- 1. What is data analysis?
- 2. A grounded theory approach to data analysis
- 3. Processing your data in preparation for analysis
- 4. Coding interview transcripts and other data
- 5. Analysis in a collaborative context
- 6. Critically evaluating your data: reflexive data analysis
- 7. Summary
- 8. Feedback Survey
3. Processing your data in preparation for analysis
It is important to allow plenty of time to process the data that you collect in the field in preparation for analysis. One way to ensure the timely processing of data is to begin processing your data as soon as possible at the same time that you are conducting your field work.
Before analyzing data, it is useful to describe, classify, and connect the data in preparation for analysis (Kitchin and Tate, 2013).
- Description “concerns the portrayal of data in a form that can be easily interpreted” and includes situational context (e.g. the time and place of data collection) and contextual information (anything that may affect, influence, or shape data analysis, including non-tangible and subjective observations on intentions).
- Classification involved categorising and arranging data into groupings.
- Connection “is concerned with the identification and understanding of the relationships and associations between different classes” (Kitchin and Tate, 2013).
Processing interview data
As soon as possible after conducting interviews, you should transcribe your recorded interviews and notes/observations. You may be able to use voice recognition software for transcription – either with a purchased program, or with a free online copy. Check if your institution has a license with a particular program. Consider testing out a few before purchasing, but if you do choose to use a free, online program, find out if the program is collecting the information and storing it.
Allot yourself plenty of hours to transcribe recorded interviews: it can take anywhere between four and nine hours to transcribe one hour of recorded material.
(a) Creating transcripts
- Type up your material on a page leaving a wide margin in which you can put notations and ideas – these will eventually lead to codes.
- Have a running header on each page, with the project name and participant number.
- Use initials for names.
- Number your pages as well as every few lines.
- Use standard codes in your transcript. For example:
(.) = a slight pause
(…) = a slightly longer pause
( ) = transcriber could not hear / understand what was said
(that) = possible hearing
((laughs)) = author description
- Make a copy of the transcription to both your hard drive and an external drive.
- Do not include the personal identifying information of the interviewee or participant on the same file as the transcription – use pseudonyms or numbers to identify each participant.
- Each transcription should have:
- a title page;
- the number of the interview and the number of the session;
- the location;
- the time;
- the duration;
- the name of the interviewer and the transcriber (if they are different)
- any other important information.
(b) Annotating transcripts
Immediately after transcribing, annotate your transcript with notes, observations and connections to other data. Annotation is an informal coding strategy.
You can use your research diary and interview notes to refresh your memory and to help you with ideas of things to note.
See if your institution has access to textual analysis software; some universities and research centres also provide training on the software for researchers. If not, annotation, content analysis and coding can be done by hand by the researcher.
Consider these strategies to help with your annotation:
- Ask interrogative questions including: who, what, where, how, why;
- Consider a substantive checklist: how well does the data fit the themes of the research?
- Shift your focus: what details apply to the big picture, and which ones are little picture?
- Use keywords: emotions, attitudes, beliefs, behavior.
Once you have completed processing your data, by transcribing your interviews and connecting those conversations with annotations from your fieldnotes, it is time to code your data.
Watch this example of an interview conducted with Sarah Nandudu from the National Slum Dwellers Federation of Uganda, recorded at the Habitat III conference in Quito, Ecuador: https://youtu.be/Y_uxfHfKB0Y
Listen to it two or three times, and consider how you would begin to annotate this interview. What keywords do you notice in the responses from the interview participant?
As you listen a second or third time, can you begin to place any keywords together under any themes?
Here is a sample interview transcript template from the United Kingdom Data Archive: http://data-archive.ac.uk/media/136055/ukdamodeltranscript.pdf
Baxter, J. and Eyles, J., 1997. Evaluating qualitative research in social geography: establishing ‘rigour’in interview analysis. Transactions of the Institute of British geographers, 22(4), pp.505-525.
Gomez, B. and Jones III, J.P. eds., 2010. Research methods in geography: A critical introduction (Vol. 6). John Wiley & Sons.
Kitchin, R. and Tate, N., 2013. Conducting research in human geography: theory, methodology and practice. Routledge.
Lindsay, J., 2006. Techniques in human geography. Routledge.
Longhurst, R., 2003. Semi-structured interviews and focus groups. Key methods in geography, pp.117-132.
Peake, L. 2018. Presentation at the ‘Workshop in Urban Feminist Research: Ethnographic Research Tools’, Ramallah, Palestine, July 2018.
Schratz, M. and Walker, R., 2005. Collective memory-work: The self as a re/source for re/search. In Research as social change (pp. 51-77). Routledge.