Understandability and Transparency of the Financial Statements of ...

analysis is both complete and impartial. He considered that NVivo enhances confidence
in the impartiality of the analysis because: “(1) it provides a chain of evidence; (2) all
cases are used in the evaluation; and (3) provides an analytical framework from which
the research problem can be tested” (Smith, 2003, p. 137). Consequently, „NVivo‟, was
utilised in this study to aid data reduction.
The initial themes, based on the four complexities of the financial statements, and
related problems identified in the literature review, were coded using NVivo „Tree
Nodes‟ (Appendix Seven). The use of NVivo enabled un-coded data to be easily
identifiable which aided in identifying further patterns in the evidence as a whole (Moll
et al., 2006; O'Dwyer, 2004; Robson, 1993; Taylor & Bogdan, 1998). These additional
themes, which recurred in the interview evidence had not been anticipated, so were
added to the preset categories of „Tree Nodes‟. The ability, through NVivo, to display
the data as an “organised, compressed assembly of information” (Ghauri & Gronhaug,
2005, p. 207) meant that conclusions in the interpretation of the data were able to be
drawn, which is discussed next.
4.6.2 Data interpretation
Data interpretation is the critical aspect of data analysis where the research moves from
description to interpretation (Taylor & Bogdan, 1998). To interpret any data not coded
into a „Tree Node‟, the data was first looked at for concepts, i.e. words or phrases in the
interviews that captured the meaning behind why the interviewees acted as they did.
Next the themes identified in the data reduction (section 4.6.1) were united by these
different concepts. As different themes were identified the data was looked at for
underlying similarities between them, and propositions were developed (Moll et al.,
2006; O'Dwyer, 2004; Taylor & Bogdan, 1998). Ryan et. al. considers this reasoning
leads to findings which must be substantiated by statements consisting of assertions,
justifications and explanations to prove the conclusion (Ryan, Scapens, & Theobald,
1992).
This supports Patton (2002) who considers that these findings must be of „substantive
significance‟, which differs from the „statistical significance‟ in quantitative studies. To
determine substantive significance certain aspects need to be addressed, including:
o Does the evidence clearly support the findings, e.g. triangulation can be used to
determine the strength of evidence;
o Do the findings deepen understanding, i.e. „verstehen‟;
91