Coding for Academic Literacy Practices

My analytical process accorded with descriptions in published academic discourse socialisation research articles of inductive analysis (Morita, 2004; Ou & Gu, 2018; Yi, 2013), thematic analysis (Anderson, 2017; Okuda & Anderson, 2017). Duff’s (2008) description of interrelated iterative, cyclical and inductive methods of qualitative data guided my overall approach. Following other researchers (e.g. Morita, 2004), I have elected to refer to this umbrella approach as inductive analysis. My data analysis began from the earliest stages, as I took note of themes which emerged both during interviews, transcription and initial reading of transcripts and other data. In addition, while I designed my interviews to allow scope for participants to discuss what they felt was relevant to their academic English, as our interviews were focused on their academic writing development and social interactions some pre-established general themes were present in the data, such as accounts of revisions to writing or seeking out support. By using the qualitative data analysis software NVivo (NVivo Pro 11, version 11.4.1.1064) I was able to develop more specific categories which were then grouped into clusters and sub-clusters; note that NVivo refers to both instances of coded data and clusters of coded data as “nodes”, distinct from the usage in Social Network Analysis and INoP. Once my initial codes can be generated, I

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looked for pattern codes (Miles, Huberman & Saldana, 2013), or patterns and associations among coded extracts. I used visualisations of data at multiple stages to suggest these patterns and relationships which I then manually checked to see if they were warranted in the data. I will briefly describe this process before discussing the next step in analysis, the looking for patterns and relationships within typologies and among clusters. Through clustering and relationships among clusters I was able to identify academic literacy practices.

I first imported the 47 interview transcripts and 320 PDF and Bitmap image files, including screen grabs of online chats threads, scanned worksheets, outlines and notes and several hundred pages of written assignment drafts. I made a link in the software between the interview transcripts and the relevant assignment or other artefacts discussed, enabling me to quickly triangulate the participants’ accounts with their actual writing. In my initial round of coding of interview transcripts, I looked for and coded accounts of similar activities including “unplanned or incidental meetings”, “studying together regularly”, “face-to-face interaction” or “online interaction”. These codes were similar to process coding (Miles, Huberman & Saldana, 2013) in that they represented accounts of actions intertwined with time, space and other emerging features of data. However, my coding also included aspects of in vivo coding, as many codes used participants’ own words rather my descriptions of their actions; for

instance, the codes “making effort” or “adding detail” came from phrases used by many interviewees. Codes did not only refer to accounts of actions but participants’ beliefs and evaluations of the actions of themselves and their judgements of other people; as such, codes were a type of participant-generated evaluation coding (Miles, Huberman & Saldana, 2013), including codes like “close friends” and “not useful (feedback)”.

When I had generated these initial codes, I grouped them into clusters.

Although I often followed the linear process of separating larger clusters into smaller sub-clusters described below, my coding also often proceeded organically as new clusters emerged at later stages of analysis. To give an example of my general process, “unplanned or incidental meetings”, “studying together regularly”, “face-to-face interaction” or “online interaction” were clustered as “Negotiating Support”. Broad clusters were divided into sub-clusters which represented more specific instances; in this case, “unplanned or incidental meetings” and “studying together regularly” were coded in the sub-cluster “Arrangements”, describing how participants arrange to meet

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people for non-class interactions. However, the Vivo software facilitated frequent, organic changes to my clustering and coding of data. I was able to combine clusters when I decided the accounts referred to the same phenomenon, separate clusters, or move/subordinate main clusters as sub-clusters when I judged them to refer to a more specific aspect of the broader theme. Conversely, some sub-clusters could also

become main clusters. At the same time as I adjusted the clustering of my data, I looked through data to determine if I missed any instances related to the groupings and categories, both manually and by using NVivo’s Text Search query function to search for terms related to the clusters. I recorded memos to remind me of my coding and clustering decisions, linked by the NVivo software to the relevant codes and clusters. I also recorded memos of my initial “folk theories” or ideas about how instances of coding and clusters appeared related. These theories formed the basis of the second stage, relationships among data.

In the second stage, I looked for pattern codes (Miles, Huberman & Saldana, 2013), the patterns, relationships and themes among clusters, groupings and instances of coded data. To do this, I used a combination of software tools and manual coding. Firstly, I looked through the data again and examined my folk-theory memos for potential pattern codes among data. I then used the NVivo Matrix Coding query to create a table with the number of instances at which codes and clusters co-occurred in the data, defined as appearing within or nearby the same data extract. For instance, the Matrix Coding query showed 20 instances in which “face-to-face interaction” co- occurred with an account in which feedback was described as “useful”, suggesting that there was a relationship between interacting face-to-face and considering feedback to be useful. The Matrix Coding query results included the instances in which my codes co-occurred. I manually examined each of these co-occurrences to assess whether a relationship was warranted in the data or whether the co-occurrence did not appear to signify a relationship. When a relationship among to nodes or clusters appeared warranted, I used the NVivo Relationships Coding function to code this relationship (the pattern code). In addition to the standard coding functions, the Relationships Coding function allows for a description of the relationship between two nodes or clusters, such as “face-to-face interaction tends to be useful”. I

established 75 relationships among nodes I had earlier coded. I then used the Explore Diagram visualisation function to investigate how particular nodes were linked to the relationships I had now theorised. This visualisation function generates a map to show

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how a selected node is related to all other nodes, memos, relationships or other data. For instance, the code “face-to-face” was related to the relationship code “face-to-face interaction tends to be useful” and also “face-to-face interaction is rarely not useful”. I then examined the data extracts to analyse whether the relationship was warranted. Coding these co-occurring relationships facilitated visualisations of academic literacy practices. Each single code represented a particular activity, such as interacting face- to-face, adding detail to writing or evaluating a peer’s contribution in an interview, but the relationships among these codes showed how these micro-level activities were instances of multi-dimensional, meso-level academic literacy practices.

During these coding stages, I also made memos and lists of emerging membership categories, category-resonant devices and potential category features, representing the first step in Stokoe’s (2012) MCA procedure quoted at length below. However, my coding for social practices in the data was not a form of membership categorisation analysis. Rather, I used the initial round of coding to reveal instances of category work for later analysis.