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avoids the ‘Bad King John/Good Queen Bess’ approach to historical analysis and reporting of
data.
Building on the criticisms lodged at discourse analysts of having pre-empted outcomes and
using data to prove this, rather than enabling the data to ‘speak for itself’, is the mistake of
over quoting data or quoting data out of context or without proper explanation of context.
This is a potential fault that is avoided through the ways outlined here. The potential to select
phrases from the data and to not make the reader aware of their context is avoided as this
research agrees that it is important to let the reader understand the wider context of the
content. Antaki et al. describe this as failing as “…it leaves the text behind” (2003, p. 11),
and Widdowson as “…disregard(ing) the information that is inconvenient” (1998, p. 147).
This does, however present some difficulties, given the large volume of text available for
analysis. If the focus of the research was a conversation analysis, it would be easier to present
the transcript of the conversation, and then to carry out the analysis required. However, with
so much data, it is unreasonable to expect all of it to be included in the research, or to even
form an Appendix. It is agreed that it is important to let the reader view the text in its entirety
and to explicitly draw on it; however doing this every time is not realistic, given the large
volume of data. Instead, this project avoids the criticism of quoting and subsequently
analysing out of context by including a selection of full raw data material as a sampling of the
analysis that then takes place, so that common principles of analysis that have been used, in
adherence to the approaches established, which also then maintains the integrity of the
research (see Appendix D). In this way, the data can be seen within its discursive context,
made explicit. This openness is, as Chenail points out an important step in establishing
trustworthiness and reliability of data, as it “allow[s] the reader to see what they can see in
the data. It is a way to ‘share the wealth’ and to invite another to continue the inquiry and
conversation…” (1995, p. 2). Furthermore, this sampling of primary source (or raw) data,
enables the reader to determine that a significant criticism of analysts, that “…tell-tale signs
of Under-Analysis through Over-Quotation would be the small amount of analyst’s writing in
proportion to the large amount of quotation…” (Antaki et al., 2003, p.11) has been avoided.
False survey is another potential area that limits the integrity of analysis. As described by
Antaki et al., it results in analysis that makes it easier to “…fatally…slip into treating one’s
findings as if they were true of all members of the category in which one has cast one’s
respondents” (2003, p. 15). False survey refers to quantifying the data analysis in ways that
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