Focussed data coding: the thematic analysis

As the data coding process for the pilot study led to a successful number of examples of text that illustrated the participants’ experiences, a decision was made to extend the initial coding phase for the second set of data collection activities (the extended study), through to a “focused coding” stage (Mertens, 2015), in the form of a thematic analysis.

The distinction between the coding levels is made clear by Mertens:

“In the initial coding phase, the researcher codes individual words, lines, segments, and incidents. The focused coding phase involves testing the initial codes against the more extensive body of data to determine how resilient the codes are in the bigger picture that emerges from the analysis. The development of codes can be used to form the analytic framework needed for theory construction.”

(Mertens, 2015: 440)

Thematic analysis is one of the main methods used to analyse data in qualitative research. As Braun & Clarke (2006) explain, a thematic analysis is a “method for identifying, analyzing and reporting patterns (themes) within data” (p. 79). This involves multiple readings of the data and identifying connections, patterns, and themes; the findings are then discussed in the context of a broad range of academic literature. Braun and Clarke (2006) discuss what constitutes the prevalence of a theme and emphasise that there are no right and wrong methods for determining prevalence. Identifying relevant content is no easy task, and there are several methods to use in order to do so. Ryan and Bernard (2003) consider the following eight techniques for what to look for during this stage of the thematic analysis: (1) repetitions, (2) indigenous typologies and categories, (3) metaphors and analogies, (4) transitions, (5) similarities and differences, (6) linguistic connectors, (7) missing data and (8) theory-related material.

As this is a qualitative study, the techniques that lead to large amounts of statistical data (such as looking for transitions, linguistic connectors and missing data) and are more suitable for qualitative analysis, were considered inappropriate. In order to build

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up what Gibbs (2007) refers to as “a framework of thematic ideas”, the following five strategies were employed, taken from Ryan and Bernard’s (2003: 89-94) techniques, Looking for:

1) “repetitions: the more the same concept occurs in a text, the more likely it is a theme;

2) indigenous typologies and categories: local terms that may sound unfamiliar or are used in unfamiliar ways;

3) metaphors and analogies: the search for metaphors in rhetoric and deducing the schemas or underlying themes that might produce those metaphors;

5) similarities and differences: searching for similarities and differences by making systematic comparisons across units of data;

8) theory-related material: evidence of social conflict, cultural contradictions, informal methods of social control, things that people do in managing impersonal social relationships, methods by which people acquire and maintain achieved and ascribed status, and information about how people solve problems”.

(Ryan and Bernard, 2003: 89-94)

Once the data had been perused for comments related to issues that arise in a mixed Deaf and hearing working environment, following the five techniques above, 83 extracts were selected and, in the same way as for the pilot study, the signed extracts were matched to the typed transcriptions, and the selected extracts were numbered in sequence as they were selected for analysis - indicated by the initial number of each extract (the transcription number) and is shown in sequence in Appendix 9. The second step of the thematic analysis next involved categorising the extended study data. For this purpose, the 83 selected data sections were highlighted with colours (and would later be assigned a theme). This is an effective method for organising data, and, as Ryan and Bernard (2003) note, “looking for themes in written material often involves pawing through texts and marking them up with different coloured pens” (p. 88). Sections of the data that illustrated conflicts in the workplace were, for ease of analysis, highlighted

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in red; data related to other problematic issues were grouped and highlighted in yellow, and data indicating positive working experiences, in green. The data were colour coded in line with Mertens’ advice that data should be “well labelled and organized to facilitate data analysis processes and accurate reporting of the results” (ibid.). Organising the extracted data with colours was visually associative for the researcher. The cognitive functioning of Deaf people as visual and associative has been recorded by much scholarly research (such as Arnold & Murray, 1998; Marschark & Hauser, 2008; Hauser, 2010). On reviewing the data, additional highlights were added to separate the 83 extracts further: extracts highlighting other negative experiences were grouped and highlighted in orange, discussions of potential resolution were highlighted in dark green and extracts that illustrated an acceptance of difference in the workplace were coded in blue. This colour coding technique is highly suited to academic research conducted in the visual medium and in line with Deaf people’s ‘visual cognition’ (Pavani and Bottari, 2012). Pavani and Bottari’s literature review refers to over forty empirical research debates that have considered the capabilities and cognitive functioning of deaf individuals as highly visual.

The next step in the thematic analysis activity was a ‘paper-cutting’ exercise.

Mertens suggests that this stage of working with the data naturally leads to a reduction, which is important in order for the data to remain manageable. While computer-based analysis programmes are effective for quantitative data crunching, it was felt more appropriate for this qualitative study to make use of this manual technique, which Mertens refers to as the “old-fashioned way” (p. 438). The 54 pages of transcribed and highlighted data were printed out and spread out along a set of 8 large, rectangular tables in a deaf studies lab at UCLan, which was secured with a door coding pad, so entry was restricted. The colour coded sections were cut out so that the data could be organised into sets of data according to the colours that they had been highlighted with.

The sections of the data that had not been highlighted and were deemed unusable, as they did not contain any remarks related to the experiences of working in a mixed environment, remained white and were left aside. As the selected extracts were now grouped, a second number (the extract number) was allocated in order to assign the

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out extracts to the sub-themes, (detailed in the following section), giving each selected chunk an extract number as well as a transcription number.