Compromiso de Río de Janeiro y Plan de Acción eLAC 2007.

Analysing qualitative data involves the researcher attempting to make sense of the views expressed by the research participants on the phenomenon being studied, bearing in mind that such views may be subjective and socially constructed. Yin (2003) and Saunders et al. (2012) suggest that the researcher carrying out one or more of the following sums up this process:

i. Summarising some parts of the data in order to condense them.

ii. Grouping data according to themes by categorising them in order to make sense of the data.

iii. Linking data categories in a manner that provides the researcher with a structure(s) to answer the research questions.

Data analysis is an important aspect of qualitative research and is central to the interpretive philosophical paradigm. Owing to the nature of qualitative research, the data obtained are non-standardised, complex in nature and likely to be in large volumes (Saunders et al., 2012). In comparison to the ‘thin’ abstractions or description primarily generated from quantitative data, qualitative data brings about ‘thick’ or ‘thorough’ abstraction or deductions (Dey, 1993; Brekhus et al., 2005).

3.9.1 Approach to analysis

While Yin (2009) agrees that the application of a standardised approach to analyse qualitative data is far-fetched, Saunders et al., (2012) points out that the approach being used is determined by the deductive, inductive or abductive nature of the research. While there is no rigidity in the analytical approach to be adopted in analysing qualitative data, Saunders et al. (2012) again point out that the focal objectives in carrying out such analysis are to:

1. comprehend often large and disparate amounts of qualitative data; 2. integrate related data drawn from different transcripts and notes; 3. identify key themes or patterns from them for further exploration;

4. develop and/or test theories based on these apparent patterns or relationships;

5. draw and verify conclusions.

De Vaus (2001) points out that the main objective of data analysis is to treat all evidence fairly in order to develop persuasive and analytic conclusions, and also to create supernumerary interpretations.

To break the data analysis process down, the researcher subscribes to the proposition of Saunders et al., (2012) to carry out data analysis in the following stages:

1. Categorising

The first stage involves the classification of data into analysable categories, to which meaningful ‘bits’ and ‘chunks’ of original data can be attached subsequently. Identifying these categories is guided by the research purpose, based on the aim and objectives. Thus the data collected in this study from the semi-structured interview conducted was classified into three categories namely:

i. African nations participation in the Olympics.

ii. Performance in the London 2012 Olympics and challenges. iii. Strategies for addressing challenges/improving performance

2. ‘Unitising’ data

This stage involves the researcher attaching relevant ‘bits’ or ‘chunks’ or ‘units’ of data to the above categories that have been identified. Here, the research considers the unit of data to be relevant words, sentences, paragraphs or other chunks of textual data that fit into the categories listed above.

3. Recognising relationships and developing categories

This stage represents the entire process of the data analysis as it involves the generation of categories and reorganising data accordingly. Yin (2009) considers this stage of data analysis very crucial as it involves a continuous search for key themes, patterns or relationships in the reorganised data.

4. Developing testable propositions

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such, this stage requires the researcher to develop testable propositions in seeking to reveal patterns and recognising relationships between categories within the data. Testing propositions that emerge inductively from the data involves seeking negative examples and alternative explanations that contrasts with the relationship or pattern being tested. While this is different from statistical hypothesis testing as in quantitative analysis, Miles and Huberman (1994) uphold that testing propositions identified helps the research in formulating valid conclusions and developing an explanatory theory, no matter how simple the theory is.

3.9.1.1 Thematic analysis

In view of Yin’s (2003) proposition, the analysis for this study was carried out through a discussion of the key themes identified from the interview transcripts. This type of analysis is also referred to as thematic analysis. Thematic analysis according to Braun and Clarke (2006) is “a method for identifying, analysing and reporting patterns (or themes) within data” in qualitative research. It is seen as a categorising strategy for qualitative data as it involves the researcher reviewing the data collected and making notes in order to sort it into categories. The simple and flexible nature of this analytic strategy helped the researcher transit the analysis from a broad interpretation of the data to identifying patterns and developing themes, which provided a richer interpretation and understanding of the data collected in this study. Thematic analysis also helped bring the researcher closer to the data as a deeper appreciation of the data content was developed.

Furthermore, Boyatzis (1998) points out that thematic analysis is a process of “encoding qualitative information” This implies that the researcher can develop ‘codes’, phrases or words that help to label the sections of the data. Codes can be developed in different sizes or shapes depending on the research question to be answered and the methodology adopted. Boyatzis (1998) further explains that a set of codes may refer to “a list of themes, a complex model with themes, indicators, and qualifications that are causally related; or something in between these two forms”. Therefore, the analysis carried out in this research was a discussion of the themes developed from the data presented in chapter five.

