Identify suitable projects and data sets.
The lapse of time since the creation of archived data sets makes it difficult to use them in this project. The researchers involved are unlikely to be able to remember the details of their analytic thinking. Consequently, we propose instead to approach PhD students and other researchers who are about to or have recently completed suitable projects and who may now be employed as researchers or lecturers. Such researchers will have details of the analytic process fresh in their minds and will have the contact details of participants who will be less likely to have moved, changed jobs etc.
The Online QDA project identified the major analytic approaches practitioners are using. The six exemplars to be produced will be drawn from grounded theory (the most popular approach), thematic analysis, narrative analysis, discourse analysis, phenomenological methods and ethnographic methods. Our survey also revealed the major discipline backgrounds of those using qualitative methods. This will be reflected by having at least one exemplar from each of the disciplines: education, sociology, psychology and anthropology.
Get licence and ethical permissions from the owners of the data.
We will seek universities’, researchers’ and their participants’ permissions to use their data in this novel fashion. Because the researchers will be still (or recently) engaged in their projects, contact with their participants will be simplified.
Work with researchers (e.g. by interviewing them) and collect any notes, memos, field notes etc. they can provide to
identify the analytic processes and thinking they went through.
We will examine the material, select samples from the cases and analytic notes (where appropriate), tidy them up and convert to electronic form if necessary. Selected interviews with researchers will be recorded for use in the RLOs.
Produce RLO material.
At the smallest level of granularity there will be examples (either in documents, still images or short video extracts) that can be used in lectures, student activities and class discussions. In the middle range there will be some complete exercises, combining illustrative material, narrative, simple tests, and self-assessment questions. Finally these units will be combined into a complete resource-based learning pack for each case, that will take the learner through all the key stages the original researcher used to undertake their analysis. This will mean that that the material could be used in blended learning; that is alongside face-to-face training sessions, self-directed learning or in resource based learning (Pailing, 2002).
Although the data, interpretative work and video will be different in each of the six case exemplars, there will be parallels in the stages and steps of the analytic processes. There will thus be scope for the development of Generative Learning Objects (GLOs) (Boyle, 2006). Most of the work on GLOs has been in the context of computing and natural science where there is an agreed knowledge base. However, some of the sections of the RLOs we plan to produce and their embedded exercises/tests will be similar in approach but different in data content. There is thus scope for the development of common structures and the project will examine the extent to which GLO ideas can be applied in the much more interpretative field of QDA. We shall collaborate with the RLO-CETL at London Metropolitan University.