Curve Estimation Models for Estimation and Prediction of Impact Factor and CiteScore Using the Journal Percentiles: A Case Study of Telecommunication Journals

Hilary I Okagbue, Patience I Adamu, Sheila A Bishop, Emmanuela C M Obasi, Adedotun O Akinola


The impact factor  and CiteScore of journals are known to be positively correlated with journal percentile but the use of the later to predict the formers are scarcely discussed, especially for journals in a specific subject classifications based on the web of science. This paper proposed different curve estimation models for predicting the impact factor and CiteScore of 89 telecommunication journals using their corresponding percentiles. Out of the 11 models, only Logistic, exponential, Growth and Compound models are the best models for predicting the impact factor and CiteScore using their corresponding journal percentiles. The models were chosen because of their high values of R Square and Adjusted R Square and low values of the standard error of the estimates. In addition, strong significant positive correlations were obtained between impact factor and the CiteScore of the journals. The findings will help authors and editors in decision making as regards to manuscript submission and planning.


Impact factor; CiteScore; Quartiles; Percentiles; curve estimation; ranking analytics; statistics.

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International Journal of Online and Biomedical Engineering (iJOE) – eISSN: 2626-8493
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