Data processing and analysis

This section gives an account of how the collected data were processed or analysed. Flick (2013:3) argues that data analysis is a very important stage in qualitative research because it determines the research outcomes. Qualitative data analysis is said to be the process of classifying and interpreting linguistic material to prove statements regarding “implicit and explicit dimensions and structures of social meaning” (Flick, 2013:5). Creswell (2012:171) advises that in qualitative research, the data analysis process includes making reference to the raw information obtained in the field, organisation and preparation of the data to be analysed, reading through the whole set of data, coding the data as well as identifying and interpreting themes. Therefore, the aim of data analysis is to gain insight into the phenomenon under study through the various responses drawn from the participants. Qualitative data analysis, as described by Henry (2015:25), takes a lot of time and focuses on a detailed analysis of meaning and themes.

Quinlan (2011:420) says that one of the functions of qualitative data analysis is to come up with a profound, detailed and a holistic profile of a phenomenon being studied. The researcher must be able to develop an understanding of the phenomenon under investigation by interpreting and synthesising the collected data. This demands the use of techniques of analysing data that enable the study participants’ voices to be heard (Frost, 2011:145). Quinlan (2011:421) advances that qualitative data is not numerical and can be presented in any way such as images, paintings, photographs or stories. Its analysis is not fixed and includes, for example, reading all data collected (in this case, interview transcripts) and writing down all the major ideas or issues that may emerge from the data, until there no more new ideas emerge. Punch (2012:194) adds that there are diverse viewpoints and approaches to qualitative data analysis, because studying social phenomena, which is the major preoccupation of qualitative research, provides rich knowledge

and is a complex process; addresses different questions; and can be elaborated from different dimensions.

Being an interpretive phenomenological study, data analysis was based on the principles of Heidegger’s phenomenological approach. Smith (2011:22) advances that Interpretive Phenomenological Analysis (IPA) is a research methodology which is qualitative in nature and seeks to gain insight into the subjective occurrences of the participants by means of individual interpretation of their daily experiences and the meanings they attach to them. In this case, the researcher has the task of comprehending the daily encounters of the participants in respect of learner performance as a phenomenon and the meaning they attach to these encounters (Callary, Rathwell & Young, 2015:63).

Reiners (2012:2) advances that in Heidegger’s phenomenological studies, data analysis involves the use of a cyclical approach characterised by continuous reviewing and analysing both parts of the text as well as the text as a whole.

In order to interpret the data collected from the semi-structured and focus-group interviews and the selected documents for analysis, the researcher followed the six steps suggested by Creswell (2012:172-176).

In the first step, the researcher organised all the raw data that were collected in the field: the responses from all the interviewed participants as well as the mission statements, school visions, records of work and learners’ record of performance, and school policies on CPD. This step is also acknowledged by Koshy (2010:112) who argues that there may not be a single approach to analysing qualitative data, but the need to be systematic is key to effectively analysing data, and this begins with organising and outlining the various categories of collected information.

In the second step, the researcher read through all the collected information in order to “obtain a general sense of the information and to reflect on its overall meaning” (Creswell, 2012:172). Koshy (2010:112) also comments that this stage provides a general impression of the contents of

the collected information and it reveals the extent to which it relates to the ultimate goal of the research.

In the third step, the researcher proceeded to coding, which is described by Creswell (2012:173) as organisation of the material into text segments prior to bringing meaning to data. Punch (2012:199) describes coding as a process of assigning codes or putting labels onto pieces of the data for the purpose of attaching meaning to the data segments, and, in turn, serves as a basis for storing and retrieving information, as well as summarising the information into categories, themes and patterns. Attaching labels to the various parts of the collected information helped the researcher to reduce all the participants’ descriptions of their lived experiences of learner performance to specific themes that would lead to understanding of the phenomenon under investigation. This step was necessary to allow the researcher, as recommended in hermeneutic phenomenology, to interpret the identified meanings that relate to the phenomena with a focus on gaining insight into the meaning of the daily encounters and interpretively engaging with data (Sloan & Bowe, 2014:9).

The fourth step focused on generating descriptions of the setting and the people involved in the study. Creswell (2012:175) advises that following the coding process is the generation of the setting or people’s descriptions and groups or topics for analysis. He goes on to say that the identified themes are the key findings in qualitative research and they are usually used as the headings in the findings section of the research.

In the fifth step, the researcher used narrative descriptions to present the outcomes of the analysis.

The sixth step focused on making interpretations or meaning of the information which as described by Creswell (2012:176) involves stating the lessons derived from the study.