Having outlined a theoretical alternative to current structures of student and staff thinking about the relationship of theory, method, and data, in the following section attention is given to the ways in which the actor-networks presented in this account could be altered to cultivate this different understanding. Here two broad areas for change will be discussed: strategies for enrolling data into these actor-networks, and secondly, approaches to strengthen QM(s) link to theory.
In order to change impressions of QM(s), ANT would suggest the building of new links and networks. However, enrolling the missing actor, data, into these networks represents more than simply bringing data into the learning-teaching environments.
Getting students’ hands on with data has long been framed as a way of engaging students with QM(s) (Neumann et al., 2013; Mvududu, 2005). In all the modules observed as part of this study students were ‘hands on’ with data, which in many cases was real, gathered data not example datasets, yet students often commented that qualitative methods were more ‘hands on’ with the data than QM(s). This closeness of qualitative methods is often justified by appealing to methodological differences – with QM(s) acting as stronger mediators and making use of greater abstraction – and the assumed interaction
qualitative researchers have with research participants. However, differences can also be observed when examining the performances present within the learning-teaching environments.
In many of QM(s) learning-teaching environments data was pacified, pre-selected to generate results, and pre-packaged to be fed to QM(s) by students. Across the modules observed students and staff spent little time getting to know a dataset. Two notable exceptions to this were a second year Criminology module – where a series of lectures covered in detail the background of the survey data used in the workshops - and a first year Economics module – where students were exposed to large datasets which required cleaning before analysing. In one of these workshops, where students were cleaning the data, the lecturer explained the value of getting to know the data as follows: (see next page)
The lecturer asks if there is anything else that the student’s have noticed about the data set. No one says anything. The lecturer points out that the salary is just called salary, but whose salary is it? As that would make a difference to what it means. He explains that in this case it’s CEOs salaries.
(Fieldnote: Economics, 1st year UG workshop, QM(s) module)
In current learning-teaching environments the voice of the QM(s) techniques dominates. Lectures are arranged around a sequence of different techniques being covered each week, with students assessed on their ability to do these techniques. To begin to get students more ‘hands on’ and closer to the dataset the voice of Data needs to be brought into the learning-teaching environments, as it is only through the hybrid QM(s)-Data actor that meaning about the world is solicited.
In addition to repositioning data, the second area for change lies in strengthening QM(s) link to theory in the learning-teaching environments. As explained earlier this link is vital for integrating QM(s) into disciplinary knowledge structures. However, as well as relating to disciplinary themes, understanding the philosophical foundations of QM(s) is likely to become increasingly important as different approaches, outlined in Section 7.2.2, gain popularity. Cultivating an understanding that QM(s) draws on different philosophies and are used differently by different theories, however, is not simple. This is the kind of problem usually faced by qualitative methods modules (Hein, 2004). In this study, as described in one handbook, different qualitative approaches were presented to students as different ways of addressing specific research questions:
This lecture concentrates on the role of ‘asking questions’ in qualitative research. Using case studies from research, the lecture will explore the different ways in which qualitative research questions can be used, and identify similarities and differences to the types of questions that generate quantitative forms of data.
(Geography, 1st year UG research methods module handbook, qualitative
lecture description) In the one qualitative methods module observed as part of this study, a similar approach was taken, with different qualitative approaches being presented as different ways of addressing a central research question, which was used and adapted throughout the module: (see next page)
The lecturer describes the thematic analysis as reducing and making sense of the interview. He clicks forward to a slide with the research question they’re
investigating on and a description of the aim to today’s session as being to identify an answer to that question. […] He continues defining epistemology, and explains that it has a greater importance in qualitative research than in quantitative research. He describes there as being a continuum from social constructivist perspective to a realist perspective, and that the position you take affects what can be said from the data. […] The lecturer refers back to the research question and says that it adopts a realist position assuming what people say can be taken to refer to what they feel/think.
(Fieldnote: Psychology, 2nd year UG lecture/practical, Qualitative methods)
Here, then, it was not just that method provided translations of data, it was emphasised that each method provided its own translations. In this setting, qualitative methods were not valorised based on their role as transferable employment skills or by emphasising the presence of words all about us (common justifications provided within QM(s)
classrooms (i.e. Paxton, 2006)), instead they were valued through their specific abilities to provide answers to certain questions.
Applying this to QM(s) learning-teaching, in strengthening QM(s) link to theory, QM(s) become another method, not a different set of techniques that are more valuable if applied (as argued for by Onwuegbuzie & Leech (2005)). Through encouraging an appreciation for the multiplicity of QM(s) – not just inferential testing – and the value of QM(s) as answering a variety of research questions the adoption of QM(s) by new users may be encouraged.
Strengthening this link could be achieved in various ways, including increasing attention on research questions not null hypotheses (as remarked upon by Andrews and Baguley (2013), embedding QM(s) into content modules, to enable a greater variety of QM(s) to be introduced to students, or through problem-based learning where discussion over the contributions of QM(s) is encouraged (similar to solutions advocated by Dobni and Links (2008) and Folkard (2004)).
8.3 Conclusion
In this chapter, the various sides of QM(s) characterised in each of the proceeding chapters have been brought together. Here it was suggested that QM(s’) unified
character, present within the literature, is achieved not through the similar techniques or narratives within modules or disciplines, but through boundary work that served to unify QM(s) based on reinforcing its difference to other kinds of knowledge. Through
cultivating its own language, notation, module structures, and assessments, QM(s) were able to enrol actors into its own actor-networks.
However, while this process of differentiating QM(s) served to unify its character and enrol users, it created problems when attempting to integrate QM(s) with disciplinary knowledge. For students, QM(s) was characterised as having a one-way relationship to theory, where QM(s) was broadly understood as a way to test theory. In contrast, for staff, this relationship was represented as two-way, with theories generating methods, and vice versa. For staff, QM(s) was understood not as distinctly separate from the discipline knowledge, but as a fundamental part of it, occupying a position of similarity to other methods.
Having discussed student and staff representations, an alternative representation of the relationship between theory and method was proposed. Given the importance of data in controlling QM(s) character, identified within Chapters 5 and 6, this alternative
representation included data as key part of this relationship.
This chapter ended by outlining some of the ways in which the actor-networks presented in this thesis could be manipulated by reinforcing QM(s) link to data and theory. Overall, the drive to raise QM(s) standards should not become the key reason for valuing QM(s). While research is needed on how to learn/teach these concepts increased attention should given to understanding how to value the research questions that QM(s) are skilled at answering, and how to foster an appreciation for QM(s) ability to transform data in different ways. Furthermore, instead of framing the problem as students not being engaged with QM(s), greater attention should be given to considering why certain topics, such as representative and bias, appear to be more easily taken up by students and transferred to other methodological approaches.