Qualitative data analysis is a focus on MethodSpace for April. This week we will look at data analysis in the context of specific methodologies, starting with ethnography.
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These open access articles offer perspectives on data analysis in ethnographic research.
Jerolmack & Khan (2017) discuss theoretical approaches to reasoning. Kurtz et al. (2017) talk about systematic thematic analysis of blogs. (Pilkington (2017) explores meta-ethnographic synthesis. Rankin (2017) suggests that researchers incorporate analytic thinking from the outset of research design. Pool (2017) examines issues related to accountability for data quality in ethnographic studies.
The Analytic Lenses of Ethnography (Jerolmack & Khan, 2017)
Abstract. It is almost axiomatic that there are two contrasting theoretical approaches to ethnography: induction and deduction. However, regardless of whether ethnographers build theory from observations (induction) or use observations to test theory (deduction), they approach the field armed with one or more particular analytic lens that leads them to focus on a distinct thread of the social fabric. We outline the suite of analytic lenses that typify ethnography and identify eight ideal types. Though not mutually exclusive, they can be usefully grouped and contrasted accordingly: (1) the level of explanation: micro, organizational, and macro; (2) the subject of explanation: people and places and mechanisms; (3) the location of explanation: dispositions and situations; and (4) reflexivity. We specify the basic modes of analysis that typify each ideal type, trace their implications for how one selects units of observation, and demonstrate how these different ethnographic styles illuminate different dimensions of the social world.
Blogs as Elusive Ethnographic Texts: Methodological and Ethical Challenges in Qualitative Online Research (Kurtz, Trainer, Beresford, Wutich, & Brewis, 2017)
Abstract. Burgeoning online environments offer completely new opportunities for ethnographic and other forms of qualitative research. Yet there are no clear standards for how we study online texts from an ethnographic perspective. In this article, we identify barriers to the application of traditional qualitative methods online, using the example of a systematic thematic analysis of weight-loss blogs. These barriers include the influence of the technology structuring online content, the fluid nature of online texts such as blogs, and the highly connected and public nature of online identities, which may span multiple social media platforms. We discuss some potential approaches to addressing these challenges as preliminary steps toward developing a tool kit suited to ethical, high-quality online modes of ethnographic research.
The Verification of Ethnographic Data (Pool, 2017)
Abstract. Anthropologists are increasingly required to account for the data on which they base their interpretations and to make it available for public scrutiny and re-analysis. While this may seem straightforward (why not place our data in online repositories?), it is not. Ethnographic ‘data’ may consist of everything from verbatim transcripts (‘hard data’) to memories and impressions (‘soft data’). Hard data can be archived and re-analysed; soft data cannot. The focus on hard ‘objective’ data contributes to the delegitimizing of the soft data that are essential for ethnographic understanding, and without which hard data cannot be properly interpreted. However, the credibility of ethnographic interpretation requires the possibility of verification. This could be achieved by obligatory, standardised forms of personal storage with the option for audit if required, and by being more explicit in publications about the nature and status of the data and the process of interpretation.
Conducting Analysis in Institutional Ethnography: Analytical Work Prior to Commencing Data Collection (Rankin, 2017)
Abstract. Institutional ethnography (IE) is an innovative approach to research that requires a significant shift in researchers’ ordinary habits of thinking. There is a growing body of methodological resources for IE researchers however advice about how to proceed with analysis remains somewhat scattered and cryptic. The purpose of the first of a two-paper series is to contribute to publications focused exclusively on analysis. The aim is to provide practical tips to support researchers to shift their ordinary habits of thinking. This first paper outlines how this must happen at the outset of the research design. Analysis of the phenomenon under study commences as the research is being formulated. The approaches to analytical thinking outlined in this paper are based on my own IE research and also my experience working with graduate students since 2008. In this first volume of the two-paper set I provide a brief background to the method and direct readers to important IE resources. I outline three core methodological concepts: standpoint, problematic and ruling relations. I discuss how these concepts guide the early analytical thinking that is embedded in the research design and the critical analysis of the literature that is part of the process of analysis in IE. The second paper provides practical advice for working with data.
Related MethodSpace Posts
Jerolmack, C., & Khan, S. (2017). The analytic lenses of ethnography. Socius, 3, 2378023117735256. doi:10.1177/2378023117735256
Kurtz, L. C., Trainer, S., Beresford, M., Wutich, A., & Brewis, A. (2017). Blogs as elusive ethnographic texts: Methodological and ethical challenges in qualitative online research. International Journal of Qualitative Methods, 16(1), 1609406917705796. doi:10.1177/1609406917705796
Pilkington, H. (2017). Employing meta-ethnography in the analysis of qualitative data sets on youth activism: a new tool for transnational research projects? Qualitative Research, 18(1), 108-130. doi:10.1177/1468794117707805
Pool, R. (2017). The verification of ethnographic data. Ethnography, 18(3), 281-286. doi:10.1177/1466138117723936
Rankin, J. (2017). Conducting analysis in institutional ethnography: Analytical work prior to commencing data collection. International Journal of Qualitative Methods, 16(1), 1609406917734484. doi:10.1177/1609406917734484