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Statistical tests for ‘related records’ search results

Author

Listed:
  • Charles H. Smith

    (Western Kentucky University)

  • Patrick Georges

    (University of Ottawa)

  • Ngoc Nguyen

    (Western Kentucky University)

Abstract

Related records searching, now a common option within bibliographic databases, is applied to an individual result record as a secondary way of refining the retrieval set obtained from the primary subject search operation. In one approach, an individual result record is linked to other article records on the basis of the number of references cited they share in common, the theory being that two articles that cite many of the same sources are likely to be highly similar in subject content. Results of the secondary search are usually displayed in the order of each item’s actual number of commonly-shared references. In the present paper we suggest an improved way of ranking the results, employing statistical significance tests. We suggest two approaches, one involving a statistical test previously unknown in bibliometric circles, the binomial index of dispersion, and the other employing the more familiar centralized cosine measure; these turn out to produce nearly identical results. An example demonstrating the application of these measures, and contrasting such with the use of raw totals, is provided. In the example the results rankings are found to be only modestly (positively) correlated, suggesting that much information is lost to the user when raw totals alone are made the basis for ordering results.

Suggested Citation

  • Charles H. Smith & Patrick Georges & Ngoc Nguyen, 2015. "Statistical tests for ‘related records’ search results," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(3), pages 1665-1677, December.
    • Handle: RePEc:spr:scient:v:105:y:2015:i:3:d:10.1007_s11192-015-1610-x
    DOI: 10.1007/s11192-015-1610-x
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    References listed on IDEAS

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    1. repec:ucp:bkecon:9780226320625 is not listed on IDEAS
    2. Dangzhi Zhao & Andreas Strotmann, 2014. "In-text author citation analysis: Feasibility, benefits, and limitations," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 65(11), pages 2348-2358, November.
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    Cited by:

    1. Patrick Georges, 2017. "Western classical music development: a statistical analysis of composers similarity, differentiation and evolution," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(1), pages 21-53, July.
    2. Müge Akbulut & Yaşar Tonta & Howard D. White, 2020. "Related records retrieval and pennant retrieval: an exploratory case study," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(2), pages 957-987, February.
    3. Patrick Georges & Ngoc Nguyen, 2019. "Visualizing music similarity: clustering and mapping 500 classical music composers," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(3), pages 975-1003, September.

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