Modeling the H-Index Based on the Total Number of Citations and the Duration from the First Publication

EasyChair Preprint 7762

Abstract

The productivity of researchers and the impact of the work they do is a preoccupation of universities, research funding agencies and sometimes even researchers themselves. The h-index is the most popular of different metrics to measure these activities. This research deals to present a practical approach to model the h-index based on the total number of citations and the duration from the publishing of the first article. To determine the effect of every factor (C and D) on H, we applied a set of simple nonlinear regression. The results indicated that both C and D had significant effect on H (p<0.001). The power of these equations to estimate of H was 93.4% and 39.8%, respectively, that verified the model based on C had a better fit. Then, to investigate the simultaneous effects of C and D on H, multiple nonlinear regression were applied. The results indicated that C and D had significant effect on H (p<0.001). Also, the power of this equation to estimate of H was 93.6%. Finally, to model and estimate the h-index, h, as a function of C and D, the multiple nonlinear quartile regression was used. The goodness of fitted model also was also assessed.

Keyphrases: Regression, citation, duration, h-index, modelling, relationship

@booklet{EasyChair:7762,
  author    = {Mohammad Reza Mahmoudi and Marzieh Rahmati and Zulkefli Mansor and Amir Mosavi and Shahab S. Band},
  title     = {Modeling the H-Index Based on the Total Number of Citations and the Duration from the First Publication},
  howpublished = {EasyChair Preprint 7762},
  year      = {EasyChair, 2022}}