Data analysis

Qualitative data analysis encompasses selection, examination, categorisation, tabulation, reviewing and/or amalgamation of quantitative and qualitative data to address the initial notions of the research study (Yin, 2003:109). According to Ritchie and Lewis (2003:199 & 220), the analysis of case study evidence is challenging since it is a continuous process that begins with the organisation of large amounts of unwieldy and detailed rich raw data. It continues with several cognitive taxing phases, for example, the immersion of data, generating themes and categories, data interpretations and drawing conclusions until it ends with the final written report that discloses the results (Marshall & Rossman, 2006:156).

Qualitative interpretation differs from quantitative analysis, since there are no clearly agreed rules or measures to analyse data and approaches vary in terms of the principal focus and objectives of the analytic process. However, in recent times, there has been a considerable improvement in terms of literature on a variety of approaches to execute qualitative data analysis. Consequently, the ethnographic content analysis approach (Mouton, 2001:149-150) was applied and utilised for the interviews and documents and by identifying themes, examining the way in which they were presented and the frequency of their occurrence (Ritchie & Lewis, 2003:199-200). Ethnographic content analysis was a practical approach to investigate experiences of the advertising agencies and allowed for a descriptive analytical framework to be created to organise the case studies (Yin, 2003:114). The descriptive framework was realised by implementing the following sequential elements of qualitative data analysis: data reduction; data structure and display; and data interpretation; and conclusion formulation (Miles & Huberman, 1994:10-11).

5.5.3.1 Data reduction

Qualitative research generally yields a large volume of unwieldy data that may take the form of hours of recordings (11 hours for the twelve participants in this BEE study), numerous pages of transcripts from interviews (a total of 50 pages), extensive documents (a total of 60 pages in the form of BEE certificates and EE plans) and a wide range of other documents (such as the EE registries, theses, BEE surveys and so forth), all for which the researcher needed to find a means to manage the data (Ritchie & Lewis, 2003:202).

Data reduction is a form of analysis that sorts, focuses, discards and organises data in such a way that conclusions can be drawn and verified, but it is important not to eradicate the context in which the data occurred. It takes place constantly during the study until the completion thereof and even prior to the data being collected. Anticipatory data reduction transpired as the researcher decided which conceptual framework, cases, research questions and data collection techniques to utilise (Miles and Huberman, 1994:10-11).

An important step in data reduction involves segmentation of data by assigning symbols, words, names or a combination of these, with the purpose of identifying information and creating interpretive constructs for further data analysis (Merriam, 1998:164). Some researchers use inductive coding, whereby they develop codes as they code the data, while others establish the codes before examining the data, which is termed priori coding (Maree, 2007:109). The researcher used priori coding to establish the themes, since the same questions were asked in the semi-structured interviews; therefore, each question represented a particular theme. This process of establishing the themes beforehand is also known as deductive analysis (Marshall & Rossman, 2006:159). Since the questions were not asked in any particular order, the next step was to organise and combine the data into relevant themes for each case, until all of the data had been sorted.

The initial descriptive stages require scrutiny of each specific theme across all of the cases and then highlight the range of perceptions, attitudes, views and experiences that have been denoted as part of that theme. A variety of colour highlighter markers were used to label related pieces of data as means to identify different views and elements that had emerged. Once all the data had been marked and the range was established, the data was sorted into key elements within the range, which revealed a number of categories (Ritchie & Lewis, 2003:237-239). Marshall and Rossman (2006:158-159) advocated that categories can be equated to the baskets or buckets into which clusters of associated and recurring patterns of text are placed.

The transcripts were then reread to ensure that all of the data was correctly categorised and further refined where necessary, as well as to verify that none of the data had been misinterpreted and that an unbiased view was taken by the researcher. The refining process again involved the use of Coloured highlighter markers to indicate correlations and divergent views between categories and in some instances new categories emerged, while others were amalgamated. A total of forty-nine categories, some with up to eight sub-categories, emerged from the questions. The researcher was also careful to retain the original words of participants as far as possible in order not to lose the context and thus retain the essence of what was said (Ritchie & Lewis, 2003:230).

Once the mass of data had been reduced to make it more manageable and the categories were determined for each theme, it was then structured into diagrams in order to facilitate interrelations and associations that are normally difficult to spot in a large quantity of text (Ritchie & Lewis, 2003:204 & 214).

5.5.3.2 Data structure and display

Categories should be explored in terms of similarities, differences, contradictions, interrelations and evidence that affirm or challenge interpretations between the categories and cases that are

explored (Maree, 2007:110). Miles and Huberman (1994:11) propose that a simple means to structure the mass of processed data in a condensed and easy to read format, is to visually display and/organise it by means of diagrams. These visual displays, in a compressed format, allow the analyst to see what is happening, analyse further and formulate conclusions, as well provide readers with an opportunity to speedily absorb large amounts of data at a glance.

No separate sections or diagrams were dedicated to individual cases, but were displayed in a format that combined answers to each question (across the twelve advertising agencies) in tables and figures (in this instance graphs), although quotes and significant data from individual cases, are presented in condensed vignettes. A principal advantage of using the question-and-answer format for multiple case designs is that the answers to the same questions should be examined to commence with cross-case analysis and readers can review the questions that are of most interest to them without any difficulty (Yin, 2003:147-148). Network illustrations were also generated by utilising Atlas.ti, which is a computer assisted qualitative data analysis software (CAQDAS) program, to graphically depict relationships between the different themes and categories.

Applicable excerpts from the transcripts and documents were placed into tables for each category, with a different colour denoting each participant, so that the reader can easily follow the views and sentiments of individual advertising agencies for each question. All of the participating advertising agencies were guaranteed that the data provided was strictly of a confidential nature and that their names or responses that would serve to identify their advertising agency, would not be disclosed in the dissertation. Any information that would identify participating advertising agencies was paraphrased in a way that would retain the essence of what was said, but would protect identities, since the information was sensitive. The bulk of the sensitive information was located in the quantitative data, which was transformed into graphs (thereby concealing individual advertising agencies identities) by using Microsoft Office Excel 2003 and was used in collaboration with qualitative data for the triangulation process.

5.5.3.3 Data interpretation and conclusion formulation

Categorisation of the data into themes and categories are essentially descriptive summaries of what participants have said and done, but nevertheless signify some level of interpretation (Merriman, 1998:187). The next step is to obtain an analytical understanding of the data in order to establish how it substantiates or contradicts and brings new understanding to an existing body of knowledge.

The researcher defined concepts and the nature of the phenomena, searched for patterns and

associations within the data and provided explanations in order to uncover new meaning and insights from the data (Maree, 2007:111-113). The explanations were derived in a number of ways:

some were given by the participants; others emerged from the number of times a phenomenon occurred across the cases or came from comparisons with other studies (or surveys) on BEE (and transformation) from different industries or countries (Ritchie & Lewis, 2003:252-255).

Conclusions that were drawn from the data were compared to the literature review and show significant connections and also deviations when compared with this BEE study’s findings (Mouton, 2001:124).