Abstract

The productivity of researchers and the impact of the work they do are 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 with presenting 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 on h, we applied a set of simple nonlinear regression. The results indicated that both NC and D1 had a significant effect on h. The determination of coefficient for these equations to estimate the h-index was 93.4% and 39.8%, respectively, which verified that the model based on NC had a better fit. Then, to record the simultaneous effects of NC and D1 on h, multiple nonlinear regression was applied. The results indicated that NC and D1 had a significant effect on h. Also, the determination of coefficient for this equation to estimate h was 93.6%. Finally, to model and estimate the h-index, as a function of NC and D1, multiple nonlinear quartile regression was used. The goodness of the fitted model was also assessed.