Data analysis in ethnographic research
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Ethnographic research aims to observe and describe human societies and cultures. Data analysis in ethnography may be done for several reasons: as a method of qualitative data analysis, as part of the iterative process of fieldwork such as memo-writing, and strictly during the closing stages of the research. It is at this third stage that an ethnographer tries to make sense of the data collected and starts looking for patterns, meanings and explanations.
In the first stage of analysis data is explored in a descriptive way. In theory, this should lead to identification of problems that need more detailed investigation or that could be used as part of a hypothesis statement. Theoretically, this would eventually lead to the development of an hypothesis which could be tested through data collection again.
In the second stage, there is selective coding and categorising, where different concepts are isolated and relationships between them highlighted. The categories used for this stage are not mutually exclusive but instead form part of a continuum or network of related ideas. This is when theories arise that are then tested against the data.
The third stage of analysis is to do with making sense of the findings, which involves shaping them into a coherent whole that tells the story of what happened in the fieldwork. This is often called 'thick description', where sort order and patterns are identified. Theories may be relevant at this point but the aim of the ethnographer is to produce an accurate, vivid and detailed account of what happened.
The main methods for data analysis used in ethnographic research are: Memo writing; Diaries & Fieldnotes; Interviews and Focus Groups; Collation and Classification; Thematic Analysis; Discourse Analysis; Narrative Analysis; Phenomenological Analysis.
Data analysis in ethnographic research
Data analysis in ethnographic research is usually an ongoing process. Generally, four stages can be identified in the analysis of qualitative data:
Familiarization with the data: The initial stage is the familiarization with the data that happens during or immediately following fieldwork.
At this time, researchers typically look for patterns and attempt to begin making sense of them while they are still immersed in their daily lives within the study context. This requires immersing oneself in the data, both temporally and spatially. For example, when conducting research in the field, following the daily activities of study participants can help illuminate how social interactions unfold over time. The process of familiarization with the data during the initial stage may also include coding sections of text or image into fields, for instance by using a specific color marker to denote participants' emotions, or by creating columns in one's analytic spreadsheet.
At this stage, researchers are not yet bringing their preconceived notions of what will be found into play. Instead, data is initially interpreted based on the researcher's most immediate experiences with it. While many researchers may prefer to begin with open-ended transcripts or fieldnotes, it is unlikely that all data will be coded in this fashion. For example, researchers might consider coding words on posters they are examining for their potential meanings.
Identifying patterns: The second stage of data analysis includes the identification of stable patterns within the data that can be used for research purposes. This may involve moving back and forth between reading transcripts or coding larger pieces of data, such as text messages or interview transcripts. By the end of this stage, researchers begin to develop some tentative theories about their subject matter. Here, they are trying to identify consistent patterns within the data that might help explain why certain events unfolded as they did (e.g., What event led to the increase in participants using a particular tone of voice or vocabulary? Or, What event led to participants feeling comfortable enough with one another to use curse words?).
Testing theories: Researchers also begin testing their theories by returning to interview transcripts and fieldnotes.
At this stage, researchers are still moving back and forth between reading and re-reading data, while at the same time looking for patterns or recurring themes. It is also important to note that these stages are not linear, nor are they completely distinct from one another. After identifying stable patterns within the data, researchers can begin asking new questions about these patterns and moving back into familiarization with the data to expand upon their theories.
Drawing conclusions: At this phase of analysis, researchers begin to draw conclusions about what they have observed and test their assumptions as well as assumptions made by others. Researchers do this by applying the theory or theories they developed in the previous phase to specific cases within their data, such as an individual participant's voice inflections or a series of emails between participants. They may also compare and contrast different instances where the same pattern emerges to further test and refine their theories.
At this stage, researchers apply what they have learned from their data in a broader way than before. This may include writing up specific cases in more detail, comparing and contrasting different examples of the same phenomenon, or completing an extensive review of literature that explains how patterns observed in study participants' behaviors could be applied to other contexts. Data analysis at this stage helps researchers make sense of their findings and helps them decide which aspects of the data they should write about in a dissertation or thesis.
This is not an exhaustive list of all of the steps that can happen at each stage, but these are some basic examples of analytic processes that researchers may go through. In the interest of space, I may have left out some critical steps in the analytic process, such as turning data from one form to another or transcribing human-to-human interaction into a more widely accessible medium (e.g., writing interviews up as case studies).
- “Analysis” in “Ethnography Made Simple” - CUNY Manifold
- Analyzing Ethnographic Data - SAGE Research Methods
- Ethnographic Data Analysis - Computer Sciences User Pages
- Analyzing Ethnographic Data
- REFLECTIONS Ethnographic Content Analysis - public.asu.edu
- Data 101: Ethnographic - LibGuides
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I hope this article has given you some insight into the process of data analysis in ethnographic research, and how it can differ from other types of qualitative research.