A common approach to analysing qualitative data is through a constant-comparative data analysis method and using a coding system to make meaning of the data (Lichtman, 2013; Mutch, 2005). The constant comparative method involves breaking down the data into discrete ‘incidents’ or ‘units’ (Lincoln and Guba, 1985) and coding them to categories. As Taylor and Bogdan (1984) summarise: “in the constant comparative method the researcher simultaneously codes and analyses data in order to develop concepts; by continually comparing specific incidents in the data, the researcher refines these concepts, identifies their properties, explores their relationships to one another, and integrates them into a coherent explanatory model” (p126). In this instance, this method of constant-comparative analysis suggested by Lichtman (2013) and Mutch (2005) was employed to analyse the data captured by the transcribed semi- structured interviews (and the accompanying field notes), and the national and individual school policy and procedural documentation relating to mentor teachers.
To gain insight, coding is part of a constant-comparison methodology that enables the researcher to reduce large amounts of raw data into categories, and then further down to conceptual themes based on the perceptions of the participants (Miles & Huberman, 1994; Mutch, 2005). Through using this data analysis approach, I was able to look at individual cases and also across the three ‘cases’ for both the beginning and mentor teachers and compare and contrast these against the policy documentation.
During this process of coding, I created an indexing/categorising system to bring order to the data analysis process. Specifically, I looked for an item of text (words, phrases, terms etc.) or behaviour that said the same thing or reflected the same thing (Cohen et al., 2011). This process emerged in three distinct phases: initial/open coding, axial coding and conceptual themes (Cohen et al., 2011; Denzin & Lincoln, 2003) (see figure 3 below-Coding process). Through these phases of data reduction, the coding tools helped me to simplify and organise the data into manageable chunks. I briefly outline below these phases and to highlight how qualitative research is inevitably interpretive, and is a reactive interaction between yourself and the data of an already interpreted social encounter (Cohen et al., 2011).
Figure 2: Coding process
The initial/open coding involved comprehensive reading and re-reading of the data to code the transcribed semi-structured interviews and the field notes, and the policy and procedural documentation. An initial/open code can be a word, a phrase, or an action and you assign a code. For instance, these might be repeated words or actions, strong emotions, metaphors, images, emphasised items, key phrases, or simplified concepts (Mutch, 2005). In essence, the data is broken ‘open’ and you go through the twin process of constantly questioning the data and then comparing the data with other empirical data (Grbich, 2007). At this stage, if needed, I made comments under the codes to refer to at a later stage. I also kept referring back to the main research questions of the study to bring my focus back to the aims of the study and this helped in bringing clarity to the constant-comparison coding process (Bogdan & Biklen, 2007). The next crucial stage of data analysis was axial coding, a process where you identify relationships and links between the codes (Lichtman, 2013). The texts were examined and labels or key words were used to identify emerging categories. During this phase, I was reducing a long list of codes down to a smaller list of categories, with some having sub- categories until further inferencing and interpretive analysis had been done (Grbich, 2007). At this stage I was having to decide and identify which categories were less important than others and come back to my research questions to guide my decision-making (Lichtman, 2013). At the completion of this stage I met with my supervisors to review the initial/open and axial coding phases to receive some constructive feedback on the data analysis process so far, and to see if the themes starting to emerge from the data were an accurate reflection of the participants’ perceptions and schools’ policies and procedures (Glesne, 1999).
The final step in the data analysis coding process is to identify key conceptual themes that reflect the meaning you attach to the data you collect (Lichtman, 2013). Data needed to be richly evident in at least four out of six participants’ accounts, or two out of three accounts if analysing responses from either beginning or mentor teachers as a separate group, and common
to other data sources to be included as a final theme in the findings section. Grbich (2007) suggests this is where you validate the relationship between a nominated central core category and drawing together additional categories of context, conditions, actions, interactions, and outcomes together with an integration of field notes/memos. I found that the more I read and re-read the data I was able to identify those themes that were richer in meaning than others. These overarching themes I identified and finalised were prevalent throughout the coding process and reflect, in my opinion, the participants’ perceptions about mentoring processes. Through this constant-comparative method I was able to feel more comfortable with the data as I progressed through the coding process and steps of reductive analysis. Moreover, I felt confident that I had reduced the data to three main conceptual themes and subthemes that quintessentially encapsulated the study (see Table 2: Findings overview below). These themes are 1: Mentoring Policy-illusion or confusion? 2: Mentoring Practice in Action. These will be presented in Chapter Four – The Findings. Following this process of data analysis, the key emergent themes were used to inform an interpretation of the participants’ perceptions and guide a report of the findings through a narrative discussion in Chapter Five.
Table 2: Findings overview
Theme 1: Mentoring Policy – illusion or confusion?
Subthemes •The illusion
Theme 2: Mentoring Practice in Action
•Perceptions of mentoring and mentoring relationships
•Learning conversations: Collaboration or gatekeeping?