Understandability and Transparency of the Financial Statements of ...

4.6 Data Analysis:
Data analysis is the process of bringing order, structure and meaning to the mass of
collected data from the document review and the interviews (Ghauri & Gronhaug,
2005). It shows the intellectual and mechanical work of the research, and has two key
aspects: (1) data reduction and (2) data display before drawing or verifying conclusions
(Ghauri & Gronhaug, 2005; Moll et al., 2006; O'Dwyer, 2004; Patton, 2002; Taylor &
Bogdan, 1998).
4.6.1 Data reduction
Taylor and Bogdan (1998) considered that qualitative data analysis is the process of
inductive reasoning, thinking and theorising. However, before analysis there needed to
be some form of data reduction of all the data sources collected in this research,
including interview transcripts and interview notes (Ghauri & Gronhaug, 2005;
O'Dwyer, 2004).
To aid in this reduction the data collected was initially analysed by identifying
similarities and differences in the reasons for the accounting treatments between
charities and any other issue that was identified in the literature review. To further
enhance inductive reasoning the data was further reduced by continually analysing and
coding, which involved re-reading the transcripts and identifying new themes and
developing further insights (Bedard & Gendron, 2004; Taylor & Bogdan, 1998). To aid
this, reference was made to the hunches, interpretations and ideas recorded in the
interview notes.
The next stage was to analyse the interview data with particular emphasis on the
constructions inherent in each interview. This was undertaken through an inductive
thematic analysis and coding of the interview transcripts utilising NVivo. Several
authors consider the ability to gain some mastery over all of the data is one of the key
benefits of using a computer assisted qualitative data analysis software for data
reduction (Anderson-Gough, 2004; Robson, 1993). They consider that being able to
scroll around a screen and at the push of several buttons being able to reproduce
„packages‟ of output that are relevant to the analysis much easier than looking through
piles of paper.
Also Smith (2003) considered that qualitative data is vulnerable because, unlike
quantitative data, there is an absence of established techniques for ensuring that data
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