Modelling of Factors Influencing the Citation Counts in Statistics

Abstract

Citation is considered as the most popular quality assessment metric for scientific papers, and it is thus important to determine what factors promote the citation count of a paper in comparison to the others in the same field. The main aim of this study is to model the citation counts of the research published in SCI or SCI-Expanded journals of Statistics field with the growing number of scientific works in Turkey. It is well known that the right-skewed nature of the counts makes the classical regression modelling inappropriate, even if the log transformation of counts is applied [1]. Due to the fact that distribution of citation counts involves a great number of zeros, this study serves for an additional aim that is to model the counts with advanced discrete regression models for a more precise prediction [2]. Data collected for this study consist of the citation counts of all scientific papers produced by 261 Statisticians in between 2005-2017. Discrete models varying from Poisson to Zero-Inflated or Hurdle were constructed by possible influential factors, such as the publication age, the number of references, the journal category etc. Predictive performances of alternative discrete models were compared via AIC and Vuong test [3]. Results suggested that Zero Inflated Negative Binomial and Hurdle Negative Binomial mixture models are the best forms to predict the zero inflation of citation counts [4]. In addition, the influential factors of the final model were interpreted to make some suggestions to Statisticians to increase the citation counts of their work.

Keywords

• citation count
• count data regression
• negative binomial regression
• zero-inflated regression

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