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Field- and time-normalization of data with many zeros: an empirical analysis using citation and Twitter data

Author

Listed:
  • Robin Haunschild

    (Max Planck Institute for Solid State Research)

  • Lutz Bornmann

    (Administrative Headquarters of the Max Planck Society)

Abstract

Thelwall (J Informetr 11(1):128–151, 2017a. https://doi.org/10.1016/j.joi.2016.12.002 ; Web indicators for research evaluation: a practical guide. Morgan and Claypool, London, 2017b) proposed a new family of field- and time-normalized indicators, which is intended for sparse data. These indicators are based on units of analysis (e.g., institutions) rather than on the paper level. They compare the proportion of mentioned papers (e.g., on Twitter) of a unit with the proportion of mentioned papers in the corresponding fields and publication years. We propose a new indicator (Mantel–Haenszel quotient, MHq) for the indicator family. The MHq is rooted in the Mantel–Haenszel (MH) analysis. This analysis is an established method, which can be used to pool the data from several 2 × 2 cross tables based on different subgroups. We investigate using citations and assessments by peers whether the indicator family can distinguish between quality levels defined by the assessments of peers. Thus, we test the convergent validity. We find that the MHq is able to distinguish between quality levels in most cases while other indicators of the family are not. Since our study approves the MHq as a convergent valid indicator, we apply the MHq to four different Twitter groups as defined by the company Altmetric. Our results show that there is a weak relationship between the Twitter counts of all four Twitter groups and scientific quality, much weaker than between citations and scientific quality. Therefore, our results discourage the use of Twitter counts in research evaluation.

Suggested Citation

  • Robin Haunschild & Lutz Bornmann, 2018. "Field- and time-normalization of data with many zeros: an empirical analysis using citation and Twitter data," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(2), pages 997-1012, August.
    • Handle: RePEc:spr:scient:v:116:y:2018:i:2:d:10.1007_s11192-018-2771-1
    DOI: 10.1007/s11192-018-2771-1
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    References listed on IDEAS

    as
    1. Fairclough, Ruth & Thelwall, Mike, 2015. "National research impact indicators from Mendeley readers," Journal of Informetrics, Elsevier, vol. 9(4), pages 845-859.
    2. 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.
    3. Haunschild, Robin & Bornmann, Lutz, 2016. "Normalization of Mendeley reader counts for impact assessment," Journal of Informetrics, Elsevier, vol. 10(1), pages 62-73.
    4. 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.
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    6. Thelwall, Mike, 2017. "Three practical field normalised alternative indicator formulae for research evaluation," Journal of Informetrics, Elsevier, vol. 11(1), pages 128-151.
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    8. Bornmann, Lutz, 2014. "Validity of altmetrics data for measuring societal impact: A study using data from Altmetric and F1000Prime," Journal of Informetrics, Elsevier, vol. 8(4), pages 935-950.
    9. Ludo Waltman & Rodrigo Costas, 2014. "F1000 Recommendations as a Potential New Data Source for Research Evaluation: A Comparison With Citations," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 65(3), pages 433-445, March.
    10. Lutz Bornmann & Robin Haunschild, 2017. "Measuring field-normalized impact of papers on specific societal groups: An altmetrics study based on Mendeley Data," Research Evaluation, Oxford University Press, vol. 26(3), pages 230-241.
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    Cited by:

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    2. 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.
    3. Peiling Wang & Joshua Williams & Nan Zhang & Qiang Wu, 2020. "F1000Prime recommended articles and their citations: an exploratory study of four journals," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(2), pages 933-955, February.
    4. 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.
    5. Wenceslao Arroyo-Machado & Daniel Torres-Salinas & Nicolas Robinson-Garcia, 2021. "Identifying and characterizing social media communities: a socio-semantic network approach to altmetrics," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(11), pages 9267-9289, November.
    6. Ting Cong & Zhichao Fang & Rodrigo Costas, 2022. "WeChat uptake of chinese scholarly journals: an analysis of CSSCI-indexed journals," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(12), pages 7091-7110, December.

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