CONTEXTUALISATION AND METHODOLOGY
4.7 Data analysis and presentation
Cohen et al. (2011) describe data analysis as “making sense of data in terms of the participants’
understanding of the phenomenon, noting patterns, themes, categories and regularities”. Yin (2011) observed that many steps come into play in data analysis to ensure that the purpose of the research is addressed. The exploratory nature of the critical questions for the study supports an inductive approach to data analysis. Therefore, the use of activity theory as an interpretative lens has imposed a sociocultural perspective onto the data. Data analysis in qualitative research consists of preparing and organising the data for analysis, reducing the data into themes through the process of coding and condensing the codes, and representing and forming an interpretation (Creswell &
Poth, 2018). Contrary to the previous authors, Simons (2009) noted that in terms of detailed analysis “there are no set rules or procedures to follow” since approaches in data analysis are diverse.
Despite differences in the number and names of steps proposed, Ho (2015) aver that most researchers are usually in agreement with the following four steps: transcribing, coding, categorising and identifying emergent themes. In this study, the researcher analysed the data gathered by immersing himself in the data by listening to and watching audio and video recordings, as well as reading and re-reading transcripts to consider the understanding and practices of FA of six different subjects (teacher educators) as a unit of activity, by asking questions of the data about how MTEs understand FA, what FA tools were used in practice by educators, and what rules influence how those tools were used. Data obtained from interviews, observations, field notes, and documents were analysed following three basic steps: organising and familiarising with data, coding and categorising, interpreting and producing the report. The steps for the data analysis were not linear but interconnected, forming a spiral of activities concerning the analysis and representation of the data (Creswell & Poth, 2018). The steps are described in the following sections.
106 4.7.1 Organising and familiarising with data
Data generated from the study, such as audio and video recordings, field notes, documents (teacher educators’ course outline and students' assessment scripts) were first organised college by college.
The researcher then immersed himself in the data by listening to and watching audio and video recordings, as well as reading and re-reading field notes for a general understanding of each case.
This was followed by transcribing verbatim the interview and observation data from the oral to written form as notes which were typed out. Transcription is "the process of converting recorded materials into the text" (King, Horrocks, & Brooks, 2018, p. 193). According to King et al. (2018), before data transcribing qualitative researchers should make key decisions based on the methodological position of the study, availability of resources in terms of finance and time and potential threats to the quality of the transcripts. The authors reiterate that the decision includes who would transcribe and what guidance or training do they need to transcribe? In this study, data were transcribed by the researcher, as the process helped him to “become closely familiar with the data” (King et al., 2018, p. 193). Also, Widodo (2014) remarked that a researcher has the opportunity to carefully listen, pay attention and think deeply when recorded data are transcribed by the researcher himself. After completion of data transcription for the six participants, the researcher once again read the whole transcripts several times to become thoroughly familiar with the data. This was accompanied by a detailed textual analysis, starting with writing down initial ideas and comments on the transcript while reading through the transcribed data.
4.7.2 Coding and categorising
Coding was used to assist in analysing the data obtained from the interviews, field notes and observations. Coding refers to labelling and systematising of data (Tracy, 2019). This author also noted that coding is an active process of identifying data as belonging to or representing a phenomenon (Tracy, 2019). The researcher looked carefully for words, phrases, and sentences to define concepts. Teacher educators' documents and transcripts were organised by using pseudonyms to replace the participants' names and their colleges (see Table 4.4) to ensure confidentiality. Students were also known and identified by codes; for example, a student from Roberkeyta College of Education is assigned and named as PSTR1. Throughout the process of coding the researcher constantly compared the data applicable to each code, and based on that codes were modified in some situations to fit the new data. The coding process leads to thematic
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analysis, which is "a method for systematically identifying, organizing and offering insights into patterns of meaning across a data set" (Clarke & Braun, 2013, p.2) This approach allows the researcher to identify common and diverging issues about the phenomenon being explored from the perspective of the participants, to make sense of the commonalities and differences through codes. Guest, MacQueen, and Namey (2011) remarked that in thematic analysis codes are developed to represent identified themes. Therefore, data generated from the six MTEs were gathered, organised and analysed using Merriam (2009) description of “ category construction”.
The researcher began the analysis by first identifying and coding segments of the data that are responsive to the critical questions. Codes that seem to align were then sorted and grouped together, forming categories. An example of a flowchart showing the process of coding into categories is presented in Figure 4.2
Figure 4.2: Flowchart showing codes inductively developed into categories.
FA is an activity system and a framework emerging from this study was used for organising the coding system. In addition to mapping the data generated onto the framework, the concept of contradiction was brought to bear on the data. Informed by early studies (Engeström & Sannino, 2011; Karanasios et al., 2017; Kuutti, 1996) on contradictions, data were considered in relation to tensions between various contextual factors and how these tensions affect teacher educators’
Codes Category
Reporting to students
Teacher educators’ understanding and operationalisation of feedback
• Providing information to students
• Telling students what they have done
• To see If there is something wrong
Nature and quality of feedback
Instructional corrective tool
• Using different strategy
• Devise means to help
• Information about your method
Category
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implementation of FA in the mathematics classroom. Karanasios et al. (2017) argue that contradictions and tensions “provide a lens for understanding how deviance from established rules and norms occur”, since individuals tend to move away from an established norm when tensions are increased and occur within an activity system.
Table 4.4: Pseudonyms and coding used for participants and their colleges
Name of college Code for college Name of participants Codes for participants
Roberkeyta college RCOE Sekyi S
Roberkeyta college RCOE Emily E
Oswald college OsCOE Wilson W
Oswald college OsCOE Fordjour F
PhilNeri college PnCOE Peprah P
PhilNeri college PnCOE Anani A
4.7.3 Interpreting and producing the report
MTEs’ understanding, and practices of FA were described and interpreted through the lens of sociocultural theories. When all data had been categorised, analysis of the data regarding the research question began. The researcher summarises and presents the data generated from all six participants in detail in Chapters 5, 6 and 7 of this report. Data were not presented using a case- by- case approach, since the themes that emerged from teacher educators were similar and, in some situations, repetitive. According to Braun and Clarke (2006) an analysis should embrace a concise, coherent, and non-repetitive account of the data represented through the specific themes. The researcher used vivid examples or extracts from the data to capture the essence of the themes.
Therefore, integral findings on data were presented to answer the research questions rather than present individual cases (Yin, 2011).