Chapter 4 Research Design and Methodology
4.4 Data Analysis
P a g e | 91 preconceptions he might have had about Nigeria. Otherwise this would not have helped the study in any way. As for the textual data that is rich in meaning, it was really difficult to summarise it and structure it properly.
P a g e | 92 to Swetnam (2004:86), “Purely descriptive, qualitative data can be tricky to present and to avoid tedium they need careful editing and presenting in blocks which can be profitably broken up with sub-headings.” Some of these may not require presenting in full and parts can be relegated to the appendices. However this is somewhat tricky for the purposes of this study as it is mainly secondary data research. Such presentations normally work well if there are interviews involved.
Analysing data is also not without its faults just like everything else in research. However some of these faults are made by the researcher and can be avoided. One of these includes drawing inferences from data that are not supported by the data. According to Mouton (2001:110), this simply means that “Conclusions that one may draw on the basis of any data set need to have sufficient and relevant inductive support before they are acceptable.”
Another error could be the biased interpretation of the data through selectivity. Mouton interprets this to mean that scholars very often attempt to prove their pet hypotheses without proper consideration of rival hypotheses and alternative explanations (Mouton, 2001:110).
This means that a researcher should be careful of falling into the trap of thinking that their interpretations or analysis are correct without first checking them against other alternatives.
It has been stated earlier that the data need to be coded into themes differently. However the great thing is that there is no right way to code textual data. One excellent guide to assist the researcher in understanding the coding process is provided by Roberts (2004:143-145). She describes, in eight steps the systematic process to analyze textual data:
1 Get sense of the whole. Read all the transcriptions carefully. Perhaps jot down some ideas as they come to mind.
2 Pick one document- the most interesting one, the shortest, the one on top of the pile. Go through it asking yourself, “What is this about?” Do not think about the “substance” of the information but its underlying meaning. Write thoughts in the margin.
3 When you have completed this task for several informants, make a list of all topics.
Cluster together similar topics. Form these topics into columns that might be arrayed as major topics, unique topics, and leftovers.
4 Now take this list and go back to your data. Abbreviate the topics as codes and write the codes next to the appropriate segments of the text. Try this preliminary organising scheme to see if new categories and codes emerge.
P a g e | 93 5 Find the most descriptive wording for your topics and turn them into categories. Look for ways of reducing your total list of categories by grouping topics that relate to each other.
Perhaps draw lines between your categories to show interrelationships.
6 Make a final decision on the abbreviation for each category and alphabetize these codes.
7 Assemble the data material belonging to each category in one place and perform a preliminary analysis.
8 If necessary, recode your existing data.
Although the above eight steps may not exactly apply to the current study as they are, however they have provided an idea of how the data were analysed. This is because the study is analysing existing data and there have been no interviews, questionnaires or surveys conducted. However these steps were more or less followed when analysing the data.
After the data has been analysed it is important for a researcher to come up with a way to ascertain whether the findings are valid. This is done to give the research some form of credibility to whoever is reading it. According to Roberts (2004:145), “Qualitative researchers often use the term trustworthiness to refer to the concept of validity. It’s the credibility factor that helps the reader trust your data analysis. In qualitative studies techniques such as triangulation, member checks, and interrater reliability are used to validate findings.” An interrater reliability is most suitable for this research as it involves two or more people independently analysing the same qualitative data and then compare the findings. In the case of this study both the researcher and his supervisor who is an expert in international relations studies embarked on this process of multiple analyses to reduce the potential bias by the single researcher collecting and analysing the data.
As indicated earlier, the type of the method used to analyse the data is discourse analysis.
Jorgensen and Phillips (2002:1) define discourse analysis as, “discourse as a particular way of talking about and understanding the world (or an aspect of the world).” This is exactly what this study has sought to do from the beginning, and that is trying to understand a particular aspect of the world. That world is that of the two countries, Nigeria and South Africa, who are amongst the great players in the world due to their rich endowment in natural resources such as minerals and oil.
Furthermore Jorgensen and Phillips provide an example of how discourse analysis can be used in research. According to the authors, it can be used as a framework for analysis of national identity.Many different forms of text and talk could be selected for analysis(2002:2).
P a g e | 94 This is what this study was all about and that is studying the identities of both Nigeria and South Africa in trying to find out the implications for both these countries now that Nigeria is the biggest economy in the African continent.