Analysing data depends on types of data which is either non-numeric or numeric. Therefore, there are two types of methods for analysing data; qualitative and quantitative.
3.9.1 Qualitative Method
Qualitative data refers to non-numeric data or data that have not been quantified and can be a product of all research strategies (Saunders et al., 2012). According to Saunders et al. (2009) and Denscombe (2010), there are five different methods for analysing qualitative data; content analysis, thematic analysis, grounded analysis, discourse analysis, and comparative analysis.
The Content analysis is a systematic technique for obtaining ideas that have been decided in advance and the data for constructs by means of transcription and coding the sentences that are compressed into the theme.
The Thematic analysis is a highly inductive analytical approach whereby themes emerge from the data collected and not imposed by the researcher.
The Grounded analysis uses categorisation and coding collected data in order to derive theories and concepts from meanings within the data.
The Discourse analysis is based on conversation; the way in which individuals talk and what persuades them to talk. The conversation or speech is analysed as performance rather than the state of the mind
The Comparative analysis refers to comparing data from different individuals until no new issue arises. This type of analysis is connected to the thematic analysis.
3.9.1.1 Semi-Structured Interview Data Analysis
As mentioned in Section 3.8, semi-structured interviews were used to collect qualitative data. These interviews were digitally recorded with an average duration of 60 minutes. Easterby- Smith et al. (2008) state that “full record of the interview should be compiled as soon as possible after it has taken place”. This view is supported by (Saunders et al., 2012) who believe that there is a need to “create a full record of the interview soon after its occurrence to control
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bias and to produce reliable data for analysis”. The important factor about qualitative data analysis is exploring the meaning through what is experienced and reported by the interviewees and what is observed by the interviewer. The aim of analysing qualitative data is to identifying pattern, concepts, themes and meanings. The qualitative data analysis is described by Bogdan and Biklen (2003) as “working with the data, organising them, breaking them into manageable unit, coding them, synthesis them and searching for patterns”. The process of qualitative data analysis begins with transcribing interviews followed by open coding of the data, which is the categorisation of data in order to identify patterns, themes, and meanings that emerges from the data. In this process, the whole data is initially explored and then the researcher reconstruct it again in a more meaningful way. This categorisation enables the researcher to compare and contrast between patterns, and deeply reflect on certain patterns of the data in order to understand them.
According to Richards (1999), a content analysis software package such as NVivo 10.0 could be used to synthesis and manage themes from large amount of qualitative data by organising data into manageable nodes (themes). The semi-structured interviews are analysed using the content analysis method to organise data into general themes. In order to make sense of the data, open coding of the data is used, which is the process of recording the number of responses that a particular interviewee gives to a question. Then thematic content coding of the interview transcripts is used to analyse responses. Initially, each transcript is individually analysed for identifying key themes. In the next stage common themes shared between interviewees are identified. These common themes are merged into new nodes as shown in Figure 3.6.
Furthermore, Interpretive Structural Modelling (ISM) is a quantitative technique to analyse qualitative data. This approach has been used by researchers to identify and represent interrelationships among various variables related to the issue (Raj & Attri, 2011). Although, there are other approaches for analysing qualitative data by using quantitative technique like Interpretive Ranking Process (IRP), Analytic Hierarchy Process (AHP), and Total Interpretive Structural Modelling (TISM), all these approaches express factors by ranking and not the interrelationship between the factors. Since the objective is not to rank the challenges in relation to performance of TKI but influence on themselves affecting the process of TKI within the TPS, the ISM approach is employed for conducting this research.
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Figure 3.6 – Snapshot of Content Coding in Nvivo 10.0
3.9.1.2 Interpretive Structural Modelling – ISM
The ISM approach uses practical experience and knowledge of experts based on various management techniques like brain storming, nominal group technique, etc. to unravel a complicated system into several elements and construct a multilevel structural model (Warfield, 1976). In other words, it is a well-established approach that can be used to identify and summarise relationships among specific variables which define an issue or a problem. The ISM technique is an interactive learning process that develops a comprehensive systematic model through structuring a set of directly and indirectly related variables. The ISM-based model represents the structure of a complex issue or problem in a designed pattern. This model depicts the direct and indirect relationships between the variables that describe the situation more accurately. In other words, it develops insights into collective understandings of these relationships. The process of ISM is further discussed in Chapter 4 Section 4.5.
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