4.10 Data analysis procedures
4.10.3 Pattern-matching analysis
4.10.3.1 Interview data transcription
The first stage in the qualitative analysis was verbatim transcription of the interview data. During the transcription process, which took several weeks to do, I did not only present the verbatim statements but also nonverbal and paralinguistic communication. The analysis involved listening to the entire audio several times and reading the transcription many times to provide a context for the categories specified in the conceptual framework (Cohen, Manion, & Morrison, 2011).
The transcript consisted of notations that explained the participant’s body language, for example, facial expressions, gestures, gazes, sighs, breathing rhythms, voice intonation, and pauses. The transcription was more complete if I accounted for these visual cues because, as Fielding and Thomas (2008) note, ‘we communicate by body language as well as speech’ (p. 253).
Furthermore, visual cues were important to note because participant’s responses are more trusted if their body language is congruent with their verbal utterances (McMillan & Schumacher, 2010).
Research methodology Data analysis procedures
By drawing upon the Jefferson (2004) system of transcription notation which features conventions for intonation, changes in volume, intake and exhalation of breath, pauses and their duration, capital letters for volume, I transcribed the interview to a sophisticated level of detail.
She (Jefferson) argues that if she were asked for the reason for including all the “stuff” in the transcript, her interesting response would be, ‘Well, as they say, because it’s there’ (Jefferson, 2004, p. 15). On a more serious note, as Irvine, Drew, and Sainsbury (2013) aver, using this system for the transcription of interview data allows for a close examination of precisely what took place – what was said and also the way in which it was said. Appendix C1 is a glossary of the symbols used in the transcript for this study (Appendix C2). I used the numbers in each line of the transcript to facilitate reference to specific points in the interactive in analyses (Hepburn & Bolden, 2013).
As I read the transcript, coding and reevaluating the development of the coding scheme, coding took place. Specifically, all the textual data in the transcript were entered verbatim into ATLAS.ti software for further qualitative data analysis. The decision to incorporate software was primarily based on the reason that ATLAS.ti does not merely speed up the process of grouping data according to categories and retrieving coded themes (Wong, 2008), it also has an attractive search facility that enables interrogation of the data and thus adds rigour to the study (Ozkan, 2004). In general, integrating computer software in this analysis finds support in Welsh’s (2002) assertion that ‘in order to achieve the best results it is important that researchers do not reify either electronic or manual methods and instead combine the best features of each’ (p. 9). Thus, the most compelling reason for using the software is that it provides a quick and simple way of counting who said what and when and in turn, provides a reliable general picture of the data (Wong, 2008).
The possible drawback, that using computer software for qualitative data analysis may distance the researcher from the data (McMillan & Schumacher, 2010; Morrison & Moir, 1998), was mitigated by integration of ATLAS.ti with my own analysis.
While I remained the main tool for analysis, all transcribed data, that is, transcript and field notes were converted from word format (.doc extension) into a rich text file format (.rtf extension) in order to use ATLAS.ti’s text and visual coding features. Then, I began to attach in vivo codes to the text units while placing references into the hierarchical indexing system. The codes are
Research methodology Data analysis procedures
referred to as in vivo codes because they retain the respondents’ words (Noble & Smith, 2014).
Thus, coding involved identifying a paragraph in the data that exemplified a particular category (Wong, 2008). For instance, coding was done through selecting a text about everyday meaning of proof and coding it at the node “semantic contamination”. Thus, I ran a text search query to find other such references. From the ATLAS.ti perspective, nodes are categories. These ideas are represented in Figure 4—6.
Figure 4—6. Flowchart of the basic steps of data analysis, adapted from Wong (2008)
Research question
Why does Presh N hold informed beliefs about the functions of proof?
Data collection
Semistructured interview data → transcribes to text
Field notes → transferred as text
Synthesis and making sense of data Description of relationship between categories
Seeking patterns and relationships Mapping interpretations of findings
Working with textual data
In vivo coding related to the research question Categorisation (using memoing) Coding of selected data at categories created
Retrieval of data coded at categories (Creation of Tree Nodes) Relationship among categories
(Model)
Research methodology Data analysis procedures
Also, I attached memos to these text segments to record the ideas, insights, interpretations or understanding that may arise from the data. Then, I displayed tree nodes to see how the participant talked about, for example, “semantic contamination”. Notably, tree nodes are categories organised hierarchically into trees. Thus, I used single item search to ensure that every mention of the word
“textbook”, for example, was coded under the “factors” tree node.
The final stage involved recording of insights gained into a memo from the display. This memo contained my commentary on text from the document to use in the interpretation stage of the project. Each node on the tree accommodated similar data and allowed storage of the comments I made. Then, I searched the indexing system to retrieve data according to themes identified in literature. The text was rechecked for the occurrence of these categories to seek patterns so as to determine relationships. I explained the relationships between the categories to seek patterns to interpret the data from the standpoint of participant’s perspectives, in their own voice (McMillan
& Schumacher, 2010). As I transcribed the interview verbatim, I demonstrated that the analysis is a nonlinear but recursive process involving a search for themes to categorise.