Assignment 7: Finding patterns from interview-based data
This assignment will give you:
- Experience on qualitative analysis of interview material. You will analyse a subset of reports that were collected in last week’s assignment (i.e., A6).
- A chance to compare different interview techniques and their usefulness for qualitative analysis.
Following the research question (RQ) of Assignment 6, your task is to seek answers to the same question (i.e., “How should Instagram increase its support for self-branding?”) by analyzing interviews. The analysis process will borrow elements from the Grounded Theory method, which is a bottom-up data-driven analytical approach.
There is a lot of literature about Grounded Theory. Here are a few sources:
- Wikipedia: https://en.wikipedia.org/wiki/Grounded_theory
- Muller, M. (2014). Curiosity, creativity, and surprise as analytic tools: Grounded theory method. In Olson, J. S. and Kellogg, W. A. (eds.). Ways of knowing in HCI, pp. 25–48. Springer. The book chapter (and the rest of the book too) can be downloaded from Aalto Library: http://libproxy.aalto.fi/login?url=http://link.springer.com/10.1007/978-1-4939-0378-8
The task this week is to analyse a subset of data from your last week's interviews on Instagram, using qualitative methods and adopting some of the principles of Grounded Theory. The purpose is to answer the same RQ that was presented already last week.
Follow these steps:
- Familiarize yourself with Grounded theory by reading e.g. the material above.
- Download your dataset: Visit https://users.aalto.fi/~asalovaa/assignment7, enter your student ID, open the link provided, and download the contents from each link to your computer. All of you will have a set of 27 interviews to analyse. If you returned a report in A6, you will find your own report in your dataset.
- Open coding:
- Start by reading all the interviews one by one
- While you read:
- Try to get into a mindset of making discoveries from the data, e.g., getting you into thoughts that “it could be this way” or “x could be important here”
- Highlight parts (e.g., with your PDF viewer’s highlighting tool) that you find important and meaningful
- Write remarks about your own observations: for example, copy-paste parts from the PDFs to a separate document and add your remarks/observations next not them
- Develop “working hypotheses” about concepts and phenomena that could be important to the RQ or could be answers to it.
- Re-read interviews if you feel that you want to deepen or re-interpret some of your earlier observations.
- Axial coding:
- This stage consists of comparisons of your last step's open observations with each other.
- Categorise your observations together into larger concepts. Here it may help if you use Post-It notes or other small pieces of papers that you can sort and group together. Each piece may contain a one observation and information where in the data you can find the exact data again.
- Develop larger working hypotheses (“substantive theories” in the GT terminology) based on your data and observations.
- Identify similarities and contradictions between different interview contents.
- When you develop a new working hypothesis, seek to verify it by contrasting it with the rest of the content in PDFs. Copy-paste the appropriate parts (especially quotes from participants) to your document
- Summarize your answers to the RQ into 1-3 main ideas. Gather the interview quotes (and screenshots, if applicable) that represent these answers to your summary. This way you can validate the trustworthiness of your findings and also provide the necessary evidence for others.
The report contents
Structure your report into answers to these sections:
- What were your open codes and other early-stage observations? Provide these as a list that contains also explanations for the codes.
- What categories did you develop during axial coding? Provide these as a similar list.
- What are your answer(s) to the RQ and what interview quotes will provide empirical evidence to your answer(s)?
- Based on your experience from working with this kind of data, how would you describe a good interviewing technique vs a poor interviewing technique?
The deadline for this report is on Sunday 4 November at 23:55 and it will be returned here in MyCourses as a single PDF.