Computer-Assisted Qualitative Data Analysis

In consultation with LACs in both communities, it was decided that I would conduct analysis of the data. A combination of thematic analysis followed by

narrative analysis guided data analysis of the interviews conducted with community Elders with the aim of generating theory from the interview data. Thematic Analysis is a commonly used systematic method for classifying the content of text into

themes and identifying relationships between them (Fereday & Muir-Cochrane, 2006). Within this type of analysis, significant themes are revealed by their consistency across and within participants (Floersch, Longhofer, Kranke, &

Townsend, 2010; Miles & Hubberman, 1994). Analytical techniques within thematic analysis seek to systematically categorise data into categories which best describe the phenomena in question. Thematic analysis distinguishes itself from similar analytical techniques, such as content analysis and grounded theory, because

significance of a theme is not solely based on its frequency and the unit of analysis in coding is not specified (Bryman, Bell, & Teevan, 2012; Charmaz, 2008; Floersch et al., 2010).

Narrative analysis was conducted following the establishment of themes within the data. Narrative analysis involves the interpretation of qualitative data where particular emphasis is placed upon the embedded layers of meaning within interviews (Berg, 2012; Bryman et al., 2012). This form of analysis allows for an understanding of the contingent, the local, and the particular (Wiles, Rosenberg, & Kearns, 2005). This emphasis situates narrative analysis as a useful method of analysis for health geographers in particular, as it provides a tool for connecting intimate discussions of experiences in the daily lives of participants to broader social and spatial relations. In short, narrative analysis allows an understanding of not only what is said during an interview, but also how individuals attach meaning to these experiences.

Transcripts of interview audio recordings were conducted by an external service. Upon receipt of the files, hard copies of each interview was printed and read

in tandem with the audio recording of the specific interview to ensure transcription accuracy. As was agreed upon during meetings with each LAC, names of local places as well as those of individuals were removed. Once all transcripts had been

formatted, each was assigned a code to ensure confidentiality. These codes corresponded to the community and interview participant, with the key kept separately from the transcripts. Hard copies of transcribed interviews were locked in a secured drawer that was kept in a locked office.

Once the accuracy of each transcribed interview was verified, I read the transcripts again in order to further familiarise myself with the data. This was especially important for interviews conducted with Elders from Batchewana First Nation, as I was not present throughout this part of the data collection. LAC

members and research assistants from Batchewana were contacted on a number of occasions in order to provide clarity on some topics discussed by Elders. Notes were made on each transcript detailing how specific sections related to the analytical framework.

Computer-assisted qualitative data analysis (CAQDA) was then conducted using QSR NVivo 9. Critics of CAQDA often argue that one of the key dangers presented by the use of software in analysis is that it can guide researchers,

encouraging reliance upon technology to find common points and patterns in data (Basit, 2003; Butler, 2001; Hesse-Biber & Leavy, 2004). This is especially facilitated in NVivo 9 and 10, where auto code features allow for entire sets of data to be coded automatically. Instead, it is argued by proponents of CAQDA that researchers should familiarize themselves fully with their data through lengthy reading and rereading

of the transcripts. This prevents contradictory or rarely referred to points from being overlooked. Other common critiques of CAQDA are that it has the potential to distance the researcher from the data, encourages quantitative analysis of

qualitative data, and creates methodological dogma (Welsh, 2002). However, proponents of CAQDA cite that this approach facilitates data analysis and answers calls for increased transparency (Baxter & Eyles, 1997; Bringer, Johnston, &

Brackenridge, 2004; Crowley et al., 2002). In my own experience with the CAQDA, I found that it provides a means for easily dealing with large sets of data.

In the early stages of analysis, nodes were created for each individual participant using the assigned individual classified codes. These nodes were

assigned attributes, using classifications such as: gender, age range, interview type (audio, video) and community (ie. Batchewana Bay, Goulais, Rankin, Pic River). Assigning attributes to the data enabled analysis based on these characteristics. For instance, I could ask the software to show me what individuals from a specific community said about a specified topic. This also allowed for quick access to interviews used specifically in the production of the documentary film. Interviews were then loaded into NVivo, with each transcript file assigned to the corresponding node created for that specific participant.

Several memos were also linked to each interview. These memos were primarily created during data collection, as such they largely related to interviews conducted in Pic River. Memos contained my own personal reflections on the experience of conducting the interviews, including any ideas that I may have had about potential limitations and areas for future exploration. Throughout interview

coding and analysis, memos served as reminders of the particular circumstances of each interview. For instance, in one memo I have written that a particular interview participant may have given relatively short answers to our questions because they expressed that they were not feeling well at the onset of the interview. In another memo I discuss why I spoke more within a particular interview, explaining that both the participant and research assistant were very emotional and that the research assistant stated they would like me to take over temporarily.

The previously created analytical framework was then incorporated into the NVivo project. This was done by creating nodes for each of the key themes within the framework. Key themes, such as ‘health’ and ‘land’ were assigned as parent nodes with related topics created as child nodes. Further child nodes were created as they emerged throughout data. For example, the parent node ‘health’ contained multiple levels of child nodes (i.e. definition of health, health outcomes).

Data analysis involved open coding of each interview transcript. This was believed to be the optimal approach to coding, given that the interviews themselves were semi-structured. While the use of the auto-coding feature in NVivo would have permitted much faster coding of interview data, this was not possible because all interviews did not follow a structured interview guide. Elder responses given to questions were coded to nodes as complete sentences or paragraphs. New child nodes were also created within each of the key areas if the specific topic had not been previously coded. Data was also coded to multiple nodes if it was believed that several themes were being addressed simultaneously. The emergence of new nodes occurred primarily while coding the first ten transcripts. As such, these transcripts

were re-coded once coding for all transcripts was finished. This was done to ensure that the data contained within these first ten transcripts would be coded to the final collection of nodes. Text search queries were also conducted to ensure that specific mentions of a theme had not been missed.

Subsequently, each individual child node was examined and compared with other child nodes of the parent. Child nodes were joined if they contained significant similarities in what had been coded within them. The joining of very specific smaller child nodes often led to the creation of new larger nodes which themselves related to broader themes existing within the data. Joining child nodes was also facilitated using the modelling feature in NVivo. Models were created for each parent node, with child nodes added in order to visualize how the child node related to the parent. If it was discovered that a node related to the parent only through multiple child nodes, the specific node was joined with a larger theme.

In the final stage of data analysis I conducted a series of matrix queries. These allow for the running of multiple queries simultaneously and are useful when trying to establish differences between categories or when exploring overlaps between themes (Bazely & Jackson, 2013). The results of a matrix query are displayed as a table that links directly to the sourced data. For instance, I ran a query to explore differences in coding relating to how individuals from each reserve area discussed direct forms of environmental dispossession. The resulting table displayed location as columns with each node coded for direct forms of environmental dispossession displayed as rows. I then assigned each cell to show me the total number of coding references that were found for each intersection. This allowed me to see where

similarities and differences existed in the data and visually showed me areas of comparability between the communities that I had not previously noted.