The process of analysing the interview data via thematic analysis involved five distinct but interconnected steps. The first step of this planned approach was familiarisation, which involved detailed reading and re-reading of the verbatim transcripts (to ensure complete immersion within the texts) and successively linking field notes to transcripts to ascertain all possible information that could assist the understanding of the contexts surrounding the data and thereby contribute to the comprehensive analysis of the interviews (Braun & Clarke 2013). The second step involved identifying a framework of general ‘categories of interest’. This framework was informed by the research questions and developed by ideas and concepts that formed throughout the information-gathering process but also particularly by the reading, and re-reading of the transcripts (Guest 2012). The next step, the third, was the categorisation and indexing of all of these categories of interest by associating all relevant pieces of text, linking any recurring patterns and connected concepts. In this way the professionally transcribed audio interview data was carefully examined, over an extended period of time, using a free manual line-by-line coding process, with pertinent texts and patterns of experiences or concerns being identified and selected to be divided into groupings that reflected the previously identified categories of interest (Corbin & Strauss 1990) such as student experiences and concerns. These categories of interest were entitled in a natural and straightforward manner, for
example ‘student experiences’ and ‘student concerns’. Later, these categories would be sub-divided into increasingly refined groupings that exhibited the student’s individual experiences and concerns in a specific, detailed manner. This examination continued by separating the categorised text into component parts of their core subject matter that were examined for commonalities and variations which were in turn graded for subject matter or themes (Kelle 2005). A comparative analysis methodology was used in an interpretative manner, which required the taking of one piece of data and comparing it to all other pieces of data. Throughout this process, the researcher looked at what it was that made this piece of data particularly similar, or particularly dissimilar, to all of the other pieces. This method of analysis was primarily inductive, with the researcher examining the data critically and drawing appropriate meaning from that data (Glaser 1965). Ultimately, over an extended period of time and with great diligence, all of the data was categorised by this same reasoning process.
When all of the data had been categorised, it was then re-examined for properties that characterised it (properties are specific attributes of a category). The data was further examined with associations being identified by making comparisons across the data, looking for parallels and variances between the student comments. In this way corresponding data were grouped together to form hierarchical sub-categories. After all of the categorisation was completed for the first time, the data was revisited and the process repeated with the information that had remained outside of categorisation, thereby further refining the data. This process was repeated until all the data had been grouped into closely defined categories and then further separated into themes, thereby ensuring that everything that could be gleaned from the interviews had been so.
Themes are characterised as units derived from patterns such as conversation topics, recurring statements, meanings or feelings. Themes are recognised as "bringing together components or fragments of ideas or experiences, which often are meaningless when viewed alone" (Leininger 1985:60). The definition of the terms ‘Category’ and ‘Theme’ are shown in Figure 7 overleaf.
All recurring themes were duly recorded in the fourth step: that is, the sorting and cataloguing of significant descriptive text, the assignment of descriptive labels with additional explanatory notes, and the grouping of emerging thematic content extracted from within the narrative accounts (Hammersley 2015). The process of analysing the content involved the segments of germane text being compared across all other student transcripts, continually revisiting the data and reviewing the classification of the data until the researcher was sure that the categories and themes used to summarise and describe the findings were a true and accurate reflection of the data (Glaser 1965). It was when comparing sub-themes to obtain a comprehensive understanding of the data that patterns often emerged. When they did emerge the researcher obtained feedback from the informants about them. This was done either as the interviews were taking place or later by asking the students to give feedback from the professionally transcribed interview data. In the former, the interviewer used the students feedback to establish, or modify, the next questions in the interview. In the latter, the researcher asked the student to provide feedback from the transcribed interview data that was then incorporated into the theme analysis.
Figure 7: Definitions of terms ‘Category’ and ‘Theme’ DEFINITIONS:
Category: A ‘category’ is a generalised grouping of wide-ranging student
experiences and concerns, all of which have been recognised from within the verbatim interviews.
Theme: A ‘theme’ is a specific subject area that describes the
phenomenon contained within a ‘category’, thereby demonstrating the distinctive characteristics of the student’s experience or concern. It
captures something important in relation to the overall research questions.
Example: ‘Language’ is a category, but ‘Students English Language
Problems’ will be a theme within that category.
The fifth and final step was the interpretation of all of the accumulated data, thematic subject matter and associated notations to form a detailed analytical report.
“One of the tasks here is not only to make sense of the individual quotes, but also to be imaginative and analytical enough to see the relationship between the quotes, and the links between the data as a whole” (Rabiee 2004:658).
From the start of the data analysis, the categories of the students main concerns were identified that would cover the major variations in their conceptions of the situations they encountered; hierarchical sub-categories were then identified within the main ones in a process of continual refinement. Recurrent themes were then isolated and any linkages to them identified and sorted within a hierarchical framework. Particular attention was paid to data relevant to the research questions and any other pertinent focal themes identified in the interviews. Continual refining and interpretation then followed, which sought to systematically corroborate and refine any uncovered categories into their thematic content. The labelling of these categories and themes∗∗ closely followed the wording of the two research questions,
and/or the students’ own vocabulary, wherever possible. For example the two primary categories were unpretentiously entitled ‘student experiences’ and ‘student concerns’. The categories and themes that were sought were those that could be clearly defined and seen in relation to one another in a coherent hierarchy.
The thematic selection process consisted of extracting important verbatim statements from the students interview data and articulating significances about them through the researchers interpretations, selecting the most applicable meanings into a series of organised themes, elaborating on the themes through rich interpretive description to ultimately deliver a definitive analysis report (Butler-Kisber 2010).
There is always the potential for ambiguity to occur when dealing with any sociocultural situation, as all human interaction is fraught with uncertainties and misunderstandings. So it was when defining the recorded interview data and allocating the student quotations into specific categories and subsequently into themes. The differentiation in language and values between student and researcher can potentially play an important part in these circumstances. The classification of student responses is not an exact science but more of an erudite artform∗, so that the combined elements of research and literature reviewing with peer and participant collaboration all combine with the researchers overall experience in the field in
∗ It was thought to be appropriate that the designation of all categories and themes should
follow the students own terminology whenever possible.
∗ As the research developed so too did my understanding of what I was finding (i.e. my understanding improved as the study progressed, with my thinking changing over time).
question to enable the most accurate selections or examples to be chosen to illustrate each significant instance found within this research study.