Research techniques for data analysis

For the purpose of analysing the data gathered during the data collection stage of this research, two data analysis techniques were used. These are content analysis and cognitive mapping.

3.5.2.1 Coding and content analysis

The semi-structured interviews conducted within the research resulted in a significant amount of free-flowing texts that are generally called qualitative data. Due to the overwhelming amount of free-flowing data, the researcher decided on an appropriate technique that helps organise and analyse such data. Silverman (2001), Remenyi et al., (1998) and Weber (1990) recognise content analysis as a qualitative data analysis technique favoured by the qualitative researchers which involves creating a set of categories based on a large amount of free-flowing texts and later counting the number of instances that fall into each category.

There are two major forms of content analysis called quantitative content analysis and qualitative content analysis (Krippendorff, 2004), an area which had been subjected to frequent discussion among researchers. While quantitative content analysis involves a mere word count, qualitative content analysis takes the form of thematic or conceptual analysis. The former approach takes more of a quantification stance as it involves quantifying words or concepts in a free-flowing text and it tends to ignore the context within which they occur and thereby do not necessarily reflect the importance of such words or concepts within a given context. Moreover, a mere word count does not usually

83 count the synonyms in the data sets and further the multiple meanings given by the same words may mislead researchers. These weaknesses can be overcome by using the qualitative content analysis technique which goes on identifying the main concepts or themes of data and later categorising them into codes (Krippendorff, 2004; Hsieh and Shannon, 2005). Hence, qualitative content analysis involves identifying terms that explicitly or implicitly represent concepts/themes under consideration (Weber, 1990; Hsieh and Shannon, 2005).

Although this research attempts to identify direct quotations for each concept/theme, sometimes a notion is expressed via examples and related ideas. Furthermore, the knowledge and values of the researcher helps to determine what are recognised as facts and the interpretations which are drawn from them. This study relies on the qualitative content analysis technique over the quantitative content analysis because of its beneficial nature compared to the quantitative technique.

All qualitative research typically employs coding techniques to organise and analyse free flowing text because a coding technique helps “to move progressively from unsorted data

to the development of more refined categories, themes, and concepts” (Hahn, 2008: p. 5). The content analysis technique also employs the coding and categorising technique to analyse free-flowing subjective text. The coding process is defined by Coffey and Atkinson (1996: p. 26) as “the process of assigning tags or labels to the data, based on

the researcher’s concepts and simply it is a way of condensing the bulk of data sets into analysable units by creating categories with and from data”. Weber (1990) describes a category as a group of words with similar meaning or connotations.

Codes and categories can be developed at three stages within the research process: before, during and after data collection. Accordingly, they can be developed by means of identifying relevant concepts/themes from the literature review, the researcher’s own experiences within the study and further through subjective data collected from the free- flowing text (or relevant data collection techniques) (Ryan and Bernard, 2003; Hsieh and Shannon, 2005). It is therefore a matter of choosing the most appropriate coding method as there are two approaches to coding data that operate with slightly different rules; while Stemler (2001) describes them as priori and emergent coding, Krippendorf (2004) uses the

84 terms deductive and inductive coding, to mean the same. In priori/deductive coding, the categories are established prior to the analysis based upon some theory and once categories are agreed upon, the coding is applied to the data. In emergent/inductive coding, the categories are established as they emerge from the free-flowing text itself. Although emergent/inductive coding is an approach closely related to grounded theory, both priori/deductive and emergent/inductive approaches are useful in case study interview data analysis. In this context, this research uses both approaches.

Hahn (2008) describes the coding as a four staged process: level 1 (initial coding, open coding); level 2 (focused coding, category development); axial/thematic coding; and level 4 (theoretical concepts) as depicted in Figure 3-10.

Figure 3-10: Coding process

(Source: Hahn, 2008)

Initial coding takes place with open coding when the interview data are transcribed and put in the text format in which codes are identified without any restrictions to discover importance of meanings (Hahn, 2008). Open coding continues until theoretical saturation is achieved where no new codes or categories are being identified. Thereafter, open coding leads to the next stage in which the previously established codes are further examined by referring back to data (Hahn, 2008). This enables discovery of categories. Once the categories are developed, the next step, called axial/thematic coding, takes place in which the strong focus is placed on discovering codes around a single category. Without being restricted to this function, axial coding can also be used to develop categories and identify relationships between categories.

Level 1 coding Initial coding, Open coding Level 2 coding Focused coding, Category development Level 3 coding Axial/thematic coding Level 4 coding Theoritical concepts

85 Due to the difficulties in handling the content analysis process manually, the researcher had to rely on an appropriate computer software package that would support the researcher’s intellectual efforts and enable the content analysis process to run smoothly. Regardless of the nature of software packages, the success and the strength of the analysis still owe much to the judgement and the skills of the researcher. NVivo (version 8) was used in this research in order to facilitate this coding and content analysis process.

After importing the interview transcripts into the NVivo software, they were thoroughly investigated to discover the main concepts related to the study. Both inductive and deductive approaches were employed in identifying the concepts and thus the literature review, data on interview transcripts, and the researcher’s own experiences with the study were of immense use in identifying the concepts. While going through this process, a code list was developed. These codes were then assigned to each concept as and when they were identified from the transcripts.

The analysis was further iterated thoroughly where the initial concepts were modified to be more appropriate, adding new concepts as they appeared more relevant. These iterations continued until no new relevant nodes were identified. At the same time, free- flowing data were further examined and broken down into content categories which are related to a particular concept. NVivo software has two types of ‘nodes’ called free nodes and tree nodes. The term ‘node’ is used in NVivo to represent the identified concepts. The codes created from the above process were listed as free nodes where free nodes appear in a flat structure within NVivo. Later, the free nodes were then arranged in a hierarchical manner and converted into tree nodes, which corresponded to the key elements identified in the initial and refined conceptual models (during the expert interviews and case study interviews respectively).

After developing the tree nodes as described above, the coded texts were further analysed by refining the relationships between nodes. This process was facilitated by the cognitive mapping technique which is described below.

86

3.5.2.2 Cognitive mapping

Cognitive mapping is a technique for seeking out connectivity between events, ideas or arguments (Brightman et al., 1999). Eden and Ackermann (1998) identify it as a tool which can be used to structure messy or complex data. The structure of cognitive mapping eases decision making, reasoning, arriving at judgments, and making predictions about future events (Daniels and Henry, 1998). By using cognitive mapping, the issues/ideas can be structured into a hierarchical network. Thereafter, the relationships surrounding and supporting information behind the issues/ideas can be exploited and can be made explicit. Thus, the cognitive mapping technique can be used to bridge the gap between raw data and theory building. In this research, the process of developing cognitive maps was facilitated by a computer software package, namely Inspiration (version 8). This was used mainly to represent the themes and concepts identified through the content analysis process.

Inspiration software helps to organise the opinions of the interviewees and identify the relationships between them. Accordingly, the opinions of the respondents can be entered in the form of ‘concepts’ and different concepts can be linked to show their relationships and interdependencies. In this research, initially the coding structure that was developed with NVivo (the tree structure) was imported into the Inspiration software to create the basic hierarchy. Then the codes within the basic cognitive map were supported with the concepts extracted from the interviews transcripts. The concepts were entered in the form of short phrases and relationships were created between the concepts and codes to make explicit surrounding and supporting information.

Throughout this chapter, effort was taken to explain the philosophical assumptions, research approach and research techniques adopted in this study. The next section provides further details of various tactics applied to ensure the quality of the research.