CHAPTER 4 DATA ANALYSIS AND INTERPRETATION
4.2 Analysis PROCESS OF QUALITATIVE DATA
Flick (2013:13) defines qualitative data analysis as a classification or interpretation of linguistic (or visual) material to make statements about implicit or explicit dimensions or structures of meaning-making of a phenomenon and its representation. The aim of data analysis is to describe a phenomenon in detail. Qualitative data analysis is arguably the most important step in the qualitative research process, since it assists researchers in making sense of the collected data.
Data analysis is about converting raw data into information that is meaningful. Data is analysed through searching, evaluation, reorganising, coding, mapping, exploring and describing patterns, trends, themes, subthemes and contradictions to get a bigger picture of what the data means for a specific purpose (Noble & Smith, 2015:18).
4.2.1 Data analysis of the semi-structured interviews
The following steps were used to analyse the audio-recorded data from the semi-structured interviews (Leedy & Ormrod, 2010:153):
• Step 1: Data transcribing
The responses of 15 participants collected through interviews were prepared and transcribed verbatim from speech into words.
A qualified and independent person ensured that the interviews were transcribed correctly in order to enhance the trustworthiness of this study. Participants were also given the opportunity to review the transcribed data to verify whether the transcripts were a true and accurate version of what they had said.
• Step 2: Initial exploration of data
First, the researcher read the transcribed responses of interviews meticulously to gain a deep understanding of the data and identify patterns and differences in themes in what participants said. The participants’ data was then categorised to identify the themes and sub-themes and take notes about the data.
• Step 3: Analysing of the data.
The data was then coded according to categories, themes, and sub-themes.
Content data analysis was used to analyse data in this study, which Wright (2017:2) defines as a technique used to analyse textual data and explain a theme. Data analysis eventually produce categories, themes, and patterns.
A hybrid approach was used as the method of data analysis, which incorporated the inductive and deductive methods of data analysis. A priori themes and sub-themes were identified from the literature on SRL, mainly from Zimmerman’s three-phase model for the development of SRL (2000) and the adapted model for SRL from Zimmerman and Moylan (2009). Additional themes were created from the responses of participants during the interviews.
Participants’ responses pertaining to their awareness and knowledge of the concept SRL; their perceptions about their roles in the development of SRL skills in certain subjects; the training the participants received from the DoE to use and implement SRL skills; the participants’ perception about learners’ roles in taking responsibility of their own learning; and the teaching and learning
environments that develop and enhance SRL were categorised under the predetermined themes and subthemes.
Other predetermined themes and sub-themes included the strategies participants use to develop SRL in the intermediate phase, the challenges teachers encounter in primary school to develop self-regulated learning, the support the participants receive from the SMT and the DoE to develop SRL skills (Saldana, 2009:12).
Since the theoretical and conceptual framework in the study is based on Zimmerman’s (2000) three-phase SRL model, the three phases in Zimmerman’s (2000) and Zimmerman & Moylan’s (2009) models were used as main themes, namely the forethought, volition and self-reflection phases. Participants’ responses were coded under the processes and sub-processes of each phase, for example, task analysis, goal setting, planning, etc. The theoretical framework was used to identify themes and sub-themes, to analyse, categorise and interpret the data collected.
The a priori coding of data enabled the researcher to obtain or retrieve and gather with relative speed the whole text of the participants and other information related to a given thematic idea, which was then sorted, examined and compared (Saldana, 2009:12). After having identified and noted the relevant categories, the collected data was also compared to identify contradictions, similarities, and relationships.
The data findings were interpreted, from which conclusions could be drawn based on the theoretical knowledge of SRL, the empirical data, and the researcher’s current understanding of the Free State DoE’s context.
• Step 4: Representation and display of data.
Thereafter, the data was interpreted in writing and illustrations in tables.
• Step 5: Data validation
Lastly, the data was interpreted and compared with the literature to validate the researcher’s own explanations.
Table 4.1 below shows the themes and sub-themes that emerged from the semi-structured interviews, based on Zimmerman’s three-phase model for the development of SRL (2000) and the adapted model for SRL from Zimmerman and Moylan (2009) among the other literature reviewed in this study.
Table 4-1: Themes and sub-themes generated from semi-structured interviews
Theme Sub-themes
4.1. 1 Theme 1
Knowledge and beliefs of SRL
Familiarity with the concept SRL Comprehension of the concept SRL Value of SRL for academic learning Instruction to utilise and develop SRL
The roles of teachers to develop and enhance SRL
4.1. 2 Theme 2
Knowledge and beliefs of how SRL was developed before task completion in the forethought phase
Subtheme 1:
SRL development in the forethought phase
Developing strategic planning Developing goal-setting
Developing motivational beliefs Developing self-efficacy beliefs Developing learning goal orientation Developing interest in learning tasks
Developing outcome expectations
Subtheme 2:
SRL development while performing tasks in the volitional or
performance phase
Task strategies Time management Group work
Self-observing learning Help-seeking
Environmental structuring Sub-theme:3
SRL development in the self- reflection phase
Opportunities for self-evaluation Reflection
Self-satisfaction
Building realistic attributions for success or failure
Adapting teaching strategies 4.1.3 Theme 3
Challenges with developing SRL
4.1.4 Theme 4:
The support from the SMT
The next paragraphs focus on the discussion and analysis of qualitative data.