113 attempting to save, download, print and scan documents for future reference.
114 The analysis of data in this study was guided by Creswell’s (2009, pp. 185-190) explanation of the analysis and interpretation of qualitative data as involving six generic steps to be found in most qualitative analysis methods and procedures. These six steps are encapsulated in the following figure (adapted from Creswell, 2009, p. 185):
Figure 3-1: Steps in qualitative data analysis and interpretation (adapted from Creswell, 2009, p. 185)
The steps shall now be discussed with special emphasis on how they manifested in the context of this study regarding the analysis of the raw data emanating from the interviews and examination of organisational documentation:
3.5.1 Step 1: Organizing and preparing data for analysis
This involved transcribing the interviews and incorporating all notes made by the researcher during the course of the interviews. This was done by inserting the notes into the transcribed interview data at the time and/or question in which they were observed. This ensured that when the researcher read through the transcribed interview data, she was able to be simultaneously aware of the contents of the interview, as well as ancillary information relating to what was
Raw data (transcripts,
field notes,
images, etc.)
1. Organizing and preparing
data for analysis
2. Reading through all the data
3. Coding the data
4. Developing themes and descriptions
5.
Interrelating themes and descriptions
6. Interpreting the meaning of
themes/
descriptions
115 perceived by the researcher in response to particular questions in the interview such as the respondents’ body language, attitude, apprehension, etc.
With regard to this step for the data acquired from the examination of documentation and archival data, information found on the organisation’s official website and online internal repository was printed, read and saved for later reference.
3.5.2 Step 2: Reading through all Data
This step involved reading and re-reading through all the transcribed interviews with their ancillary notes, as well as a re-examination of the significant points in the documentation. The first reading allowed the researcher to get a general idea of what emerged from the fieldwork and documentation analyses. The second reading enabled linkages between the various interviews, as well as between data from the two different sources, to emerge. In this way, a more holistic picture began to emerge of the sustainability issues encountered by, and influencing Oxfam. This also allowed finer discrepancies between the various interviews to be noticed, which is important as the recognition of data that differs from the rest of the data set is especially significant in qualitative studies.
3.5.3 Step 3: Coding the data
Coding of the data occurs when the data is analysed and categories are delineated from the whole data set. It is a method of organizing the learning emerging from the data set via the use of key words, tags or labels that are able to exemplify the essence of the category of data they are allocated to (Basit, 2003, p. 144). These codes are then compared across the data sets in a method known as the Constant Comparative Method (CCM). According to this method, once the data is coded into particular categories, the data is then compared to similar pieces of data, as well as ones that are different in order to determine the linkages between them (Thorne, 2000, p.
69). This basically means that all pieces of data are compared with each other to ascertain whether there are any relationships between them and if so, what is the nature of such relationships and what implications does it have for the data set as a whole.
In this study, once the interviews were transcribed and read, the researcher began to compare each interview with the next. This began initially within the mind of the researcher as she read and re-read through all the interview data. Therefore, linkages already began to emerge in the organizing and reading stages mentioned earlier. This was a good indication of the fact that
116 analysis of the data is not fixed in a particular stage or step, but occurs from even before the fieldwork commences and persists long after the data is analysed. Thus, analysis is an on-going process and often also happens parallel to the process of acquiring the data in the first place (Thorne, 2000, p. 68).
Since the analysis was guided by the CCM, each interview was compared to all the others, as well as with the data from the examination of the documentation and archival data. Similarly, organisational documentation were also analysed using the CCM.
Generally in the study of social phenomena, the researcher allows the codes to emerge through the organizing and continual reading of the data (Creswell, 2009, p. 187), instead of conceptualising codes before these activities are performed. This was the case with this study, as the researcher was guided by her readings of the data to formulate codes that were closely linked to the raw data, instead of enforcing codes onto the data set prematurely. Such codes were recorded on the transcribed interview manuscripts, often in the margins or to the side. In the case of the documentation analyses, codes were recorded in the margins of the documentation and sometimes in a separate journal.
