Data management & analysis
1 Session overview
The basic presumption behind this session is that it is best to think about analysis before starting data collection, since failure to do so can leave a researcher with several major problems. Indeed, a common difficulty faced by new researchers is that they return from data collection and have little idea of what to do next; and when they do decide on a plan for analysis, they discover that they do not have all the material they need or have too much material which is of little relevance to the project.
If you want to address all three objectives outlined below, we suggest a whole day in total for this session. If you focus on the first two objectives and restrict yourself to general principles, you could manage with a long half-day.
Objectives of the Session:
The purpose of this session is to discuss three main topics:
- ‘Basic housekeeping’: how qualitative data should be stored, managed and distributed
- The essentials of qualitative data analysis
- The strengths and challenges of using a computer-assisted qualitative data analysis software (CAQDAS) package to help code and access qualitative data.
Approaches to the Session
How you deal with the first objective depends on the kind of workshop you are running:
- If this is the beginning of a project, or if you want to run this session after a few interviews in such a project have been carried out, then you might want to use this opportunity to go into the details of your project and begin to take decisions. Or,
- If this is a general training session, and your groups includes solo researchers as well as people likely to work in teams, you might want to discuss the general principles and encourage people to share their ideas of how they will resolve basic management issues in future large projects, PhD or other degree projects etc.
The second objective is crucial: we would expect you to include a session of this kind in any course.
How you deal with the third objective again depends both on your participants and on local constraints:
- If you already have purchased or have access to a specific CAQDAS program, then you might move directly to orientation with respect to that program; alternatively, you might want to reserve most of this material to a time nearer when the analysis will actually take place (because people rapidly forget how to carry out basic tasks in a new program, unless they get quite a lot of hands-on practice immediately).
- If your group expects to use a CAQDAS package, but has not yet decided which one, or has not yet got access to the package, you might restrict your discussion to a general overview, and offer some advice on choosing a package for those who have yet to make their minds up.
- If there is little prospect of people having access to a CAQDAS package in the near future, or if you do not feel that it is appropriate to use such a package, then you might be better to focus only on the first two objectives. Managing a small project without using a CAQDAS package may make most sense; but the larger the project, the more difficult data management without a package becomes.
2 Management of data
Time: 30-60 minutes
Preparation: This depends on whether you are training a group of researchers in an existing project or not. If so, you should have start with your own ideas about managing the data you collect. But you’ll certainly need a flip-chart, black- or white-board or computer plus projector for the brainstorm sessions.
- Small groups (2-3 people in each) to come up with three issues that need to be considered when thinking about the management of data (10 minutes)
- Feed-back session in which issues are listed (5 minutes)
- Full-group discussion of possible solutions to the more important issues (from 15 minutes to 45 minutes).
If you have a project which needs to set basic ground-rules with respect to these data management issues, you might like to prepare a handout that can be given out at this point.
Translation is often a big issue for projects that have limited resources. There is a handout on issues of translation used in the previous session which discusses some general principles. But in practice hard decisions may need to be taken about whether (or how much) to transcribe recorded interviews or focus group discussions; when (and how much) to translate; and who should translate, as well as what kinds of quality controls will be introduced to ensure the data are good enough for coding and eventual possible citation.
3 Introduction to qualitative data analysis
Time: Allow about 2-3 hours (half a day) for this part of the session. Many people find qualitative data analysis the hardest thing to understand, and the more they engage with practical examples, the better.
Preparation: You’ll need a flip-chart, black- or white-board or computer plus projector for the brainstorm sessions.
You might want to bring in a couple of books with indexes, to show people the similarities between a familiar exercise – looking something up in an index – and basic descriptive coding.
A popular non-computer-based coding method is the‘cut and paste’ method. Here the researcher cuts and pastes quotes from interviews under various themes/topics on a big sheet or chart paper. Each chart paper denotes a single theme/topic and all the quotes from various interviewees can be arranged in columns. When reading across the sheets you can understand the variation in responses to a particular question or around a particular theme, and when reading down the sheet you see how an individual’s responses fit into a larger picture of their life).
If you have an example of your own, it would be worth bringing it in and talking participants through what you did. Alternatively, you could create an example, using invented quotes, cut and pasted onto large sheets of paper or card.
You’ll also need a sample (perhaps one page) from an interview transcript, fieldnotes or other form of qualitative data, one copy for each participant and an electronic version that can be shown on a projector to help in the feed-back session.
You can use the PowerPoint Presentation on Qualitative Data Analysis as the basis for this session. We suggest that you break up the flow with a series of full-group brainstorms or small-group tasks. For example:
- Ask people what they see as the main differences and similarities between the analysis of quantitative and of qualitative data.
- Draw on the experience of the group in making sense of unfamiliar places – noticing, collecting and thinking are processes we all use.
- Use the sample extract as a basis for getting people to generate codes, and to think about the differences between descriptive and analytic codes.
4 Basic introduction to the use of computers to help with qualitative data analysis
Time: 45 minutes
Preparation: This depends on whether you are training a group of researchers in an existing project or not, whether you have the need, desire (and funds) to use a software package, and if so, whether you have already selected and purchased a package that you want everyone to be familiar with.
But you’ll certainly need a flip-chart, black- or white-board or computer plus projector for the brainstorm sessions.
If you want to run this just as a general introduction to CAQDAS (Computer Assisted Qualitative Data Analysis Software), we suggest you use the attached Presentation on CAQDAS with worked examples from projects with which you are familiar.
There is a Handout on CAQDAS : you could give this out at the end of this session
There is a comprehensive web-page that covers many issues with respect to different versions of qualitative data analysis:
This site includes a summary statement comparing the various CAQDAS packages available in 2004, and links to other review pages:
We are not recommending any particular CAQDAS package: to our minds, each has its strengths and weaknesses. For further information on Computer Assisted Qualitative Data Analysis, see CAQDAS.
Singal, N., and Jeffery, R. (2008). Qualitative Research Skills Workshop: A Facilitator's Reference Manual, http://oer.educ.cam.ac.uk/wiki/RECOUP, Cambridge: RECOUP (Research Consortium on Educational Outcomes and Poverty, http://recoup.educ.cam.ac.uk/). CC BY-NC-SA 4.0. (original page)