Data analysis consists of a number of interconnected processes that help to summarize the data gathered and also to organize them in such a manner that provides responses to the research questions (Kothari, 2004). There are so many principles and strategies that must be observed when dealing with or doing data analysis. This study followed the suggestions of Mar Iman (no year), who states that when doing data analysis the researcher must:
Be objective Be accurate Be true
Separate facts and opinions, and Avoid wrong reasoning/argument
But before the researcher took the above advice into practical consideration he had first to understand the goal of analysis. Though it may differ from one research project to another, according to Mar Iman (no year) the goals of data analysis are:
To explain cause and effect phenomena. To relate the research with the real world To predict or forecast the real world To find answers to a particular problem.
To draw conclusions about real-world events based on the problem. To learn lessons from addressing the problem.
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In this study the analysis of data was done quantitatively and qualitatively. This is due to the fact that initially the researcher had chosen a mixed methods approach to conduct the research study.
3.10.1 Quantitative data analysis
As a quantitative measure, the data derived from the responses to the questionnaire were entered into the Statistical Package for Social Sciences (SPSS) programme to calculate the counts of frequencies and the percentages of key concepts. According to Cohen, Manion and Morrison (2007), in order to be able to use the SPSS programme productively it is necessary to understand the concepts that underpin statistical analyses. After the data had been entered into SPSS it was analysed using variables, which then gave results which were then turned into statistics. Statistics help to turn data into useful information to assist with decision making (www.bcps.org). Thus, in this research the data was coded, classified and processed, which assisted in summarizing it and describing patterns of relationships and connections. (These will be presented clearly in the “findings” chapter).
3.10.2 Qualitative data analysis
Qualitative data gathered using the semi-structured interview schedule was analysed through thematic content analysis. Semi-structured interviews allow for thematic analysis of the qualitative data (Anil & Charatdao, 2012). According to Cohen, Manion and Morrison (2007) data analysis involves organizing, accounting for and explaining the data; in short, making sense of data in terms of participants’ definitions of the situation, noting patterns, themes, categories and regularities. Terre Blanche and Kelly (1999) state that data analysis involves reading through your data repeatedly, and engaging in the activities of breaking the data down (thematising and categorizing it) and building it up again in novel ways (elaborating and interpreting it). In this study engaging with the data required a constantly close exploration of themes. Data analysis also involved carrying out an elaboration of data. Terre Blanche and Kelly (1999) argue that elaboration has a purpose, which is to capture the finer distinctions of meaning not captured by your original, possibly quite crude, coding system.
For it is usually the norm that when data was collected through, interviews, focus groups, and observation and or documentary analysis, content analysis will be used to analyse it (www.libweb.surrey.ac.uk). This research also used content analysis. Content analysis is defined as the procedure of categorizing data for the purposes of classification, summarisation and tabulation (www.libweb.surrey.ac.uk).
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In analysing the data qualitatively, the information was coded and classified, and this made it easier to make sense of the information received from the respondents. Moreover, to make things clear the researcher took the following steps in the content analysis (www.libweb.surrey.ac.uk):
Copy and read through the transcript - make brief notes in the margin when interesting or relevant information is found
Go through the notes made in the margins and list the different types of information found
Read through the list and categorise each item in a way that offers a description of what it is about
Identify whether or not the categories can be linked in any way and list them as major categories (or themes) and/or minor categories (or themes)
Compare and contrast the various major and minor categories
If there is more than one transcript, repeat the first five stages again for each transcript
When you have done the above with all of the transcripts, collect all of the categories or themes and examine each in detail and consider if it fits and its relevance
Once all the transcript data is categorised into minor and major categories/themes, review them in order to ensure that the information is categorised as it should be. Review all of the categories and ascertain whether some categories can be merged or
if some of them need to be sub-categorised
Return to the original transcripts and ensure that all the information that needs to be categorised has been so.
Quantitative research was used to describe the magnitude and distribution of change, while qualitative research gave an in-depth understanding of the social, political and cultural context. Mixed methods, therefore, assisted the researcher to triangulate findings which can strengthen the validity and increase the utility of work researched (www.libweb.surrey.ac,uk).