Improving the normalization effect of mean-based method from the perspective of optimization: optimization-based linear methods and their performance

Zhihui Zhang (), Ying Cheng () and Nian Cai Liu ()
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Zhihui Zhang: Shanghai Jiao Tong University
Ying Cheng: Shanghai Jiao Tong University
Nian Cai Liu: Shanghai Jiao Tong University

Scientometrics, 2015, vol. 102, issue 1, No 31, 587-607

Abstract: Abstract Mean-based method may be the most popular linear method for field normalization of citation impact. However, the relatively good but not ideal performance of mean-based method, plus its being a special case of the general scaling method y = kx and the more general affine method y = kx + b, implies that more effective linear methods may exist. Under the idea of making the citation distribution of each field approximate a common reference distribution through the transformation of scaling method and affine method with unknown parameters k and b, we derived the scaling and affine methods under separate unweighted and weighted optimization models for 236 Web of Science subject categories. While the unweighted-optimization-based scaling and affine methods did not show full advantages over mean-based method, the weighted-optimization-based affine method showed a decided advantage over mean-based method along most parts of the distributions. At the same time, the trivial advantage of weighted-optimization-based scaling method over mean-based method indirectly validated the good normalization performance of mean-based method. Based on these results, we conclude that mean-based method is acceptable for general field normalization, but in the face of higher demands on normalization effect, the weighted-optimization-based affine method may be a better choice.

Keywords: Citation distribution; Field normalization; Mean-based method; Optimization-based scaling method; Optimization-based affine method (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s11192-014-1398-0

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