Initial Coding

Initial coding requires close examination of the data. Word-by-word coding offers a highly detailed approach. It supports a thorough consideration of what participants say. Often captured through the application of in vivo codes, concepts that are repeated in the data can be identified (Birks & Mills, 2015; Saldaña, 2016). Alternatively, Charmaz (2014)

identified line-by-line coding as being a common first step in grounded theory research. The intent of line-by-line coding is to uncover and identify patterns that exist within the data. As patterns emerge, they can be analysed to consider what occurs, as well as how and why (Charmaz, 2014). Ideas that occur to the researcher during this process are recorded through memos, which become the foundations upon which the grounded theory is built. When undertaking line-by-line coding, vivo codes can still be employed. This allows the researcher to utilise the word(s) of the participants to code the data. Commonly used in grounded theory research, in vivo codes are noted as valuable for retaining the voice of participants and maintaining the inherent meaning (Saldaña, 2016).

An alternative to the use of in vivo codes during initial coding is the application of process codes. Process codes, referred to by some authors as gerunds (Charmaz, 2014; Saldaña, 2016), are verbs which are used as nouns and applied to capture a process. Hence process codes allow the researcher to emphasize the action or processes that take place within the data. An advantage of applying process codes is that the researcher focuses on what is

occurring rather than capturing types of participants. Furthermore, it restrains the

researcher from develop theoretical conceptions too early in the analysis process (Charmaz, 2014).

I commenced initial coding using the word-by-word approach with the intent of employing in vivo codes to maintain the perspectives and experiences of the student-participants (Saldaña, 2016). On completion of the initial coding of my first four transcripts, whilst I had identified and applied a small number of in vivo codes, I was anxious about the lack of codes I had created. Hence, I revisited the data and began to utilise line-by-line coding and employ process codes to capture actions and events in addition to applying in vivo codes. Use of a line-by-line approach resulted in a greater wealth of codes being created. Ashwin (2012) conceptualises of teaching-learning as a process that occurs through the actions and interactions of individuals (section 1.2). Hence, examination of the data for the application of process codes was a nature progression of my coding.

During initial coding Charmaz (2014) advises the researcher to remain close to the data and being open to seeing all potential theoretical directions the data may lead. She presents the following set of questions for researchers to use to support their initial coding:

How does the process develop?

How does the research participant act while involved in the process?

What does the participant profess to think and feel while involved in the process? What might his or her observed behaviour indicate?

When, why, and how does the process change? What are the consequences of the process?” (p. 127).

Use of these questions during initial coding and analysis facilitated my consideration of the data. It allowed me to consider a range of potential explanations regarding what was occurring when using the teaching tool in each phase of data collection. Their use became notably influential once I had commenced theoretical sampling (section 6.4.2).

Initial coding also highlights areas of information where data is insubstantial. Construction of grounded theory requires robust evidence (Charmaz, 2014). Hence it is essential for the researcher to identify areas that require further data to illuminate the issue. This prompts the commencement of theoretical sampling for the collection of additional data.An initial code identified within my first set of transcripts raised a question regarding whether the design of the teaching tool provided authentic representation of occupation. This prompted me to commence theoretical sampling. As I progressed through initial coding of further Phase I transcripts questions grew from the final year student-participant transcripts as to whether the teaching tool could be utilised by students for collaborative-learning, again resulting in theoretical sampling.

6.9.1.1 Initial Coding of Phase I Data

In the initial coding of Phase I data eighty-three codes were created (Appendix 10) and applied across the transcripts. Codes were created as each transcript was analysed. As later transcripts were reviewed, and new codes created, I returned to previous transcripts to ensure later codes were applied where appropriate. Hence, producing my constant comparative analysis.

In each one-hour data capture event the focus was on my teaching of the concept of occupation using the teaching tool. However, toward the close of each data capture event student-participants were provided with opportunity to ask questions regarding the

research. In addition to asking questions regarding the research student-participants chose to make comment on their impressions of the teaching tool in relation to their learning and its’ potential use in occupational therapy education. This data was recorded and included in all transcripts.

6.9.1.2 Initial Coding of Phase II Data

I began by transcribing the verbal communications of first Phase II data set. Whilst this process enabled me to become familiar with the data, I also noted that my transcripts were only capturing the verbal communications for coding and analysis. As the teaching tool is a physical entity and the learning process occurred in a social context, I noted non-verbal communications and actions occurring in each session that may also require examination.

