethnographic inquiry: 1) recording and collecting the data, 2) anonymizing, 3) data verification, and 4) destroying, preserving and sharing data.
Recording and collecting data
Ethnographers engagement of technologies described by Murphy et al. (2021) adds to traditional “face-to-face interaction” (p.46) is twofold: 1) use of technological devices for data recording and 2) study of “what people do online” (p. 46) through either active involvement or passive observation.
Anonymizing
Mechanism of anonymizing data (aimed to protect participants' privacy) can hinder data transparency and reanalysis. The authors conclude that the level of anonymizing used in a study “will vary given the context of population” (p. 49). The authors emphasize that ethnographers have to be transparent with their participants about the level of anonymity accepted in the study in question.
Data Verification
Qualitative scholars view participant accounts, such as narratives and questionnaires, as windows into the ways participants make meaning of their experiences or the ways people want their experiences to be understood by researchers. Thus, participant accounts cannot be regarded as factual truth. However, as Murphy et al. (2021) explain, requests for verification of participant accounts come from other academic fields, such as journalism and jurisprudence. This criticism motivated the debate about the extent to which verification of data can be used in ethnography. The authors review literature that tackles such ideas as corroboration of accounts from multiple participants, collecting diverse sources of evidence, and “checking stories for consistency” (p. 51). Also, debate is ongoing regarding the need for data verification by third parties or by external reviewers. Here the concern is that fact-checking might conflict with goals of the ethnographic inquiry in those cases where the researcher is trying to capture how reality is constructed by the participants.
The authors conclude that, although there is no clearly defined solution to the concern of verification, ethnographers should be clear and transparent about the way their data was collected and recorded.
Destroying, preserving, and sharing data
New technologies made it possible to store digitized qualitative data in “online repositories” that makes data available for sharing and reanalysis. This option is in contrast to the commonly accepted convention in which field notes are destroyed after some time, to assure participant anonymity and confidentiality. The authors argue that merely destructing field notes is insufficient for protecting confidentiality because data often include other documents and artifacts. Instead, digitizing data opens new possibilities for data protection by such mechanisms as controlled access, confidentiality agreements, and varying the level of access. The authors also discuss the issue of participant consent when data are shared and eventually used for purposes other than participants had agreed to.
What questions and types of analysis does this text suggest for your own work?
One of my interests is models of midwifery care in the US. The paper by Murphy et al. (2021) tackles two problems essential for my research. First is the problem of creating the platform for collaboration between researchers and three groups of participants: midwives, medical doctors and women under midwifery care. I hope to have the data available to participants and researchers for reanalysis. Second, I plan to collect different types of data, including those from digital platforms and also audio and video recordings that capture interactions of women and midwives. This is because I believe that the nature of such interactions is crucial for understanding the principles of midwifery care.
Although all concerns described in this paper are important for my research, I am especially interested in the problem of data verification. Professionally, nursing and midwifery are situated on the casp separating biomedicine and social studies. Nursing and midwifery scholars need to find methods for generating knowledge generalizable across disciplines. Achieving such generalization without sacrificing rigor of quantitative methods or richness of qualitatively captured data is a significant challenge.