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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. Bornmann, Lutz & Haunschild, Robin & Adams, Jonathan, 2019. "Do altmetrics assess societal impact in a comparable way to case studies? An empirical test of the convergent validity of altmetrics based on data from the UK research excellence framework (REF)," Journal of Informetrics, Elsevier, vol. 13(1), pages 325-340.
    2. 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.
    3. Haunschild, Robin & Leydesdorff, Loet & Bornmann, Lutz & Hellsten, Iina & Marx, Werner, 2019. "Does the public discuss other topics on climate change than researchers? A comparison of explorative networks based on author keywords and hashtags," Journal of Informetrics, Elsevier, vol. 13(2), pages 695-707.
    4. Bornmann, Lutz & Haunschild, Robin, 2018. "Normalization of zero-inflated data: An empirical analysis of a new indicator family and its use with altmetrics data," Journal of Informetrics, Elsevier, vol. 12(3), pages 998-1011.
    5. Thelwall, Mike, 2017. "Three practical field normalised alternative indicator formulae for research evaluation," Journal of Informetrics, Elsevier, vol. 11(1), pages 128-151.
    6. Sergio Copiello, 2020. "Other than detecting impact in advance, alternative metrics could act as early warning signs of retractions: tentative findings of a study into the papers retracted by PLoS ONE," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 2449-2469, December.
    7. Sergio Copiello, 2020. "Multi-criteria altmetric scores are likely to be redundant with respect to a subset of the underlying information," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(1), pages 819-824, July.
    8. Lutz Bornmann & Robin Haunschild, 2018. "Do altmetrics correlate with the quality of papers? A large-scale empirical study based on F1000Prime data," PLOS ONE, Public Library of Science, vol. 13(5), pages 1-12, May.
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