ABSTRACT

This chapter addresses how Legitimation Code Theory (LCT) can be used to overcome this divide in qualitative research. Specifically, we discuss how to develop an ‘external language of description’ or translation device between theory and data. We ground our discussion in the example of a major study that enacted the LCT concepts of specialization codes (Chapter 1, this volume) to explore how constructivist pedagogy shapes the educational experiences of students (Chen 2010). First, we elaborate on Bernstein’s notion of an ‘external language’ – its rationale, its role in research, and ways it has been interpreted – to clarify the nature of a ‘translation device’. Second, we introduce the study we use to exemplify how such a device can be evolved. Third, we analyse the evolving process of that study. There are few published examples of ‘external languages’; there is even less public discussion of how they can be developed. Publications typically reveal the products of research; here we reveal the process as well as the product, to make explicit part of the craft of LCT (Chapter 1, this volume). We analyse the study as an unfolding narrative, focusing on how relations between theory and data were negotiated in the development of an external language of description. Last, we introduce the resulting translation device, discuss how it enables dialogue between theory and data, and consider the nature of the process more generally. We should emphasize that this chapter is intended to be neither a definitive guide nor a template for enacting LCT. More widely, it aims neither to normatively define how theory and data should be related nor to restrict diversity in how this can be achieved. As we discuss, there are several interpretations of ‘external languages’, and, as other chapters in this volume illustrate, there are many ways of using LCT and developing translation devices. Rather, by focusing in detail on one study we hope to shed some illustrative light on how the framework can be used in qualitative research to generate explanatory power through fostering dialogue between theory and data.