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Interpretation and inference for altmetric indicators arising from sparse data statistics

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  • Smolinsky, Lawrence
  • Klingenberg, Bernhard
  • Marx, Brian D.

Abstract

In 2018 Bornmann and Haunschild (2018a) introduced a new indicator called the Mantel-Haenszel quotient (MHq) to measure alternative metrics (or altmetrics) of scientometric data. In this article we review the Mantel-Haenszel statistics, point out two errors in the literature, and introduce a new indicator. First, we correct the interpretation of MHq and mention that it is still a meaningful indicator. Second, we correct the variance formula for MHq, which leads to narrower confidence intervals. A simulation study shows the superior performance of our variance estimator and confidence intervals. Since MHq does not match its original description in the literature, we propose a new indicator, the Mantel-Haenszel row risk ratio (MHRR), to meet that need. Interpretation and statistical inference for MHRR are discussed. For both MHRR and MHq, a value greater (less) than one means performance is better (worse) than in the reference set called the world.

Suggested Citation

  • Smolinsky, Lawrence & Klingenberg, Bernhard & Marx, Brian D., 2022. "Interpretation and inference for altmetric indicators arising from sparse data statistics," Journal of Informetrics, Elsevier, vol. 16(1).
  • Handle: RePEc:eee:infome:v:16:y:2022:i:1:s1751157722000025
    DOI: 10.1016/j.joi.2022.101250
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    References listed on IDEAS

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    1. Omar Kassab, 2019. "Does public outreach impede research performance? Exploring the ‘researcher’s dilemma’ in a sustainability research center," Science and Public Policy, Oxford University Press, vol. 46(5), pages 710-720.
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