title: Adding scientific rigour to qualitative data analysis: an illustrative example

authors: Samkin, Grant

subjects: accounting standards

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.

text: Adding scientific rigour to qualitative data analysis: an illustrative example 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.

year: 2008

title: Nachhaltigkeitslücken in der frühkindlichen Erziehung: Paradigmatischer und pädagogischer Wandel in Aotearoa Neuseeland?

authors: White, Elizabeth Jayne

abstract: Dieser Beitrag antwortet auf die von Julie Davis geäußerte Kritik an einer erheblichen Forschungslücke im Zusammenhang mit dem Forschungsdiskurs zur Nachhaltigkeit im Bereich der frühkindlichen Pädagogik. Ihre Kritik in Bezug auf die auch international noch unzureichend entwickelte Forschung hebt vor allem auf grundsätzliche Schwierigkeiten von akademischen Untersuchungen im Bereich dieser kindlichen Altersgruppe und auf praktische Probleme bei der Entwicklung neuer wissenschaftlicher Forschungsfelder ab. In diesem Essay wollen wir jedoch auf eine noch grundsätzlichere Dimension dieser Forschungslücke, man könnte auch sagen: dieses Forschungsabgrunds ("research hole"), eingehen, die wir als Resultat eines mangelnden kritischen Dialoges über das, was Nachhaltigkeit for uns, die Lehrer und Lerner eigentlich ist, ansehen.

text: Nachhaltigkeitslücken in der frühkindlichen Erziehung: Paradigmatischer und pädagogischer Wandel in Aotearoa Neuseeland? Dieser Beitrag antwortet auf die von Julie Davis geäußerte Kritik an einer erheblichen Forschungslücke im Zusammenhang mit dem Forschungsdiskurs zur Nachhaltigkeit im Bereich der frühkindlichen Pädagogik. Ihre Kritik in Bezug auf die auch international noch unzureichend entwickelte Forschung hebt vor allem auf grundsätzliche Schwierigkeiten von akademischen Untersuchungen im Bereich dieser kindlichen Altersgruppe und auf praktische Probleme bei der Entwicklung neuer wissenschaftlicher Forschungsfelder ab. In diesem Essay wollen wir jedoch auf eine noch grundsätzlichere Dimension dieser Forschungslücke, man könnte auch sagen: dieses Forschungsabgrunds ("research hole"), eingehen, die wir als Resultat eines mangelnden kritischen Dialoges über das, was Nachhaltigkeit for uns, die Lehrer und Lerner eigentlich ist, ansehen.

year: 2011

title: "He Matapihi ki te Mana Raraunga” - Conceptualising Big Data through a Māori lens

authors: Hudson, Maui

abstract: Advances in computer technology and analytical processes create an environment where data becomes the raw material mined to create valuable information and insights. The idea of Big Data emerges from the collation of increasing vast amounts of data enabled by the shift towards an increasingly open data environment. How this changing context alters the relationship between Iwi/Māori collectives and their mātauranga, cultural information and data has yet to be fully explored. However, the concept of Māori Data Sovereignty, which anticipates Māori governance over Māori data, has a natural appeal. This chapter outlines some of the Māori concepts and presents a framework which may be used to inform how data and data use may be conceptualised through a Māori cultural lens.

text: "He Matapihi ki te Mana Raraunga” - Conceptualising Big Data through a Māori lens Advances in computer technology and analytical processes create an environment where data becomes the raw material mined to create valuable information and insights. The idea of Big Data emerges from the collation of increasing vast amounts of data enabled by the shift towards an increasingly open data environment. How this changing context alters the relationship between Iwi/Māori collectives and their mātauranga, cultural information and data has yet to be fully explored. However, the concept of Māori Data Sovereignty, which anticipates Māori governance over Māori data, has a natural appeal. This chapter outlines some of the Māori concepts and presents a framework which may be used to inform how data and data use may be conceptualised through a Māori cultural lens.

year: 2017