Analysis of data is a process of inspecting, cleaning, transforming, and modelling data with the goal of discovering useful information, suggesting conclusions, and supporting decision-making (Bertram & Christiansen, 2014).
Qualitative data analysis involves arranging and explaining the data generated (Cohen
et al., 2011). There are varieties of approaches to adopt when presenting and analysing
qualitative data (Clandinin & Connelly, 2000). The implication of this is that the choice of adopting a data analysis procedure depends on the purpose and design of the research. Choosing a suitable data analysis procedure can be daunting for the researcher; however, it enables the researcher to reduce dense information generated to report the significance of the participants experience (Stinger, 2007). There is no wrong or right way to qualitative data analysis as long as the researcher abides by the rules of fit for purpose, which means, the data generated must be given a suitable type of analysis (Cohen et al., 2007). Furthermore, Cohen and others suggested that the implication of deciding the purpose is that it will define the type of analysis done on the data and impact on the pattern of writing the analysis.
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The following diagram illustrates that qualitative data analysis is not a linear process. It is according to Seidel (1998, p. 2), “iterative and progressive”. On this basis, data generated in this research was read and re-read to identify indicators that speak to the pre-decided themes for data analysis.
Figure 3. 1: Diagram showing steps of data analysis (Creswell, 2012, p. 237)
As mentioned earlier, this research made use of document analysis for its data generation and the data generated from the documents were analysed through content analysis. For document analysis, there are two kinds of analytical strategies for the analysis of the textual resources. These two analytical strategies are: content analytic strategy and context analytic strategy (Atkinson, Delamont, & Coffey, 2004). In view of this, the research made use of content analytical strategy for its textual resource analysis.
The term content analysis indicates a range of qualitative analytic strategies to “identify core consistencies and meanings” in a volume of qualitative data (Patton, 2002, p. 453). Content analysis can be used to describe a range of analysis techniques such as intuitive, interpretive or systematic textual analysis and can be conducted with any written material such as reports and diaries (Cohen & Manion, 2011). This research generated and interpreted its data from the SL project reports and reflection diaries written during the project. Qualitative content analysis aims to go beyond counting words, to examine
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the language for meaning. Curtis (2011) claims that this type of analysis which is prominent in qualitative research is used to reveal the presence of and to categorise explicitly the concepts within the research. It is a research method that identifies and examines certain words, phrases, or expressions that point to a semantic foundation, and inscribes certain concepts and their relationships in a particular research content/text (Russell & Gregory, 2009).
Qualitative content analysis is one of the numerous research methods used to analyse text data. It is regarded as a flexible way of analysing data (Cavanagh, 1997). It is also probably the most prevalent approach to the qualitative analysis of documents and searching out of themes in the materials being analysed (Bryman, 2004). In more specific terms, Bryman (2004) defines qualitative content analysis as “an approach to document that emphasises the role of the investigator in the construction of the meanings of and in texts” (Bryman, 2004, p. 542).
Research using qualitative content analysis focuses on the characteristics of language as communication with attention to the content or contextual meaning of the text (Budd, Thorp, & Donohew, 1967; Lindkvist, 1981; McTavish & Pirro, 1990; Tesch, 1990). Similarly, in this research, the selection of documents for content analysis is generally based on purposive sampling strategy. Qualitative content analysis goes beyond merely counting words to examine language intensely for classifying large amounts of text into an efficient number of categories that represent similar meanings (Weber, 1990). In this research, after the first stage of collecting large quantities of documents for data and purposively selecting the sample, the next stage was deciding how the data will be analysed and presented for the results. The daunting task of data analysis was to first make sense of the unstructured data of twenty documents which consisted of 14 project reports, 14 reflection diaries and one module outline. To analyse the data from this research, the data generating documents were read carefully, line by line, and categories were developed with meaningful words, phrases, and sentences. These categories can represent either explicit communication or inferred communication. The coding process for the categories was hugely rooted on the pre-service teachers’ views of what was considered important; hence, repeated in several sections of either the project reports or reflection diaries. The coding process was also guided by the reviewed literatures on
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the concepts of SL. The data generated from this research was deductively analysed using the following themes pre-decided by the conceptual framework:
(i) Structure (ii) Reciprocity (iii) Relationship (iv) Reflection
As well as another theme that emerged from the data during analysis: (v) Sustainability
The data generated were interpreted and made sense of which includes noting regularities in the participants’ writings to categorise them according to the above listed themes, while considering possible new recurring themes (Cohen et al., 2007). Critical engagement with the data was necessary in order to establish links within the data. During this period of analysis, credible explanations on the SL projects were identified and answers to two research questions emerged.
Since this research was done using the qualitative approach, data was presented descriptively using expressions from the pre-service teachers’ point of view. This corresponds with Orb, Eisenhauer, and Wynaden (2001) position that a qualitative case study should describe a phenomenon from the participants’ point of view. Cohen et al. (2007) opine that the most suitable way of writing a case study might be as a descriptive narrative. It is also necessary to maintain the participants’ accounts in the analysis text so that readers can connect between the findings and the data from which they were retrieved (Jones, 2002). This research also used verbatim description in its narrative. This means that the pre-service teachers were quoted word for word in the analytical narrative. One of the advantages direct quoting is that the flavour of the original data is not lost but maintained during the report. Another advantage to direct quoting is that it portrays the researcher’s faithfulness in using exact words from the participants and thus meets the ethical obligations of the researcher to describe others experience in the most truthful way possible (Mills, Bonner, & Francis, 2006).