Adding scientific rigour to qualitative data analysis: an illustrative example

doi:10.1108/11766090810910227

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Qualitative Research in Accounting & Management

ISSN: 1176-6093

Online from: 2004

Subject Area: Accounting and Finance

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Adding scientific rigour to qualitative data analysis: an illustrative example

Document Information:
Title:	Adding scientific rigour to qualitative data analysis: an illustrative example
Author(s):	Grant Samkin, (Department of Accounting, University of Waikato, Wellington, New Zealand), Annika Schneider, (Department of Accounting, University of Waikato, Wellington, New Zealand)
Citation:	Grant Samkin, Annika Schneider, (2008) "Adding scientific rigour to qualitative data analysis: an illustrative example", Qualitative Research in Accounting & Management, Vol. 5 Iss: 3, pp.207 - 238
Keywords:	Accounting standards, Data analysis, Neural nets, Qualitative methods
Article type:	Research paper
DOI:	10.1108/11766090810910227 (Permanent URL)
Publisher:	Emerald Group Publishing Limited
Acknowledgements:	Joe Woelfel and Hao Chen (PhD Candidate) from the Department of Communication at State University of New York at Buffalo are thanked for their assistance in the rotation and interpretation of the coordinate files and output. Stewart Lawrence is thanked for his advice and thoughtful comments. All errors however remain the responsibility of the authors.
Abstract:	Purpose – The purpose of this paper is to illustrate how qualitative data may be analysed using a method that can be considered as rigorous/scientific as any statistical analysis of quantitative data. Design/methodology/approach – An artificial neural network programme CATPAC II™ was used to evaluate selected portions of two accounting standards: the Financial Reporting Standards Board of New Zealand's standard on consolidation; and the equivalent standard developed by the International Accounting Standards Committee and revised by the International Accounting Standards Board. Findings – The analysis of the concepts of control in the two standards identifies the differences that exist between the two standards. These differences are illuminated through the use of a hierarchical cluster analysis of 40 unique concepts in each of the two standards and 2D representation of the concepts. The extent of the differences in the concepts was established through a rotational analysis of the two datasets. Research limitations/implications – This research is limited to the analysis of the concept of control and associated commentary paragraphs and supporting documents associated with two accounting standards. Different results may have been obtained had the whole standard been analysed. Practical implications – Artificial neural network software can be used to support the intuitive textual understanding of the differences that exist in qualitative data. In this paper, the differences identified in the concepts of control may result in different interpretations being taken by the accounting standard users when determining what reporting entities to include in consolidated financial statements. Some additional uses for artificial neural network software in accounting research are also identified. Originality/value – This paper is the first in the discipline to use artificial neural network software to analyse and compare different texts.