CHAPTER 3: RESEARCH METHODOLOGY
3.10 Data analysis
Cousin (2009, p. 31) argued that “qualitative data analysis explores themes, patterns, stories, narrative structure and language within research texts (interview transcripts, field notes, documents, visual data, etc.) in order to interpret meanings and to generate rich depictions of research settings”. The author stresses that data must be well handled and focused during gathering and analysis to produce theoretical findings.
Creswell and Poth (2018) underscored the fact that qualitative data analysis goes beyond scrutinizing text and image data. The authors contend that data analysis is a helical process that involves the systematic organization of the data, meticulous reading of the data, coding, identifying categories, developing themes, representing and interpreting the data accordingly.
It is significant to note that qualitative data analysis, including interpretation, is not a linear process but an iterative (back and forth) or circular process (Hesse-Biber &
Leavy, 2011; Lichtman, 2010). Figure 10 and Figure 11 are diagrammatic representations of data analysis processes, as iterative and spiral, and alluded to by Creswell (2014, p. 261) and Creswell and Poth (2018, p. 186) respectively.
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Figure 10: Qualitative Process of Data Analysis Creswell (2014, p. 261)
According to Figure 10, the researcher collects qualitative data using different strategies. This evidence-based information is transcribed and read through several times to make meaning out of it before it is assigned codes in line with the phenomenon under study and the research questions to be answered by the study. “A code is a qualitative inquiry is most often a word or short phrase that symbolically assigns a summative, salient, essence-capturing, and /or evocative attribute for a portion of language-based or visual data” (Saldaña, 2016, p. 4). The short phrases of codes as explained by the author are categorized and organized into themes in order to answer the critical questions of the study. In addition, Figure 10 also indicates that the process is not one way or linear because the researcher must go back and check that the codes and categories conform to the raw data generated from the various sources. Simultaneously, the themes should synchronise with the codes and articulate with the literature and theoretical framework of the study.
Therefore, Creswell (2014, p. 262) concurred that the “qualitative process of data collection is an inductive process, going from the particular or the detailed data (e.g.
transcriptions or typed notes from interviews) to the general codes and themes”.
Principally, qualitative research is an inductive process that ensures that data are
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organized into groupings to determine the patterns and connections among them to elucidate a new phenomenon or expatiate on an already existing occurrence (Court, Abbas, Riecken, Seymour, & Le Tran, 2018; McMillan & Schumacher, 2014). In this study, the data from the interviews were transcribed verbatim in order to capture the exact voices and body expressions of the participants before coding was done. I immersed myself in the understanding of the data transcribed, data from the reflective journals and portfolios of evidence, and to have a good sense of the responses in line with the answering of the research questions before coding and establishing the patterns and relationships among the data.
The following Figure 11 explains the spiral process of data analysis:
Figure 11: The Data Analysis Spiral (Creswell & Poth, 2018)
Similarly, Figure 11 depicts the data analysis spiral process. Figure 11 reveals that data analysis must begin with the collection of the data. Since the data could be voluminous both text and images, data needed to be managed and organized in such a way as to extrapolate the main ideas that will form the codes and themes as the process of data analysis unfolds along the spiral. At the emergence of the theme, the findings must be accounted for and they must be in congruence with the raw data collected. This process according to Creswell and Poth (2018) is instinctive, sensitive,
Data collection
Account of findings
Describing and classifying codes into themes Managing and organising the data
Representing and visualizing the data Developing and assessing interpretations
Reading and memorising emerging ideas
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with the belief that there is no absolute truth and participants have a divergence of opinions.
At the initial stage of the analysis, I used computer software called Nvivo to code the data from the focus group interviews and individual interviews after transcribing the audio information. Later, I switched to the manual process to complete the coding before I generated the categories and the themes to apply my mind to the categorization procedure. Simultaneously, the coding of the reflective journals and portfolio of evidence were manually coded while the categories and themes materialized. More so, Patton (2015, p. 531) argued the use of computer software may not be as productive as researchers may think because the “real analytical work takes place in your head”. I agree with this notion because the data analysis is qualitative analysis and is iterative where you go back and forth in your thoughts as you make sense out of the myriads of data at your disposal.
Moreover, content analysis was used to analyse the data generated from this study.
Kumar (2014, p. 318) defined content analysis as the process of “analysing the contents of interviews or observational field notes in order to identify the main themes that emerge from the responses given by your respondents or the observation notes made by you”. Bauer (2000) contended that content analysis can be used for analysing any written information while Flick (2014) argued that a theoretical framework is usually applied when categorizing textual data to give more meaning and ultimately summarizing the volume of data generated by the researcher. The content analysis encompasses the methodological synopsizing of data that are written to extrapolate the main ideas and important information from the sets of written evidence available to the researcher (Cohen et al., 2011; Schreier, 2014). According to Cohen et al.
(2011, p. 563), data generated via content analysis used for content analysis are “in permanent form (texts) verification through re-analysis and replication is possible”.
As a result of the foregoing arguments by the scholars, I employed the use of content analysis to reduce the volume of data derived from focused interviews, individual interviews, reflective journals and the portfolios of evidence. I systematically scrutinized the main ideas, in line with the theory and literature related to the phenomenon, coded, categorized the codes and generated themes required to provide answers to the research questions of the study.
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