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Reliability of researcher capacity estimates and count data dispersion: a comparison of Poisson, negative binomial, and Conway-Maxwell-Poisson models

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  • Boris Forthmann

    (University of Münster)

  • Philipp Doebler

    (TU Dortmund University)

Abstract

Item-response models from the psychometric literature have been proposed for the estimation of researcher capacity. Canonical items that can be incorporated in such models to reflect researcher performance are count data (e.g., number of publications, number of citations). Count data can be modeled by Rasch’s Poisson counts model that assumes equidispersion (i.e., mean and variance must coincide). However, the mean can be larger as compared to the variance (i.e., underdispersion), or b) smaller as compared to the variance (i.e., overdispersion). Ignoring the presence of overdispersion (underdispersion) can cause standard errors to be liberal (conservative), when the Poisson model is used. Indeed, number of publications or number of citations are known to display overdispersion. Underdispersion, however, is far less acknowledged in the literature. In the current investigation the flexible Conway-Maxwell-Poisson count model is used to examine reliability estimates of capacity in relation to various dispersion patterns. It is shown, that reliability of capacity estimates of inventors drops from .84 (Poisson) to .68 (Conway-Maxwell-Poisson) or .69 (negative binomial). Moreover, with some items displaying overdispersion and some items displaying underdispersion, the dispersion pattern in a reanalysis of Mutz and Daniel’s (2018b) researcher data was found to be more complex as compared to previous results. To conclude, a careful examination of competing models including the Conway-Maxwell-Poisson count model should be undertaken prior to any evaluation and interpretation of capacity reliability. Moreover, this work shows that count data psychometric models are well suited for decisions with a focus on top researchers, because conditional reliability estimates (i.e., reliability depending on the level of capacity) were highest for the best researchers.

Suggested Citation

  • Boris Forthmann & Philipp Doebler, 2021. "Reliability of researcher capacity estimates and count data dispersion: a comparison of Poisson, negative binomial, and Conway-Maxwell-Poisson models," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(4), pages 3337-3354, April.
  • Handle: RePEc:spr:scient:v:126:y:2021:i:4:d:10.1007_s11192-021-03864-8
    DOI: 10.1007/s11192-021-03864-8
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    1. Seth D. Guikema & Jeremy P. Goffelt, 2008. "A Flexible Count Data Regression Model for Risk Analysis," Risk Analysis, John Wiley & Sons, vol. 28(1), pages 213-223, February.
    2. Wolfgang Glänzel & Henk F. Moed, 2013. "Opinion paper: thoughts and facts on bibliometric indicators," Scientometrics, Springer;Akadémiai Kiadó, vol. 96(1), pages 381-394, July.
    3. John C. Huber & Roland Wagner-Döbler, 2001. "Scientific production: A statistical analysis of authors in mathematical logic," Scientometrics, Springer;Akadémiai Kiadó, vol. 50(2), pages 323-337, February.
    4. Henk F. Moed & Gali Halevi, 2015. "Multidimensional assessment of scholarly research impact," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 66(10), pages 1988-2002, October.
    5. Hall, B. & Jaffe, A. & Trajtenberg, M., 2001. "The NBER Patent Citations Data File: Lessons, Insights and Methodological Tools," Papers 2001-29, Tel Aviv.
    6. John C. Huber & Roland Wagner-Döbler, 2001. "Scientific production: A statistical analysis of authors in physics, 1800-1900," Scientometrics, Springer;Akadémiai Kiadó, vol. 50(3), pages 437-453, March.
    7. Rolf Ketzler & Klaus F. Zimmermann, 2013. "A citation-analysis of economic research institutes," Scientometrics, Springer;Akadémiai Kiadó, vol. 95(3), pages 1095-1112, June.
    8. De Boeck, Paul & Bakker, Marjan & Zwitser, Robert & Nivard, Michel & Hofman, Abe & Tuerlinckx, Francis & Partchev, Ivailo, 2011. "The Estimation of Item Response Models with the lmer Function from the lme4 Package in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 39(i12).
    9. Didegah, Fereshteh & Thelwall, Mike, 2013. "Which factors help authors produce the highest impact research? Collaboration, journal and document properties," Journal of Informetrics, Elsevier, vol. 7(4), pages 861-873.
    10. Li, Guan-Cheng & Lai, Ronald & D’Amour, Alexander & Doolin, David M. & Sun, Ye & Torvik, Vetle I. & Yu, Amy Z. & Fleming, Lee, 2014. "Disambiguation and co-authorship networks of the U.S. patent inventor database (1975–2010)," Research Policy, Elsevier, vol. 43(6), pages 941-955.
    11. Yutao Sun & Belle Selene Xia, 2016. "The scholarly communication of economic knowledge: a citation analysis of Google Scholar," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(3), pages 1965-1978, December.
    12. Burrell, Quentin L., 2007. "Hirsch's h-index: A stochastic model," Journal of Informetrics, Elsevier, vol. 1(1), pages 16-25.
    13. Gerhard Fischer, 1987. "Applying the principles of specific objectivity and of generalizability to the measurement of change," Psychometrika, Springer;The Psychometric Society, vol. 52(4), pages 565-587, December.
    14. Gad Yair & Keith Goldstein, 2020. "The Annus Mirabilis paper: years of peak productivity in scientific careers," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(2), pages 887-902, August.
    15. Subrata Chakraborty & Tomoaki Imoto, 2016. "Extended Conway-Maxwell-Poisson distribution and its properties and applications," Journal of Statistical Distributions and Applications, Springer, vol. 3(1), pages 1-19, December.
    16. M. J. Faddy & R. J. Bosch, 2001. "Likelihood-Based Modeling and Analysis of Data Underdispersed Relative to the Poisson Distribution," Biometrics, The International Biometric Society, vol. 57(2), pages 620-624, June.
    17. S. Chakraborty & S. H. Ong, 2016. "A COM-Poisson-type generalization of the negative binomial distribution," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 45(14), pages 4117-4135, July.
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    Cited by:

    1. Boris Forthmann, 2023. "Researcher capacity estimation based on the Q model: a generalized linear mixed model perspective," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(8), pages 4753-4764, August.
    2. Kennedy Ndue & Melese Mulu Baylie & Pál Goda, 2023. "Determinants of Rural Households’ Intensity of Flood Adaptation in the Fogera Rice Plain, Ethiopia: Evidence from Generalised Poisson Regression," Sustainability, MDPI, vol. 15(14), pages 1-19, July.
    3. Krzysztof Rusek & Agnieszka Kleszcz & Albert Cabellos-Aparicio, 2023. "Bayesian inference of spatial and temporal relations in AI patents for EU countries," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(6), pages 3313-3335, June.

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    More about this item

    Keywords

    Researcher capacity; Item response models; Rasch Poisson count model; Conway-Maxwell-Poisson count model; Dispersion; Reliability;
    All these keywords.

    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General

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