Data Analysis

Interpretive analysis is an iterative, inductive process of decontextualisation and recontextualisation (Ayres, Kavanaugh, & Knafl, 2003; Morse & Field, 1995). Different interviewees have different opinions of the same questions, therefore, in order to understand the relationship within the data, the recontextualisation process is required to extract context and meaning from the primary data – decontextualisation.

During decontextualisation the analyst separates data from the original context of each collection of interview data and identifies codes to understand the inter- relationship in the data, and so reintegrates, categorises and diminishes the data to

formatting the similar concepts. Interpretive methods extract contextual data for developing categories or concepts, which helps to achieve the research outcome.

4.2.1 Identification of Data Analysis Methods

During the initial stages of data analysis, the possibility of using computer assisted qualitative data analysis software (CAQDAS) such as NVivo, Atlas and Nudist was explored, looking at the pros and cons. This involved examining how software would affect the process as well as the end product of the research, and if software was used what would be the most appropriate.

There are advantages and disadvantages to using computer assisted qualitative data analysis software (CAQDAS) for data analysis. Barry (1998) states that CAQDAS helps to automate and thus speed up the coding process; provide a more complex way of looking at the relationships within the data; provide a formal structure for writing and storing information in order to develop the analysis; and help more conceptual and theoretical thinking with regards to the data.

However, based on the data analysis literature, there are concerns with regards to using data analysis software, such as negative associations with technological advance, which is identified by Seidel (1991), as he points out that the major worries are that: using software will distance people from their data, because continuing data analysis requires the necessity to re-read data as the entire transcript in categorised chunks, also it needs reading over and over again to analysis and develop theory in- depth. On the other hand, using software will lead to qualitative data being analysed

quantitatively; and, it may lead to increasing homogeneity in the methods of data analysis.

In the end, software was not used because it was believed that the traditional methods of annotating sections with highlighter pens would be more effective. As a bricoleur, researcher and DIY artist it was for the benefit of this research to analyse the data by hand.

4.2.2 The Role of the Analyst

For qualitative data analysis, the researcher acts as the instrument for analysing the data, because the research identifies all the coding, categorising similar concepts; thus, evaluating the inter-relationship within the data and developing the research outcomes (Strauss & Corbin, 1998). The data analysis process begins by identifying the coding for data categorisation, and then the analyst develops a theory around a main category that explains the core phenomenon within the data.

Generally, the aim for using grounded theory is to create theory, the findings of a complete theory often demonstrate the relationship between the core category and the other dominant categories. Therefore, grounded theory has been used in various research areas by researchers who are interested in designing interventions to support people engaged in the social processes explained by the theory, and other researchers who design studies to test the theory in practice. However, there are still some researchers who just use it for identifying the samples within and between the categories.

4.2.3 Data Analysis Process

Therefore, there are many different resources available to help with learning the qualitative data analysis process (Miles & Huberman, 1994; 2002; Silverman, 2001). They evaluate that for the qualitative data analyst, whether it is carried out by using computer programs, such as NVivo or manually, the principles are the same, although there is no strict set of rules. Hence, Williamson and Bow (2002) conclude by identifying the detailed analysis processes for providing processes on how to code qualitative data. The following are the steps for processing data analysis:

1. Transcribe the data so that it is in printed form.

2. Read through the data, making notes and identify the key points.

3. Categorise or label passages of data according to content so that identically labelled or categorised data can be retrieved as needed, as well as the data that relates to the category. Initially broad categories are subdivided to be more precise as the analysis progresses.

4. Categorise the related concepts. This should start early in the process and continue throughout. It means thinking about the similarities, differences, and relationships between the categories (Miles & Huberman, 1994).

Furthermore, Dey (1999), Strauss and Corbin (1998) also confirm that using grounded theory for data analysis involves a constant comparison method of coding and analysing data through three stages:

1. Open coding used for examining, comparing, conceptualising, and categorising data

2. Axial coding aims to reconstruct the data into groupings based on their inter- relationships and samples within and among the categories identified in the data

3. Selective coding, which helps with identifying and describing the central phenomenon, or “main category,” in the data. Ideally, each interview and observation is coded before the next is conducted so that rich and valuable data can be collected (Starks and Trinidad, 2007).

Hence, based on grounded theory as an influence, the data was broken down into categories as key words were coded using different coloured highlighter pens to differentiate between each section.

This meant rigorously reading and re-reading the data collected and attempting to find links and patterns specifically looking for inter-relationship within or between

the data, which finally evolved into five stages of analysis in the diagram below (Figure 8).

Figure 8 Data analysis stages 1-5