Coding and Analysis
I applied computer assisted qualitative data analysis software (CAQDAS), concept mapping, and word cloud generation throughout the project. Digital technologies that I had suggested in the research proposal were explored, but time constraints prevented a fulsome examination of the extensive list of the various CAQDAS tools available. As I had previously used NVivo, I revisited this software to address issues of privacy and security, as well as how to create visualizations within this software. As a result, NVivo was selected for the coding of this research. Otter.ai software was used to convert audio files into transcript texts from the interviews. As noted previously, using transcription software is not without its issues, so careful review and rereading while simultaneously listening to the interview videos. The transcripts were revised before being imported into the WordArt word cloud generator. Revisions included the removal of names, locations, and unnecessary text elements such as prepositions and conjunctions. Concept mapping tools - Lucidchart - was used to map out the ideas and conceptions as coding and analysis progressed. In this way, I engaged in the crystallization of the multiple and layered elements in the reserach data. I acknowledge that recordings and digital artifacts “offer lively and intriguing options for making, assembling, and becoming qualitative data” (Ellingson & Sotirin, 2020, p. 33, emphasis in original).
Vagle (2018) suggests a whole-part-whole sequence for data analysis and synthesis that includes:
1) a holistic reading of the full text to become “attuned to the whole material-gathering event” (p. 110);
2) a first line-by-line reading while note taking, adding marginalia, and journaling;
3) writing follow-up questions to ask the participants;
(4) a second line-by-line reading to examine meanings and extracting excerpts, thus creating a new data moment from these gathered texts;
(5) a third line-by-line reading focusing on analytical thoughts; and
(6) subsequent readings to reveal and name the emergent patterns, themes, and meaningful units across and amongst the participant’s collective data.
As noted in the graphic of the research sequence provided, I applied multiple opportunities for multimodal, media making and creative constructions to enhance the potential of opening new lines of meaning and understanding, of seeing what frames my seeing (Lather, 1993). While Saldaña (2011) suggests that patterns emerge from the field notes and visualizations, I understand that I have a researcher’s responsibility to actively interact with the data that is generated. It is through subsequent close listening and deep reading that I can construct and develop the codes, memos, and themes of the participant’s “routines, rituals, rules, roles, and relationships” (Saldaña & Omasta, 2018, p. 15). As the research process diagram shows, in phase two and three I continually revisited and reviewed the video interviews, transcripts, visualizations, codes, and concept maps in order to make meaning of how these data moments represented and reflected the lived experiences of the participants.
While the exact coding techniques and strategies emerged from the data and the research design, awareness of essential skills and attributes supported the coding of my research. Saldaña (2016) identifies personal attributes that qualitative researches should possess – organization, perseverance, ability to deal with ambiguity, flexibility, creativity, rigorously ethical, and an extensive vocabulary. These supported my cognitive skills of “induction, deduction, abduction, retroduction, synthesis, evaluation, and logical and critical thinking”, as identified and required for qualitative researchers (Saldaña, 2016, p. 338).