There are a few schools of thought in the literature. Prior to consolidating the suitable qualitative analysis for this study a review of available strategies by several theorist are discussed. Two of the most influential groups of theorists are the naturalists (Miles and Huberman, 1994) and grounded theory (Glaser and Strauss, 1967). In this section, several data analysis methods are compared (see Table 4.15) which help to derive a suitable strategy for data analysis in this study. Based on the grounded theory, collection and analysis of data obtained needs to go in hand as theories and themes will then emerged during the investigation (Glaser and Strauss, 1967). This is the strength of grounded theory development.
Table 4.15 Qualitative data analysis model – a comparison
Citation (Glaser and Strauss,
1967)
(Miles and Huberman,
1994)
(Kvale, 1996) (Baker, 1999) (Bryman,
2004) (Gillham, 2005) Model Grounded theory Qualitative data analysis Interview data analysis Qualitative data analysis Interview data analysis Interview data analysis Process/ Stages Collect data Note taking Coding Memoing Categorising/ sorting- Saturate data Writing report Data reduction Data display Conclusion & verification Condensation Structuring/ Narrative Interpretation Ad-hoc Condense data (coding & memoing) Display data (themes, patterns ) Develop & test conclusion Code Read Re-read Review codes Theoretical ideas Slice data Transcribe Categorical analysis Derive category (coding) Write report Combine with other sources Source: Research analysis compilation
When all the relevant information has been collected from both semi-structured interviews and multiple sources of documents, the next stage involves analysing the data. In Wahyuni‟s (2003) study, the author argued that qualitative interviews capture richness and complexity of subject matter that needs to be explained in a comprehensive manner. Apparently, the adoption of interviews as research methods involves challenges as well. It is not merely new method as it yields qualitative text rather than quantitative data (Kvale, 1996) but tools of research to gather facts (Gillham, 2005). But the „facts‟ do not speak for themselves however they are obtained. All raw data requires interpretations which involved analysis (Corbin and Strauss, 2008). However, it can almost never be a finish process. As there is always a need to extend, amend and reinterpret when new insights or situations arise. Therefore, analysis is a process of generating, developing and verifying concepts, that are built up over time and with the acquisition of data (Corbin and Strauss, 2008).
Reviewing the various strategies of data analysis in Table 4.15 had provided a clearer insight into the process of data analysis. In the next few paragraphs the steps taken for this research will be presented.
Data analysis can be broken down into several actions as illustrated in Figure 4.4. First, after all data is collected from each respondent in the case study. The interviewed data will be transcribed using Express Scribe (NCH), a computer assisted transcription software to generate a written interview report from each respondent. Then, when all the information has been gathered, each interview report is read, re-read iteratively to derive meaningful categorical analysis through the coding technique. According to Glaser and Strauss (1967) coding is one of the most central processes in grounded theory. At this first step, open coding is the initial step used to break down, examine, compare and later group into categories (Glaser and Strauss, 1967, Bryman, 2004). In open coding, the researcher is immersed into the data through line by line analysis, coding as much data as possible and writing memos about the conceptual and theoretical ideas that may emerge during the analysis process. The process is completed when the researcher begins to see the possibility of a theory that embraces all the data (Walker and Myrick, 2006).
Second, once categories of themes are developed, the second phase of coding known as axial coding (Glaser and Strauss, 1967) is done in each of the identified themes in the report. This action brings forth sub-codes from the original codes. It helps the researcher to further derive immersed categories from the data. In addition, it helps to break down, conceptualise and put back data into new meanings, an action in building theories from data (Wahyuni, 2003). The process will be an iterative cycle until all data are saturated and no new theories are derived.
Third, after immersed categories are derived through coding, the data needs to be presented and displayed with the use of some inventive method. According to Miles and Huberman (1994), data display goes beyond data reduction providing a more organised, compressed assembly of information that permits conclusion drawing. It can be in an extended piece of text, chart, matrix or diagram to elaborate the ideas that had been developed (Baker, 1999, Miles and Huberman, 1994).
Hence, at this stage a computer assisted qualitative data analysis software (CAQDAS) NVivo 8 (QSR, 2007) will be utilised to assist the development of an appropriate diagrammatic form to display the data and make extrapolations. The use
of this application will help to discern systematic patterns and relationships that will help derive an interpretative analysis in the last stage.
Fourth, the process of cross-checking the analysed data and adherence to the principles of best practice case studies methodology as described in section 4.4 were carried out. The examination of multiple sources of evidences from the case studied is carried out in this stage. At this stage, a process of cross-checking findings derived from secondary sources such as project documentations obtained from respondents are conducted, a process also known as triangulation (Bryman, 2004). Triangulating is a mean to understand the historical context within events such as company documents that increases the validity of findings in reflection to the theory postulated (Stavros and Westberg, 2009). Finally, upon consolidating all the themes, a diagrammatic representation of codes and interrelationships of each category is derived.
The next stage involves writing a report of the analysis. Completed reports of analysis and summary of the recommendations will be sent to all key respondents (both university and industry). The intention is to provide an opportunity for key respondents to review, validate any mistakes in the supplied information and for respondents to provide recommendations and suggestions in their perspectives on the proposed practices and methodology. Steps of qualitative data analysis adopted in this study are summarised in Figure 4.4.
Interview data collected
Transcribe data
upon collection Categorisation
Interpretation Triangulate analysed data with other sources Derive categorical analysis Data presentation
Figure 4.4 Model of qualitative data analysis