A wealth of non-verbal communication and action was observed as occurring for the

student-participants during the data capture event. Hence, I decided to attempt to code the visual data in addition to the written transcripts. To support my coding and analysis of data I had chosen to undertake computer aided analysis, utilising the data analysis software of

ATLAS.ti (https://atlasti.com/ ; section 6.10). This allowed me to upload visual recordings of the data capture sets alongside the written transcripts. I then began initial coding of the visual data. However, exploration of the facilities available within the software lead me to note that whilst it was possible to link codes within the system, the addition of the visual data did not add new insights.

Creation of the initial visual coding enabled me to identify two key issues that I had to consider; confidentiality and anonymity, and, limited enhanced insight of the data. Confidentiality and anonymity of student-participants had been articulated during the process of gaining ethical approval. I had stated that whilst student-participants would be visually recorded during Phase II their confidentiality and anonymity would be protected in relation to my research report and any related publications. Thus, as I created initial codes it was necessary to create doctored visuals for the codes to exclude any student-participant defining features. A further challenge became apparent as I noted that any student- participants who originated from Non-Caucasian background would be more identifiable that their peers. Initial visual codes I created captured the hands of student-participants. As the ethnic origins of the student-participants was not broad, inclusion of visual codes would result in some student-participants potentially being identifiable, breaching their anonymity and confidentiality.

Secondly, the non-verbal communications I aimed to capture were action based. The static screenshots produced using the ATLAS.ti system did not capture the dynamic actions of participants. Hence, inclusion of this facility of coding did not add to either my

noted that the static shots did not reflect variety between codes. This resulted in confusion for me when progressing through the initial coding process.

As a result of these insights gained during initial coding of Phase II data, I did consider whether there was visual data to be coded in Phase I. Thus, I moved back through Phase I data in exploration. A key difference in the recording of Phase I and Phase II was that in Phase I student-participants were not recorded visually, only audio recorded. Hence, the visual data was only of myself. I therefore undertook initial coding of the visual data of Phase I. However, it once again became apparent that insights were limited due to the static nature of the recording of the codes. One key aspect did emerge and is presented with examples of the visual codes in section 8.3.1, Figure 23.

Whilst I did not progress with the creation of visual initial codes within either phase, I did proceed with initial coding of Phase II data in the same manner as I had undertaken for Phase I data. As processes and actions emerged during initial coding, so sub-categories and categories surface, leading to focused coding.

Once several initial codes had been created, I began to allocate colours to different initial codes (Table 2). Colours were allocated to codes that appeared to share the same

properties. I began this process early in the initial coding as it enabled me to see when different issues appeared to come together in groups, or sub-categories. Whilst this relates to the later development of categories for some code groups, I was also mindful to not move into focused coding too early in the process.A further advantage I discovered of having applied colours to codes was that it highlighted that a number of codes belonged to more than one category. This identification became useful during my focused coding process (section 8.3).

Table 2: Colour Allocation of Initial Codes

Colour Sub-category Description of Sub-categories

Green About the Teaching Tool Applied to codes where properties of the teaching tool are identified and perceived to support learning.

Yellow Behaviours of Student- Participants

Applied to codes that aim to capture the (learning) behaviours displayed by student participants. Purple Knowledge Development Applied to codes where student-participants are

noted as verbalising their learning.

Blue Different Formats Applied to codes when student-participants identify different formats that they would like to see the teaching tool developed into for support of their learning.

Red Uses in Education Applied to codes when student-participants identify and suggest different uses for the teaching tool, separate from teaching about the concept of occupation, purposeful activity and activity. Orange Behaviours of Tutor-

Participant

Applied to process codes that aim to capture behaviours of the tutor-participant.

Pink Utility Applied to codes that identify different uses of the teaching tool

Brown Complex Applied to codes in which challenges to learning the concept of occupation are identified

Grey Challenge Design Applied to codes in which student-participants challenge the design and/or utility of the teaching in support of their learning.

Turquoise Prompts Reasoning Applied to codes that capture when student- participants appear to specifically draw on professional reasoning skills.

Black Features Applied to codes that relate the design features to the specific features of occupation as a concept. White Nothing Applicable Codes that remain unlabelled did not appear to me

N.B. The colours allocated to initial codes in Phase I data were maintained and applied to codes that were also employed in Phase II, and visa-versa.

As initial coding progresses categories of data emerge and, on occasion, certain code labels come to the attention of the researcher more than others. These codes can develop to form categories enabling conceptualisation of data for the researcher (Birks & Mills, 2015). This leads to the next stage, that of focused coding.