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Abstract

The present two-part article introduces matrix comparison as a formal means of evaluation in informetric studies such as cocitation analysis. In this first part, the motivation behind introducing matrix comparison to informetric studies, as well as two important issues influencing such comparisons, are introduced and discussed. The motivation is spurred by the recent debate on choice of proximity measures and their potential influence upon clustering and ordination results. The two important issues discussed here are matrix generation and the composition of proximity measures. The approach to matrix generation is demonstrated for the same data set, i.e., how data is represented and transformed in a matrix, evidently determines the behavior of proximity measures. Two different matrix generation approaches, in all probability, will lead to different proximity rankings of objects, which further lead to different ordination and clustering results for the same set of objects. Further, a resemblance in the composition of formulas indicates whether two proximity measures may produce similar ordination and clustering results. However, as shown in the case of the angular correlation and cosine measures, a small deviation in otherwise similar formulas can lead to different rankings depending on the contour of the data matrix transformed. Eventually, the behavior of proximity measures, that is whether they produce similar rankings of objects, is more or less data-specific. Consequently, the authors recommend the use of empirical matrix comparison techniques for individual studies to investigate the degree of resemblance between proximity measures or their ordination results. In part two of the article, the authors introduce and demonstrate two related statistical matrix comparison techniques the Mantel test and Procrustes analysis, respectively. These techniques can compare and evaluate the degree of monotonicity between different proximity measures or their ordination results. As such, the Mantel test and Procrustes analysis can be used as statistical validation tools in informetric studies and thus help choosing suitable proximity measures.

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Abstract

This article introduces the concept of relevance as viewed and applied in the context of IR evaluation, by presenting an overview of the multidimensional and dynamic nature of the concept. The literature an relevance reveals how the relevance concept, especially in regard to the multidimensionality of relevance, is many faceted, and does not just refer to the various relevance criteria users may apply in the process of judging relevance of retrieved information objects. From our point of view, the multidimensionality of relevance explains why some will argue that no consensus has been reached an the relevance concept. Thus, the objective of this article is to present an overview of the many different views and ways by which the concept of relevance is used-leading to a consistent and compatible understanding of the concept. In addition, special attention is paid to the type of situational relevance. Many researchers perceive situational relevance as the most realistic type of user relevance, and therefore situational relevance is discussed with reference to its potential dynamic nature, and as a requirement for interactive information retrieval (IIR) evaluation.

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Abstract

The present two-part article introduces matrix comparison as a formal means for evaluation purposes in informetric studies such as cocitation analysis. In the first part, the motivation behind introducing matrix comparison to informetric studies, as well as two important issues influencing such comparisons, matrix generation, and the composition of proximity measures, are introduced and discussed. In this second part, the authors introduce and thoroughly demonstrate two related matrix comparison techniques the Mantel test and Procrustes analysis, respectively. These techniques can compare and evaluate the degree of monotonicity between different proximity measures or their ordination results. In common with these techniques is the application of permutation procedures to test hypotheses about matrix resemblances. The choice of technique is related to the validation at hand. In the case of the Mantel test, the degree of resemblance between two measures forecast their potentially different affect upon ordination and clustering results. In principle, two proximity measures with a very strong resemblance most likely produce identical results, thus, choice of measure between the two becomes less important. Alternatively, or as a supplement, Procrustes analysis compares the actual ordination results without investigating the underlying proximity measures, by matching two configurations of the same objects in a multidimensional space. An advantage of the Procrustes analysis though, is the graphical solution provided by the superimposition plot and the resulting decomposition of variance components. Accordingly, the Procrustes analysis provides not only a measure of general fit between configurations, but also values for individual objects enabling more elaborate validations. As such, the Mantel test and Procrustes analysis can be used as statistical validation tools in informetric studies and thus help choosing suitable proximity measures.

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Date

8. 3.2007 19:55:22