 Techniques used for theme identification

The variation in methods for analysing research data makes it difficult, if not impossible, to apply a universal concept in theme identification, particularly in qualitative research. Typically, themes are induced from empirical data – from sounds, images and texts (Ryan and Bernard, 2003). But even with a fixed set of open-ended questions, Dey (1993) notes that it is impossible for the researcher to anticipate all the themes that develop prior to analysing the data. This means that, although the researcher could expect to find certain themes from the data, some themes could arise from the data unexpectedly and if analysed, could lead to useful findings.

Ryan and Bernard (2003) note that there is no ‘right or wrong’ approach for identifying themes from the data. Tesch (1990) also recalls that the choice of a technique for theme identification is relative, as individual researchers have different recipes for arriving at a set of themes. The process of identifying the key themes in the data from this study commenced in earnest with the transcribing of interview recordings. Having the sole responsibility of conducting the interviews and subsequently transcribing them, helped the researcher become more familiar with the key issues that were developing from the data. Ryan and Bernard (2003) argue that the researcher’s involvement in handling the data is always helpful for finding themes. Once the interviews had all been transcribed, the researcher then started to paw through the written texts, underlining key phrases and marking up the key issues that had been raised with different coloured markers – one colour to a new issue, making it easier to revisit the issues subsequently. Bogdan and Biklen (1982) suggest reading over the transcribed text at least twice, which the researcher did, to ensure a full awareness of the key issues. Sandelowski (1995) further observes that proofreading the written texts and simply marking key phrases is a good first step towards identifying and analysing themes. To apply a more systematic approach in identifying the themes discussed in this study, the researcher adopted a combination of the following three techniques suggested by Ryan and Bernard (2003):

I. Repetitions: These are mainly topics within the data that “occur and reoccur” (Bogdan and Taylor, 1975) or are “recurring regularities (Guba, 1978). D’

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talk…knows how frequently people circle through the same network of ideas”. For instance, in the interviews conducted with the NOC Presidents/Secretary Generals, it was found that the participants repeatedly made references to ideas associated with specific issues such as sponsorships, funding, planning, among other issues. Thus, the researcher concluded that these ideas were important themes in the performance of the participants’ countries at the Games. In a similar fashion to that adopted by Strauss (1992), the researcher in showing the relationship between these ideas, wrote the concepts on a piece of paper and used lines to connect them with the participants’ verbatim expressions – the more the same concept occurred in the text, the more likely it was a theme. This process was replicated with all the interviews carried out. II. Similarities and differences: This technique involves looking for similarities

and differences by making systematic comparisons across units of data. Glaser and Strauss (1967) refer to it as the “constant comparison method”. In applying this technique to this research, the researcher compared pairs of expressions from the various research participants in order to establish the differences or similarities between these expressions. The abstract similarities and differences generated from this process formed the data themes. For example, the issue of funding was a common issue pointed out by different participants to have had an impact on their country’s performance. A comparison of expressions by the participants showed that the points they were making regarding this issue were very similar. Therefore the researcher considered the issue to be a theme.

III. Word lists and key words in context (KWIC): The word list and the KWIC technique simply involves an observation of the words used by the participants. To generate word lists, Ryan and Bernard (2003) note that researchers first identify all the unique words in a text and then count the number of times each occurs. This word-counting technique helped the researcher condense the data to allow concentration on the core points raised by the participants (Tesch, 1990). One risk of using word lists and counts is that words can be taken out of their original context. However, the researcher, fully aware of this risk adopted the KWIC approach to prevent this risk. This approach ensured that each key word or phrase identified were systematically searched within the text to make sure that all instances of it’s appearance in

the text had similar contexts. Using this technique, themes were identified by simply sorting the examples into piles of similar meanings.

It is worthy of mention that there are some computer programs that can be used to identify and organise research data in a quicker fashion. An example of this is NVivo. However, the researcher’s reluctance in using this tool was a conscious move to maintain control over the data analysis process at all times, rather than face the risks, limitations and uncertainties posed by the use of technology. Although the techniques adopted to identify the themes in this study proved laborious to execute, the researcher believes that the meticulous combination of these techniques increases the reliability and validity of the findings that have emerged from the data.