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Normalization of zero-inflated data: An empirical analysis of a new indicator family and its use with altmetrics data

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  • Bornmann, Lutz
  • Haunschild, Robin

Abstract

Recently, two new indicators (Equalized Mean-based Normalized Proportion Cited, EMNPC; Mean-based Normalized Proportion Cited, MNPC) were proposed which are intended for sparse scientometrics data, e.g., alternative metrics (altmetrics). The indicators compare the proportion of mentioned papers (e.g. on Facebook) of a unit (e.g., a researcher or institution) with the proportion of mentioned papers in the corresponding fields and publication years (the expected values). In this study, we propose a third indicator (Mantel-Haenszel quotient, MHq) belonging to the same indicator family. The MHq is based on the MH analysis – an established method in statistics for the comparison of proportions. We test (using citations and assessments by peers, i.e. F1000Prime recommendations) if the three indicators can distinguish between different quality levels as defined on the basis of the assessments by peers. Thus, we test their convergent validity. We find that the indicator MHq is able to distinguish between the quality levels in most cases while MNPC and EMNPC are not. Since the MHq is shown in this study to be a valid indicator, we apply it to six types of zero-inflated altmetrics data and test whether different altmetrics sources are related to quality. The results for the various altmetrics demonstrate that the relationship between altmetrics (Wikipedia, Facebook, blogs, and news data) and assessments by peers is not as strong as the relationship between citations and assessments by peers. Actually, the relationship between citations and peer assessments is about two to three times stronger than the association between altmetrics and assessments by peers.

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  • 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.
    • Handle: RePEc:eee:infome:v:12:y:2018:i:3:p:998-1011
    DOI: 10.1016/j.joi.2018.01.010
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    References listed on IDEAS

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    9. Ehsan Mohammadi & Mike Thelwall & Kayvan Kousha, 2016. "Can Mendeley bookmarks reflect readership? A survey of user motivations," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 67(5), pages 1198-1209, May.
    10. Bornmann, Lutz & Haunschild, Robin, 2016. "Normalization of Mendeley reader impact on the reader- and paper-side: A comparison of the mean discipline normalized reader score (MDNRS) with the mean normalized reader score (MNRS) and bare reader ," Journal of Informetrics, Elsevier, vol. 10(3), pages 776-788.
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    16. Mojisola Erdt & Aarthy Nagarajan & Sei-Ching Joanna Sin & Yin-Leng Theng, 2016. "Altmetrics: an analysis of the state-of-the-art in measuring research impact on social media," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(2), pages 1117-1166, November.
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    18. Haunschild, Robin & Bornmann, Lutz, 2016. "Normalization of Mendeley reader counts for impact assessment," Journal of Informetrics, Elsevier, vol. 10(1), pages 62-73.
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    20. Lutz Bornmann, 2015. "Interrater reliability and convergent validity of F1000Prime peer review," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 66(12), pages 2415-2426, December.
    21. Franceschet, Massimo & Costantini, Antonio, 2011. "The first Italian research assessment exercise: A bibliometric perspective," Journal of Informetrics, Elsevier, vol. 5(2), pages 275-291.
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    23. Bornmann, Lutz, 2014. "Do altmetrics point to the broader impact of research? An overview of benefits and disadvantages of altmetrics," Journal of Informetrics, Elsevier, vol. 8(4), pages 895-903.
    24. Lutz Bornmann & Robin Haunschild & Werner Marx, 2016. "Policy documents as sources for measuring societal impact: how often is climate change research mentioned in policy-related documents?," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(3), pages 1477-1495, December.
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    Cited by:

    1. 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.
    2. Jianhua Hou & Bili Zheng & Yang Zhang & Chaomei Chen, 2021. "How do Price medalists’ scholarly impact change before and after their awards?," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(7), pages 5945-5981, July.
    3. 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.
    4. Huang, Cui & Yang, Chao & Su, Jun, 2021. "Identifying core policy instruments based on structural holes: A case study of China’s nuclear energy policy," Journal of Informetrics, Elsevier, vol. 15(2).
    5. 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).
    6. 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.

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