Quality & Quantity 36: 277–289, 2002.
© 2002 Kluwer Academic Publishers. Printed in the Netherlands.
Utilising Recent Advancements in Techniques for
the Analysis of Incomplete Multivariate Data to
Improve the Data Quality Management of Current
DAVID J. FOGARTY
and JOHN BLAKE
Leeds Metropolitan University, United Kingdom
Abstract. This paper discusses the importance of managing data quality in academic research in
its relation to satisfying the customer. This focus is on the data completeness objective dimension
of data quality in relation to recent advancements which have been made in the development of
methods for analysing incomplete multivariate data. An overview and comparison of the traditional
techniques with the recent advancements are provided. Multiple imputation is also discussed as a
method of analysing incomplete multivariate data, which can potentially reduce some of the biases
which can occur from using some of the traditional techniques. Despite these recent advancements
in the analysis of incomplete multivariate data, evidence is presented which shows that researchers
are not using these techniques to manage the data quality of their current research across a variety of
academic disciplines. An analysis is then provided as to why these techniques have not been adopted
along with suggestions to improve the frequency of their use in the future.
Source-Reference. The ideas for this paper originated from research work on David J. Fog-
arty’s Ph.D. dissertation. The subject area is the use of advanced techniques for the imputation of
incomplete multivariate data on corporate data warehouses.
Key words: multiple imputation, missing data, data quality, incomplete multivariate data, single
Data are used in the delivery of scientiﬁc research and in many products and ser-
vices and for industry, and so data quality is an important component of customers’
perceptions of the quality of these research studies, products and services. Custom-
ers for academic research represent editors, reviewers and readers. In a strict sense,
the editorial board could be considered to be the customer and the reader as the
consumer. Since researchers create and deliver a product (research article) for the
needs of their customers and consumers it is important that customers of academic
research perceive the management of poor data quality to lead to unbiased results
in the research. If not, the customer may return the product, or worse, both the
Author for correspondence: David J. Fogarty, 36 Crescent Road, Riverside, CT 06878, U.S.A.