Loading...
A new algorithm for zero-modified models applied to citation counts
Wilson, Paul
Wilson, Paul
Editors
Other contributors
Affiliation
Epub Date
Issue Date
2019-08-31
Submitted date
Alternative
Abstract
Finding statistical models for citation count data is important for those seeking to understand the citing process or when using regression to identify factors that associate with citation rates. As sets of citation counts often include more or less zeros (uncited articles) than would be expected under the base distribution, it is essential to deal appropriately with them. This article proposes a new algorithm to fit zero-modified versions of discretised log-normal, hooked power-law and Weibull models to citation count data from 23 different Scopus categories from 2012. The new algorithm allows the standard errors of all parameter estimates to be calculated, and hence also confidence intervals and p-values. This algorithm can also estimate negative zero-modification parameters corresponding to zero-deflation (fewer uncited articles than expected). The results find no universal best model for the 23 categories and a given dataset may be zero-inflated relative to one model, but zero-deflated relative to another
Citation
Shahmandi, M., Wilson, P. and Thelwall, M. (2019) A new algorithm for zero-modified models applied to citation counts, in Catalano, G., Daraio, C., Gregori, M., Moed, H. F. and Ruocco, G. (eds.) 17th International Conference on Scientometrics & Informetrics ISSI2019, Volume I, 2-5 September 2019 Sapienza University of Rome, Italy, pp. 1020-1031.
Journal
Research Unit
DOI
PubMed ID
PubMed Central ID
Embedded videos
Type
Conference contribution
Language
en
Description
Series/Report no.
ISSN
EISSN
ISBN
9788833811185