Qualitative data analysis – thematic data analysis

Qualitative data collected during research can be analysed in different ways using one or more of the various methods such as: thematic analysis, descriptive approach, or more in-depth methods. Thematic data analysis is used for the analysis of qualitative data collected in the course of this research.

Thematic data analysis looks across all the generated data to see or identify the common issues that occur (Fereday & Muir-Cochrane 2006). It seeks to find the main theme(s) that summarizes all of the views of the data collected. This method is the

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most commonly used for descriptive qualitative projects and research. The following were steps used in the thematic data analysis of this research.

 Collect all data. – Data was collected with the use of pen and paper (scribbled notes), which was the method acceptable by all of the research participants

 Transcribe conversations in order to start to see and list the patterns of experiences of the participants. - This was done mainly by paraphrasing common ideas. I ensured the use of direct quotes was omitted due to the high profile of the research participants. This is also to ensure confidentiality and anonymity which is quite crucial considering the cultural context of Nigeria.

 Identify data that relates to the already classified patterns.

 Combine and catalogue related patterns into sub-themes - Themes are derived from patterns such as recurring activities, meanings, and conversational topics (Singh 2006). They are identified by bringing together experiences, components or fragments of ideas which are meaningless when viewed alone.

 Build a valid argument for choosing the themes by studying related literatures.

The initial plan was to use the Nvivo software for coding and thematic data analysis in order to find out the recurring words and themes. However, the use of pens was used for coding (see appendix 8). . Aside the problems encountered while loading the Nvivo software on my computer (such as difficulty in installation, and eventual system crash), I opted for the pen and paper option for the following reasons:

1. Nvivo does not recognise or process Microsoft-Word easily. It insists on rich text format. This means all transcripts have to be formatted to rich text format for Nvivo to recognize it.

2. Another challenge of some software packages is the issue of over coding. I wanted to ensure the core messages that were conveyed during the interviews

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were not lost due to over coding. With the use of software packages, the researcher could end up with too many themes and categories that could make the process of categorizing the data unmanageable (Welsh 2012).

The use of software also brings the limitation of only seeing a section of the material on the screen which poses the difficulty of fully visualizing and conceptualizing the whole data (Mclafferty 2006).

How were the themes generated?

The themes from the interviews were generated in two ways:

1. By highlighting and noting down some recurring notions from the interviewees during the interviews (see an example in appendix 9).

2. By pen and paper coding of the interview notes developed after each interview to identify common patterns and themes (see an example in appendix 8).

Indeed, the themes generated were a result of the synchrony between the noted recurring notions made by the interviewees during each interview and those discovered in the course of the pen and paper coding of the interview notes. In appendix 7, extracts from the interview notes from different interviews were presented. Appendix 9 shows some examples of recurring themes and statements during the interviews. The interview protocol is presented in appendix 4, while the coding frame for the interviews is presented in appendix 10.

With respect to the theoretical lenses, the coding process was not directly influenced by the theoretical lenses. What really happened was that the coding went on and the themes generated were now compared to see what theoretical lenses they supported. In essence, the interview protocol were influenced by the theoretical lenses, but not the coding process.

108 3.5.3. Assessing validity and reliability

Reliability measures the consistency of data over time. Validity tests the degree of truthfulness of the source of data presented in a research. A good and reliable research must have three essential qualities:

1. It has to be relevant 2. It has to be verifiable 3. It has to be unbiased.

In this research, relevance was measured by ensuring that the evidence collected were up-to-date and that they directly explain the subject matter under study. Evidence from archival records, trade journals, academic literature, policy documents and interviews were collected to determine the aftermath of events and happenings over a period of time in relation to the subject under study. The evidence collected gave ideas about the various contexts that have impacted on energy infrastructure supply in Nigeria over time.

Since evidence are verifiable facts, testimonies, expert witnesses or pieces of statistics; this research has focussed on evidence that are verifiable through archival documents and interviews. If the same sets of documents and a similar sample frame is used to conduct this research, (following the arguments) the researcher should be able to reach the same set of conclusions (Creswell 2003).

Biases are preconceived notions or opinions that are not based on actual experience or reason. The issue of bias in this research was addressed by ensuring that the findings and conclusions were data driven. However, there is also a limitation since the research was done by an individual, to limit the possibility of bias, all of the findings of this research have been peer-reviewed by others and have been published in highly

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ranked academic journals. Throughout this research, I have kept the bias limitation in mind and further studies would either support or demonstrate where bias may have appeared. Indeed, I have considered this point from the very start of this research (Podsakoff et al. 2003).

Within a mixed methods research context, the importance of triangulation is emphasized (Yeasmin & Rahman.K.F 2012). Since this research used mixed methods, I have focused more on triangulation. Validation is a positivist notion. Critical realists acknowledge that subjectivity (plus bias) is inevitable. Indeed, in this study validation does not mean that the same thing is found, but together the data can point towards the rest layer which may be less influenced by real world experience.