Data Coding

Analysing data in the qualitative approach is described by Creswell (2005), and Ziebland and McPherson (2006) as an eclectic process and agree that there is no single ‘right’ approach. Nevertheless, in general, data analysis uses a method of evolving codes and classifications, and producing methodical subjective comparisons and contrasts of the data. The sole aim of the process, as Ponkinghorne explains, “… is not the marks on the paper but the meanings represented in these texts” (2005, p. 138).

“Analysing data is the heart of building theory from case studies, but it is both the most difficult and the least codified part of the process” (Eisenhardt, 1989, p. 539). Therefore it is essential to gauge the degree to which the case study researcher has clarified the data analysis processes. To improve the understanding of the findings and to assist the reader in judging the degree to which they are the output of a systematic and rigorous process, a clear description of the analytic technique or techniques is required (Dubé & Paré, 2003).

Authors such as Stake (1995) and Ziebland and McPherson (2006) remind researchers that the goal of qualitative data analysis is to communicate an understanding of data, and a significant aspect of this is to provide an understanding of the data collection methods, such as semi-structured interviews. Diefenbach (2009), Anfara et al. (2002), and Ziebland and McPherson (2006) indicated that the large volume of data semi- structured interviews usually generate can be problematic due to the complexity of analysis, and the often difficult and at times contradictory nature of the data collected.

Chapter Three – Methodology

Coding is a technique used to categorise data into different groupings, using descriptive words, phrases, themes or codes in an attempt to capture the meaning from the data. The process aims to transform the data to a higher level of abstraction by reducing data into smaller groupings of analysis and at the same time reducing the volume of the data (Creswell, 2005; Neuman, 2011). Miles and Huberman (1984b, 1994) described data analysis as consisting of three concurrent activities; 1) data reduction: “… the process of selecting, focusing, simplifying, abstracting, and transforming the raw case data”, 2) data display: the “... organised assembly of information that permits conclusion drawing”, and 3) conclusion drawing and verification: “… noting regularities, patterns, explanations, possible configurations, causal flows, and propositions” (Miles & Huberman, 1994, p. 11). These three processes occur throughout the data analysis process and selection of a theoretical framework and is accomplished through producing summaries, coding, separating out themes, clustering, partitioning, and note writing (Miles & Huberman, 1984b, 1994). Miles and Huberman (1994) also consider qualitative analysis, through its detailed study, to be a very effective approach for assessing causality, and further, that it has the ability to identify structures beyond nominal association.

The processes of open coding, axial coding and selective coding were adapted from the Grounded Theory methodology (Strauss & Corbin, 1990) for use in the coding phases of this research project. Each of these coding techniques were employed in the data analysis phase of the research project.

3.5.8.1 Open Coding

Open coding, a coding technique first described by Strauss (1987) and refined further by Strauss and Corbin (1990, p. 57) “… represents the operations by which data are broken down, conceptualised, and put back together in new ways”. Neuman (2011) suggests that the open coding facilitates finding themes from deep within the data. Strauss and Corbin (1990, p. 62) go as far as to say that “Without this first basic analytical step, the rest of the analysis and communication that follows could not take place”.

Chapter Three – Methodology

3.5.8.2 Axial Coding

Axial coding builds on the open coding process briefly outlined above, focusing more on the codes than on the data, with the aim of organising and expanding the theoretical framework by identifying linkages within and between the categories (Neuman, 2011). Axial coding observes the situations and conditions which give rise to a phenomenon and the strategies used to influence and manage that phenomenon (Strauss & Corbin, 1990).

3.5.8.3 Selective Coding

Selective coding is defined as “… the process of selecting the central or core category, systematically relating it to other categories, validating those relationships, and filling in categories that need further refinement and development” (Strauss & Corbin, 1990, p. 116). All codes drawn from the axial coding process need to be either directly or indirectly related to the focal core code. “These codes can be classified as representing context, conditions, actions, interactions and outcomes” (Douglas, 1997, p. 50).

3.5.9 Crystallisation of Results

The concept of crystallisation has been employed in the derivation of the results, where the same topic is seen from different perspectives by different interviews (Richardson, 2000), and the analysis has retained the validity of these perspectives. It replaces the concept of ‘triangulation’ and can typically be found in multi-mode presentations (Ellingston, 2009).

“Viewed as a crystalline form, as a montage, or as a creative performance around a central theme, triangulation as a form of, or alternative to, validity thus can be extended. Triangulation is the display of multiple, refracted realities simultaneously. Each of the metaphors ‘works’ to create simultaneity rather than the sequential or linear. Readers and audiences are then invited to explore competing visions of the context, to become immersed in and merge with new realities to comprehend” (Denzin & Lincoln, 2000, p. 6).

Chapter Three – Methodology

3.6 Data Analysis

Bernard (1988) suggests that a researcher needs to be able to describe and explain the phenomenon being researched. A description of a complex phenomenon can be express by dismantling the phenomenon into a set of its component parts. However, he warns that to stay true to the meaning of the data, excessively reduction of the component parts must be avoided. Further he states that an explanation of the phenomenon can be formulated by describing the rules that link those component parts together (Bernard, 1988).

The use of graphical networks is put forward by Miles and Huberman (1994) as a means of describing and explaining the phenomenon being researched.

A series of cause and effect diagrams or causal diagrams developed from the work Miles and Huberman (1994) were used in this research to visually depict the component parts of each case study. This technique of analysis was useful in finding the issues contained within the data, in identifying the relationships between these issues, and in supporting the process of interpreting and explaining (Halldorsson & Aastrup, 2003). The nodes of the graphical networks were developed from the concepts uncovered during the data coding process. The causal links, or relationships between the nodes were identified through an exhaustive analysis of the interview transcripts. In order for a relationship to be included into the diagrams, it needed to be clearly identified or strongly implied by the interviewee. Tables were developed using a spreadsheet to help in the process of theme identification. Sets of causal diagrams were developed for each of the interviews and they were grouped according to their identified theme.

One of the major advantages of using the causal diagrams is that they can offer a deep insight into the structure of the data and the relationships that exists, which in turn assists in the deeper understanding of the interaction at play within each set of data. Extensive analysis was performed on each of the transcripts so that the diagrams and tables would be saturated with information derived from each of their respective interviews.

Chapter Three – Methodology

3.6.1 Cause-Effect Relationships

Following the transcription of the interviews, each interview was explored by means of qualitative analysis technique to build up the interpretation through the coding process and the exploration of the various alternative explanations of the phenomena as detailed by the interviewee. During the process of coding the transcripts analytical notes were taken to assist in providing meaning and clarity, and to also aid the process of revising and enhancing the structure of the coding (Miles & Huberman, 1994).

The significant topics were compared across the different interviews looking for matches. Each interpretation and explanation was critically analysed to reassess their grounding in the data, the degree to which there was mutually self-consistency, and how plausible the explanations of the issues mentioned by the participants were. Illustrative diagrams were then developed to characterise and clarify the character of the cause-effect associations contained in the data.