RESEARCH DESIGN AND METHODS
4.5 Data presentation and analysis
I used both qualitative and quantitative data presentation and analysis procedures in line with my data collection instruments above.
4.5.1 Interview data
The first process was to identify the audio-taped interviews by the pseudonyms of my interviewees in Appendix E. I downloaded the files from the digital recorder into the computer. After that, I played each interview as I transcribed it in long hand. I
typed all the twelve interviews and proof read them. I corrected all the errors to the best of my ability. I re-played each of them several times on my desk-top computer.
Phenomenological analysis of transcribed data is initially more open, tentative and intuitive. Organization and analysis of data begin with horizontalization or regarding every statement relevant to the topic as having equal value (Moustakas 1994). The meaning units are listed and clustered into common themes. It focuses on meaning units of analysis. Those are the smallest segments of text that are meaningful by themselves, to describe themes and patterns in the data (Leedy 1997). I transcribed verbatim data from the audio tape record of each interview.
The first part of the analysis involved coding concepts in the data (Kazmer 2000). I used the NVivo qualitative data analysis programme. In NVivo programme each interviewee is regarded as a case. Therefore, I created a casebook (Appendix E) of the twelve interviewees and entered all their personal and professional characteristics. On the basis of shared attributes like gender, age, marital status, geographical location, educational qualifications, previous DL experience, duration between past studies and B.Ed., enrolment intake into the B.Ed. programme, reasons for studying through DL and work positions I created what, in NVivo, we call sets (Richards 2005). They were 22 sets. I also used the sets as units of data analysis.
The twelve audio and twelve written interview transcripts constituted my internal sources of data. From the transcripts, I drew up about 60 free/child nodes on the basis of sub-themes. In NVivo, free or child nodes are the smallest containers for the ideas and for the coding that gathers data about an idea (Richards 2005) through which the structures and strategies used by adults in coping with DL are identified.
Clusters of such containers into themes form the tree or mother nodes.
I then condensed the sixty free nodes into 10 tree/mother nodes on the basis of the themes that came out of the data from interviews. Those themes are what I refer to as the data trees (Appendix F) under which I cite excerpts of interviews in my data presentation, analysis and discussion chapters
In qualitative research, data collection and analysis are interwoven. Data is collected, analysed and interpreted in view of the respondents‘ feelings, attitudes and beliefs about the subject(Maxwell 2005). The twelve phenomenological interviewees constituted my primary units of analysis (Groenewald 2004). I constantly compared interview to interview data at segment level within and across tree nodes and patterns. These theoretical comparisons are tools for looking at properties objectively rather than naming or classifying them without a thorough examination at the property and dimensional levels(Patton 2002). Constant comparison continued until saturation, that is, when there was no longer need to add information to themes and patterns or their properties. The intention was to understand the participants‘
perspectives on their experience with the use of ZOU structures and their own coping strategies in DL (Ertner 1996).
Interviews engage the researcher and participant in a mutual partnership as the earlier carefully listens to hear meanings, interpretations and understandings of the latter while the latter is allowed to elaborate, illustrate and clarify events the best way possible. Descriptions of experience are the central focus in phenomenological interviews (Leedy 1997). Data analysis involved synthesizing information from respondents‘ descriptions of their experiences in order to identify themes and patterns that emerged (Landbeck and Mugler 2000).
Unlike in either ethnography or grounded theory which use observation marks, in phenomenological research the use of spoken or written text as data marks the ideological predisposition of the method. Phenomenological research attempts to
minimize a priori preconceptions about the nature of the data through bracketing and reduction. Bracketing out the researcher's entering predispositions in meaning and interpretation towards the question is recognition of the unavoidable a priori dimensions of the method‘s attempt to allow the data to speak for themselves as much as possible. Phenomenological reduction used here needs not be misconstrued for the reductionist natural science methodology in which analysis subjects data to cause and effect relationship. Instead, it is a deliberate and purposeful opening ―to pure subjectivity‖ by the researcher to the phenomenon ―in its own right with its own meaning‖ (Osborne1994; Moustakas 1994; Groenewald 2004).
I had to get to the heart of my subject matter through delineating units of meaning and subsequently clustering those units into themes concealed in the participants‘
experiences of everyday life in DL. Delineating of units of meaning from data was done by extracting those statements that illuminated the structures and strategies used by adult learners to cope with DL (Groenewald 2004). Such statements were coded into free nodes. I developed clusters of free nodes that contributed towards common themes from different participants‘ descriptions of their experiences in DL and they became my tree nodes (Richards 2006). Rigorous examination and discussion of the nodes are done under data analysis and discussion chapters to elicit their essence in the holistic context (Groenewald 2004) of the support structures and strategies used by adult learners in coping with DL.
4.5.2 Questionnaire data
The questionnaire survey data was analysed using statistical package for social sciences (SPSS)(Miller, Acton et al. 2002). I produced frequency tables (Appendix G) out of the descriptive analysis of the data. I also analysed and presented survey results in cross-tabulations between variables. I used Chi-square and t-tests to determine the significance levels of my results (Scott and Usher 1999; Gorard 2001).