Collaborate – Think Critically: Learn from researchers who have been in your shoes – use their examples and experiences to explore how their ‘lessons learned’ can improve your own research approach. Take it one step further with additional thought-provoking questions online.
3.1: Choosing qualitative methods
Benton, A. D., Androff, D. K., Barr, B.-D., & Taylor, S. (2012). Of Quant Jocks and Qual Outsiders: Doctoral student narratives on the quest for training in qualitative research. Qualitative Social Work, 11(3), 232–248.
In this paper, Amy Benton and others present personal narratives of four current and recent doctoral students who have incorporated qualitative methods into their research. The themes that emerge from these narratives include early exposure to qualitative methods and a commitment to methodological pluralism, as well as experiences with encountering biases, additional costs, and the challenges of translating the methodologies of other disciplines. Although this paper focuses on American social work PhD students, it has relevance for all beginning qualitative researchers.
What made you decide to use qualitative methods? Which, if any, of these personal narratives applies to you?
What facilitators and obstacles have you encountered in your qualitative research? Which of these have you found to be most influential and why?
3.2: Learning from stumbles
Following Thomas Lister’s account, list three occasions where your research seemed to stumble.
- What did you learn from these stumbles?
- How would you do things differently in the future?
Lister also emphasises that the research process is not linear and you may go backwards to revise previous steps. Which tasks are you tempted to skip, and which parts of the research process do you find most rewarding?
3.3 Beginning a research project
What happens in the early stages of a student research project? In this fascinating article, based on their own supervisor-student relationship, Li and Seale examine the kinds of problems that can arise in analyzing your data and how these problems can be overcome.
List any of these problems and solutions which apply to your own research project
In particular, how would you address issues such as overinterpretation of evidence and struggling to code your data? How can your supervisor help you with this?