Contribución al ejercicio de los derechos culturales

Qualitative analysis is defined as: the “non-numerical assessment of observations made through various qualitative research techniques” (Babbie, 2010:394). It is the process of resolving data into its constituent components to reveal its characteristic elements and structure with the aim of describing the objects or events to which the data refers” (Dey, 2005:31). Thus it depends largely on the purposes of the research and should be integrated from the start with other parts of the research, rather than being done as an afterthought. For Glesne (2006), qualitative data analysis is essentially a process of categorising, synthesising, searching for patterns and interpreting data. Since its data is full and rich, it is often difficult to find analytic paths through it, thus researchers must avoid being captivated by the richness of the data (Bryman, 2008:538).

Lieblich, Tuval-Mashiach & Zilber (1998:11) have a different opinion. They believe that narrative research does not require the replicability of results as a criterion for its evaluation. Instead the readers of the report need to rely more on the personal wisdom, skills and integrity of the researcher. However, data analysis in narrative research does not mean absolute freedom for speculation and intuition; rather, intuitive processes should be in the service of comprehension and should be tested against the narrative material. In other words the researcher must not make ‘wild’ interpretive decisions but should rather be justified by systematic inferential processes.

Given that the main research focus was on examining how narratives of experiences and meanings attached to being an academic relate to the construction of academic identities, when analysing the data I had to employ ‘dialogical listening’: (a) I had to listen carefully to the voice of the narrator as represented in the text; (b) I had to listen to the voice of the theoretical framework which provides the concepts and tools for interpretation and (c) I had to listen to my own voice in the act of reading and interpreting (Lieblich et al., 1998:10). I also paid special attention to both the form and content of their narratives as they were two important and main dimensions in the narrative analysis, (1) the holistic versus categorical approach and (2) the content versus form approach (Lieblich et al., 1998:12).

Firstly, the categorical approach may be adopted when the researcher is interested in a shared or collective problem; it analyses the story by dissecting it, and collecting sections or single words belonging to defined categories from the entire story or from several texts belonging to a number of narrators. In contrast, the holistic approach is preferred when the person as a whole is being studied since it focuses on a person’s life story taken as a whole and interprets sections of the text in the context of other parts of the narrative (Lieblich et al., 1998:12). The second dimension, the distinction between the content and form of a story, focuses on the traditional dichotomy used in the literary reading of a text. In the content approach of a narrative, the focus is on the meanings, motives or images that the story symbolises from the tellers’ perspective.

Conversely, the form approach focuses on the structure of the plot, the style, the choice of metaphors, the sequencing of events, complexity and coherence (Lieblich et al., 2012:12-13). Therefore this study adopted the categorical-content narrative analysis approach (known as the content analysis) since I am primarily interested in how meanings attached to distinctive and interpersonal experiences of work and its setting relate to the narratives of the construction of academic self-concepts at NUL; this is a phenomenon shared by academics as a group.

4.5.1 Data preparation and familiarisation

The first step of analysis in a qualitative study is data preparation and familiarisation (Tesch, 2013; Elliot & Timulak, 2005). In this case, I conducted a few interviews at a time and transcribed them as soon as they were completed because this allowed me to reflect on the quality of data, to refine the questions and to pursue emerging avenues of inquiry in further depth in subsequent interviews (Pope et al., 2000). In the ‘initial’ or primary analysis of the data and since the interviews were audio recorded, I listened to the conversations and transcribed everything that the participants said verbatim within two days of completion of each interview. Since the interviews were in English; this was appropriate and part of the

lingua franca of the university. I also ensured that I typed all the literal statements and noted the non-

When I was satisfied that I had reached data saturation since no new information transpired, I began to re-read the entire data set transcripts, paying closer attention to what the participants said (Tesch, 2013). I listened to and read each interview transcript several times for the non-verbal and para-linguistic levels of communication such as intonations, emphases and pauses for it was assumed that they might provide a context for the emergence of specific units of meaning and themes later on. I also read through each interview transcript to get a feel for what the participants had really said. When doing this, I approached the data with openness in order to allow for the interviewee’s ‘sense of the whole’ to emerge from the data (Tesch, 2013). I also went through the data to get the participants’ opinions in order to understand the meaning of what each respondent was saying, rather than what I expected the respondent to say.

4.5.2 Locating meaningful units

Next I started dividing the data into distinctive meaning units; in this case, these were parts of the data that communicated sufficient information to provide a piece of meaning to the reader (Elliot & Timulak, 2005:153). When analysing data using content analysis, meaningful units are in the form of utterances of the text and these units must be extracted, classified and gathered into categories or groups (Lieblich et al., 1998:13). This process is termed ‘secondary’ data analysis, whereby the researcher searches the interview transcripts to locate meaningful units (small bits of text) which, in this study, conveyed meaning independently (Babbie, 2010).

I immersed myself in the data, read, re-read and dwelt on it so that I could be close to it and have a sense of the whole interview. When I was satisfied that the text was familiar to me, I painstakingly delineated all the meaning units throughout the entire interview transcription and then decided which ones were relevant to the research questions. Meaning units according to Wertz (1985 cited in Tesch, 2013:93) are part of the “description whose phrases require each other to stand as a distinguishable moment”.

