Collaborative Data Analysis

Flick, U. (Ed.). (2013). SAGE Handbook of Qualitative Data Analysis. London, GBR: SAGE Publications Ltd. Retrieved from http://www.ebrary.com.

Collaborative Data Analysis

  1. It is common to work in a team, but researchers usually work in parallel and fail to integrate multiple points of view.
    1. Collaborative data analysis: “processes in which there is joint focus and dialogue among two or more researchers regarding a shared body of data, to produced an agreed interpretation” (p.79) – I think dialogue will be important for us, because we can accomplish more thinking both together and individually, rather than just dividing and conquering the work we have to do.
    2. The finished produced should be a product of a combination of different perspectives.
      1. What are the backgrounds of my co-researchers? How do we allow ourselves to work efficiently, but contribute to each other? How do I help the whole team feel comfortable sharing their perspectives? How do I manage our limited time so that we can get to the important thoughts quickly?
      2. Dimensions
        1. I am insider/they are outsiders (in terms of collection)
        2. Interdisciplinary – We have different backgrounds even though we are all in IP&T now.
  2. Why Collaborative analysis is an interesting methodology (Perspectivism)
    1. Perspectivism – “all knowledge is relative to a point of view and an interest in the world” (p. 80). The most important thing about knowledge is whether it is effective in a situation.
    2. It is important to analyze from different perspectives, but very difficult to remove ourselves from our own perspective.
  3. Potential methodological benefits – finding coder agreement/constructing new ideas through collaboration.
    1. Inter-code reliability (only makes sense when aiming for representativeness. Does not increase validity, two codes might just happen to agree). – we probably won’t do this, but I think it will help us think critically to talk about the different ways we code things.
      1. having an auditor lends accountability.
      2. Inter-coder reliability statistics
    2. Incorporating rich local understandings (language, culture, history)
    3. Perspective-Transcending Knowledge (learning from other researchers)
    4. Reflexivity – collaborators with other perspectives can help us be reflexive – I hope we are able to do this in our discussions.
    5. Useful Knowledge – If the goal is to help the public, it makes sense to involve a possible beneficiary in the analysis. Helps to narrow communication gaps and interest others in findings.
  4. Models of team organization
    1. Hall et al. iterative collaborative analysis process (6.1) – “coordination through mutual adjustment” (p. 86) not centralized decision making. Everyone is in charge of part of the data, coordination early on informs individual work later). – I think I’ll work these steps in to my plan for this project, so that we can maximize the usefulness of our time and energy working together.
      1. Team building (understanding goals)
      2. Reflexivity (surfacing existing presuppositions)
      3. Contracts (formal agreements over roles)
      4. Individual analysis
      5. Pairs Compare
      6. Full team analysis
      7. Individual synthesis (tentative explanatory frameworks)
      8. Full team debate (critique and develop framework)
      9. Individual writing (each with various responsibility)
      10. Individual feedback (circulate drafts)
    2. Pairs – we are a group of three, but I wonder if rotating pairs might be useful, but not too time consuming for my co-researchers.
      1. Insider/outsider
      2. Co-research – One research with expertise in the subject, and one with expertise in research
      3. Loose team research – everyone does their own thing, combining as necessary
  5. Typical Challenges
    1. Practical
      1. Geographical distance, not rewarded by institutions, time consuming organization, conflicting schedules
      2. Need to have clear and explicit requirement at formulations. – I think we have a good start here with our mentor, and I don’t want to make it too formal, but it would probably be good to hash some of this out in more detail in the beginning.
    2. Identity Challenges – people can feel defensive of their discipline or themselves as researchers. Unique identities should be treated as useful tools, not something to be feared.
    3. Challenges to Open Debate – differences in many dimensions of status status Everyone should be encouraged to contribute.
Advertisements
This entry was posted in Qualitative and tagged . Bookmark the permalink.

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out /  Change )

Google+ photo

You are commenting using your Google+ account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s