The CCM is not a uniquely novel method for data analysis in itself, but is rather at the heart of many other data analysis strategies such as reading and re-reading strategies, memo writing, coding, data matrices, diagram construction, etc. (Boeije, 2002, p. 391) because they all rely on the constant comparisons to make sense of the raw data and to be able to identify common themes across the data set. For this reason, it is an effective tool for analysing all kinds of qualitative data and in assisting in the generation of theory relating to the study findings. In this study, the CCM was effective in identifying themes within the interview data set, as well as the data from the organisational documentation. Further, it provided an efficient framework for the comparison of codes and themes across all these data sets, allowing findings to be emerge that were closely related to the raw data.
Coding was stopped when the researcher felt that the “saturation point” was reached. This implied that the researcher could no longer identify any new codes and the codes generated were adequate in covering and representing all the raw data (Glaser, 1965, p. 441). This was not a once off task because the researcher first coded the raw data, and then revised her codes as she proceeded to get deeper into the findings. She did this again upon examining the lists of codes she had generated in order to ensure that that there were no repetition of code words and/or
117 phrases, or unnecessary coding. This made the process of constructing themes from such coding a more efficient process.
3.5.4 Step 4: Construction of themes and descriptions
Once the data is coded and reviewed, similar codes form the basis for the generation of themes, or groupings of similar kinds of information. In this study, once the data was coded using particular keywords and/or phrases, lists and tables were drawn up encapsulating all the codes so that the researcher could see at a single glance, the commonalities between them, as well as any disconfirming data that was different from the commonalities. These commonalities then allowed the researcher to identify particular themes emerging from the raw data. These themes in turn became the core focus of the study, and thus of the Findings and Discussion chapters to follow.
Coming back to the qualitative diagramming method utilised in the SD approach (which will be explained in the SD chapter to follow), these themes allowed the researcher to begin to identify the feedbacks inherent in the problem issue, i.e. the feedback relationships influencing Oxfam GB’s overall sustainability. Thus, the researcher used the emerging themes from the interviews and documentation to construct a Qualitative SD Model constituted of graphical descriptions of the feedback relationships found to be operative in the sustainability of the organisation.
3.5.5 Step 5: Interrelating themes and descriptions in the qualitative narrative
In addition to the construction of the CLDs to depict the nature of feedback relationships pertaining to Oxfam GB’s sustainability, links between the themes emerging from the interviews were also made explicit by the researcher to depict the interconnected nature of all the interview data. Links between the data emerging from the various data sources (i.e. the interviews and the documentation) were also made to ensure that all the data was viewed holistically instead of analysing the data from the various data sources in isolation from one another. This was regarded as important by the researcher in exemplifying the holism approach to the study discussed earlier, as well as due to the fact that “sophisticated qualitative studies go beyond description and theme identification and into complex theme connections” (Creswell, 2009, p.
189). The identification of these connections facilitated a deeper interrogation of the meaning of the themes and descriptions emerging from the study.
118 3.5.6 Step 6: Interpreting the meaning of themes and descriptions
Once themes and descriptions were identified, the next step involved the researcher’s interpretation of the meaning of such themes and descriptions. This process of sense-making of the analysed data formed the essence of the research. The researcher also always kept in mind the overall research issue at the core of the study, namely: the issue of investigating the NPO’s quest for sustainability in a holistic fashion. The lessons learnt from the analysed data is often shaped by the researcher’s personal interpretations as influenced by their worldviews, their history, culture, experiences, perceptions, etc. (Creswell, 2009, p. 189), thus such learning is not the product of the data analysis stage alone. Therefore, interpretations of the data are always made through the framework or lens which the researcher uses to view social reality, as well as the theoretical lens they choose to utilise as a guiding framework for the investigation, thus influencing the nature of the interpretations that emerge.
Figure 3-2: Analysed data is influenced by theoretical frameworks and the researcher’s personal view of reality