Using Repertory Grids to Test Data Quality and Experts’ Hunches Simone Stumpf and Janet McDonnell Department of Computer Science, University College London, Gower Street London WC1E 6BT
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Abstract The ‘theorise-inquire’ technique is described, which supports the testing of both experts’ hunches and the quality of data sources. This technique is useful for the identification of data sources and data gaps by domain experts. We describe and illustrate the use of group contrasts, an analysis technique that allows an expert to explore and interpret repertory grids interactively to find significant contrasting relationships between attributes and test these against data sources.
1. Introduction The aim of our work is concerned with making experts’ knowledge explicit, and then using this knowledge to steer data analysis and mining to support individual and organizational learning [1]. Knowledge management is as much about questioning individual and organisational assumptions as about sharing what is already known. Sometimes, what is considered reality is nothing but a collective hunch [2]. In this paper, we firstly describe a technique, the ‘theorise-inquire’ technique, which supports the testing of both experts’ hunches and the quality of data sources. This technique has general interest for the identification of data sources and data gaps for use by domain experts in a wide variety of fields. As part of the technique, we describe how group contrasts can be developed from repertory grids; these can be used in the expression and testing of experts’ hunches. This analysis allows an expert to explore and interpret repertory grids interactively to express and reveal significant contrasting relationships between attributes. Our work is being applied to the retail sector in the field of internal loss prevention to support improvements in knowledge management by providing greater efficiency in processing information using existing knowledge, and by supporting the creation of new knowledge to adapt to a changing environment [3]. This work helps retail security in three areas: predicting theft by retail staff, aiding in the
investigation of staff theft cases and removing opportunities for theft. Statistical analysis and machine learning techniques have increasingly been used to investigate and predict crime patterns, e.g. analysis of data can be used to predict the rate of crime [4]. Our approach differs in that domain experts are directly involved in every stage of the process.
2. The ‘Theorise-inquire’ Technique In our work, experts use a technique, termed ‘theoriseinquire’, that supports the capture and testing of knowledge [5]. Knowledge in this sense is intricately related to data: data “is raw numbers and facts, information is processed data, and knowledge is authenticated information” [6]. It could be argued that knowledge should dictate what data is collected, whilst data supports the confirmation of knowledge. The technique therefore addresses knowledge tests in both directions by investigating both experts’ hunches and the quality of data sources. To summarise, the technique proceeds through 4 stages. Firstly, tacit expert knowledge is made explicit through the use of repertory grids [7]. Once a satisfactory repertory grid has been captured, the experts concentrate on features that they believe to be important or interesting. We then make available a range of techniques for analyzing the repertory grid data that experts can understand; using these experts articulate and develop theories – their “collective hunches”. The features involved in the theories are then associated with appropriate data, either by providing a mapping between data sources or identifying data gaps that need to be addressed. Finally, knowledge is tested by applying the same analysis techniques to data and comparing these results with the experts’ theories.
2.1. Expressing Knowledge We capture the knowledge of experts about stereotypical situations in repertory grids. The repertory
4th International Workshop on Theory and Applications of Knowledge Management (TAKMA’03), Prague, 1-5 September 2003
1 Good management compliance Low store total net revenue Permanent employee Area manager's base store No previous burglary No previous robberies Immediate closure of audit (within period) Offender has family working in business Keyholder offender Offender Long service Offender Management High staff turnover (>30% p.a.) in store Inventory declaration available and justified High management turnover (>20% p.a.) in store Suspect temporary promotion Activity evident after trading hours good audit result/ low discrepancies low shrinkage store Store risk level high low level of autoreplenishments Offender low transfer rate store to store (1 store per year) low volume rental product loss store without manager Offender low appraisal rating
Walford 5 4 1 1 1 1 4 1 1 1 1 1 5 3 5 1 5 5 1 5 5 5 1 2
Homerton Village 5 3 1 1 2 2 4 1 1 2 1 1 5 1 1 1 5 5 1 5 5 5 1 3
Hackney Marshes 5 3 1 5 1 4 4 5 1 2 1 1 5 1 1 1 5 4 1 4 5 5 5 3
Bluetrench 3 1 1 5 1 1 2 5 1 1 1 4 1 4 5 1 3 2 1 5 1 2 1 4
Strikeby 3 1 1 5 2 1 2 1 1 5 5 4 5 3 5 1 3 3 1 2 1 3 1 2
Lowsound 2 3 5 5 5 2 1 1 5 5 1 2 5 2 1 1 5 5 1 5 3 5 5 2
5 Poor management compliance High store total net revenue Contracted employee Not area manager's base store 5 or more previous burglaries 4 or more previous robberies Prolonged audit closure (6 months or more) Offender has no family working in business No keyholder offender Offender Short service Offender Sales Person Low staff turnover(
