CHAPTER 3: RESEARCH DESIGN AND METHODOLOGY
3.7 DATA ANALYSIS
In a mixed methods research study the analysis of data involves both the quantitative and qualitative data sets. While similarities exist in this process, such as data
preparation, data exploration, data analysis, representation, and data validation, in mixed method research the data analysis is based on the design of the study (Creswell & Plano Clark, 2007). As such, with this study making use of a convergent parallel design, a convergent data analysis process has been utilised. Analysing the data in this way emphasises the importance of both phases of this study, as well as both data sets.
3.7.1 Quantitative Data
The quantitative data analysis occurred after the administration of Phase One of this study. Once respondents had completed the online survey in Phase One of this study, the data was then exported into the software program IBM SPSS Statistics (Version 22). This resulted in 400 fully completed, usable responses, a number which would support the reliability of the research analysis (Field, 2013). The quantitative data (Dimensions 1-4) was then analysed using the statistical functions of the SPSS software package.
Descriptive statistics were used to describe the nature of scales and groups, and the distributions across the scales and groups. t-Tests and ANOVAs were employed to determine points of difference within these scales and groups. Factor analysis was carried out to determine the nature of the influences that reflected the respondents’
rationale for their unwillingness or willingness to apply for school leadership positions. These influence factors were then tested for internal reliability using Cronbach’s alpha. Finally, the impact of the respective unwillingness and willingness to apply influence factors, both within and across hierarchical levels, was explored.
3.7.2 Qualitative Data
The qualitative data derived from the survey open-ended responses found in Phase One of this study were analysed using a thematic analysis approach (Byrne, 2017; Guest, MacQueen, & Namey, 2012). Braun and Clarke (2006, p. 79) define thematic analysis as “A method for identifying, analysing and reporting patterns within data”. Using this inductive process, the textual data was first broadly coded and then these codes were refined into a smaller number of categories, and finally, these categories were coalesced into abstract themes.
A grounded theory data analysis approach was utilised for the Phase Two qualitative components in this study following the general principles outlined by Glaser (2005) and Corbin and Strauss (2008). This process involved the critical review of responses to determine appropriate coding, from which tentative conceptual categories were determined. Parallel with this process, memoing was conducted to explore for links between codes and to develop a deeper understanding of theoretical connections between codes and categories. These conceptual categories were then constantly compared with the data and other categories to ensure underlying uniformity. Nested categories, grounded from within the data collected, were then mapped into
substantive themes. These abstract themes were then able to be used to construct a general theory relating to the overarching research question.
In Phase Two of this study, the qualitative data obtained from the semi-structured interviews were recorded on an Olympus WS-831 digital voice recorder and then transcribed by the researcher. The benefits of this process were the ability to ensure the confidentiality of the interviewees, as well as immersing the researcher in the data – a process that assisted in data analysis. During this time of transcription, memoing took place and became an integral part of the analysis process. Having transcribed the interviews into Microsoft Word documents, initial open coding (line by line coding)
commenced which enabled the identification of a number of similarities and patterns in the responses. The software package NVIVO was introduced at this stage, and this enabled the researcher to label, group, create nodes and define similarities and patterns from within this interview data. Processing the data using the NVIVO software package was useful in terms of grouping, storing and efficiently retrieving data, but the researcher felt it created a distance between the data analysis process and the researcher that had not been the case in the initial manual open coding process.
The next step in the data analysis process of converting codes into conceptual categories, and then categories into themes, was processed by the traditional manual approach rather than continuing with computer aided software. These categories and themes were then compared to the data and presented to a number of ASA employees, both individually and in group settings, to test whether they were intuitively
consistent with their experiences of ASA succession practices. To a great extent, this feedback confirmed the initial themes, and connections between these themes, suggesting that at this stage the lines of enquiry had been saturated and the conclusions reached had a degree of validity.
3.8 CONCLUSION
This chapter has described the research methodology and the rationale for why this methodology was selected, and outlined a number of limitations associated with adopting a mixed method research methodology. It has outlined the study worldview and philosophical stance, and presented the research design implemented in order to undertake this study. Guided by the overarching research question “What are the perceptions of the classroom teacher, school-based administrator and system-based administrator hierarchical levels, with regards to Adventist Schools Australia succession practices?”, as well as sub-questions, the purpose of the study was to examine ASA employee perceptions of school leadership succession practices. The data analysis of both Phase One of this study (online survey) and Phase Two (semi- structured interviews) made use of a convergent mixed methods approach, which allowed the data to drive and direct this study.
The following two chapters present and discuss the findings of Phase One (Chapter 4) and Phase Two (Chapter 5) of this study.