I then broke down each transcript into small sections, describing what was said in each section. To ensure that my analysis was data driven, I identified key themes and issues in the text using two activities: (1) Writing descriptive summaries of what the participant said, what issues were identified, what events were relayed, what feelings were expressed and (2) Making initial interpretations - about what those issues, events and feelings might mean, that is, how they help me understand the experiences and perceptions of job satisfaction and motivation amongst academic staff at NUL.

4.5.3 Generation of categories

After locating meaningful units from the data I realised that different sets of meaning units describe different aspects of academic identities. I then restated the content or ‘theme’ of each meaning unit by summarising it into a more professional language. According to Tesch (2013), if any of the meaning units from one interview show similarities, they should be clustered together. To ensure that I got meaningful units, I went back and forth between the data and isolated the themes, dialoguing with the text in order to achieve the most revelatory wording of a theme. Next I ensured that I got general descriptions of the data by identifying the fundamental structure of the phenomenon, which I then used to describe the constituents of which the particular issue I was interested in comprised (Tesch, 2013).

I then categorised (coded) the meanings in the meaning units; that is, I interpreted the data and used labels close to the original language of the participants (Elliot & Timulak, 2005:154). I then integrated the identified sections that had a similar focus or content and then identified what appeared to be important and assigned that piece of information a name or a code. After I had coded all the data under these domains, I then arranged major code clumps into logical order by asking myself which clumps or parts belonged together in the final code arrangement in the manuscript (Glesne, 2006).

When coding the data, I started with open coding, whereby I labelled words or phrases that describe the research questions under investigation. I then divided the data into segments and scrutinised them for commonalities that could reflect categories or themes. Once the data was categorised, I examined it for properties that characterised each category and grouped similar comments together to form categories. I made connections between categories and selected the main categories of data, and then systematically related them to the other categories (Kleiman, 2004). I also ensured that the coding system that I had developed and used was comprehensive, accurate and useful enough to help me in describing and understanding the various descriptions of behaviour, statements, feelings, and thoughts of academic staff towards their understanding of work motivation and satisfaction (Pope et al., 2000).

The coding of data facilitated what Dey (2005) terms a ‘thick’ description of data which essentially includes information about the context of the phenomenon, how people understand it and its subsequent evolution into guiding their behaviour. Lastly, I developed themes. Themes are defined as abstract constructs that link expressions that are found in texts. When developing the themes, I ensured that they reflected either the overall experience, the structure of the experience, the function, the form or the mode or recurrence (Ryan & Bernard, 2003). Elliot and Timulak (2005:153-154) also add that sorting data into domains or very broad headings for organising the data provides a conceptual framework for the data or axial coding. In this case, four main domains emerged; (1) good and bad work experiences; (2) community culture and participation; (3) job attitudes and (4) loyalty to NUL.

4.5.4 Demographic characteristics of NUL academics

Age, marital status, academic rank and the highest level of education were deemed important for the presentation of NUL academics’ demographic characteristics (see Appendix C: Demographic Characteristics). At the time of the interviews, the ages of the academics ranged from twenty-eight to seventy-one years and the duration of employment from two years to forty-six years although one participant had been a part-time lecturer since 2012. The research participants were selected from the seven faculties of NUL but for privacy and anonymity purposes I decided not to indicate either the faculties or departments in the analysis. Of the thirty-one participants, twenty-five were locals, two were from Cameroon and two came from Zimbabwe, while one came from Ethiopia and another one from Kenya.

The demographic analysis also revealed that twenty-four participants were married, two were divorced while five were single. Almost all the participants had children with only two reporting that they had none while one was expecting a first child. Regarding the highest level of education, twenty-one participants had a Master’s degree while ten had PhDs. In order to contextualise the experiences of being an academic at NUL, it was important that all academics be full-time teaching staff at NUL regardless of academic rank, although they could hold university service positions such as head of department, dean or tutor. I assumed that this mix of stories would let me get a sense of what being a junior academic versus a senior one was like; or what being a young, less experienced member of staff versus older and more experienced staff was like.

My initial interviews were hard to conduct as I was feeling apprehensive; participants often asked me to clarify what a particular question meant. For example, the question ‘can you tell me about yourself?’ was often met with puzzlement; with most participants considering it too broad. I soon learned to narrow it down into issues that I expected to hear from them and, with practice, I realised that the interviews were more like conversations. I realised that this also helped me to get the information I really wanted. During the interviews, I always began by asking participants to tell me about their family, education, and where they came from. However, for subsequent questions, there were times when I would begin asking the first question on my schedule, while at other times I mixed it up a bit. I found that this technique helped the participants who were a bit shy to come alive and talk more freely about their work. For example I found that the questions; ‘What do you like/dislike most about your job?’ and ‘Have you ever thought of resigning from NUL?’ were good for getting the conversation going.

Nevertheless one challenge was steering the conversation back to the interview. My experience was that once participants started talking about themselves and their work experiences they would invariably mix their responses with issues that were not related to the study especially those having to do with

politics, friends, colleagues, family and other issues. These issues were especially evident when analysing initial interviews; I realised that participants had answered very briefly what I had asked them, yet they had talked at length. For the subsequent interviews I ensured that I reined in some of the conversations in order to steer them in the direction that I was interested in. Ultimately, the stories that came later were of better quality and displayed the issues more appropriately.