A Statistical Approach to Model the H-Index Based on the Total Number of Citations and the Duration from the Publishing of the First Article
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 (h) is the most popular of different metrics to measure these activities. This research deals with presenting a pr...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/6351836 |
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| _version_ | 1832566028491554816 |
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| author | Mohammad Reza Mahmoudi Marzieh Rahmati Zulkefli Mansor Amirhosein Mosavi Shahab S. Band |
| author_facet | Mohammad Reza Mahmoudi Marzieh Rahmati Zulkefli Mansor Amirhosein Mosavi Shahab S. Band |
| author_sort | Mohammad Reza Mahmoudi |
| collection | DOAJ |
| description | 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 (h) 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 (NC) and the duration from the publishing of the first article (D1). To determine the effect of every factor (NC and D1) on h, we applied a set of simple nonlinear regression. The results indicated that both NC and D1 had a significant effect on h (p < 0.001). 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 (p < 0.001). 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. |
| format | Article |
| id | doaj-art-dcd0c962f431459186fb340dd4494175 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-dcd0c962f431459186fb340dd44941752025-02-03T01:05:27ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/63518366351836A Statistical Approach to Model the H-Index Based on the Total Number of Citations and the Duration from the Publishing of the First ArticleMohammad Reza Mahmoudi0Marzieh Rahmati1Zulkefli Mansor2Amirhosein Mosavi3Shahab S. Band4Department of Statistics, Faculty of Science, Fasa University, Fasa, Fars, IranDepartment of Computer Engineering, Yazd University, Yazd, IranFaculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, MalaysiaEnvironmental Quality, Atmospheric Science and Climate Change Research Group, Ton Duc Thang University, Ho Chi Minh City, VietnamInstitute of Research and Development, Duy Tan University, Da Nang 550000, VietnamThe 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 (h) 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 (NC) and the duration from the publishing of the first article (D1). To determine the effect of every factor (NC and D1) on h, we applied a set of simple nonlinear regression. The results indicated that both NC and D1 had a significant effect on h (p < 0.001). 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 (p < 0.001). 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.http://dx.doi.org/10.1155/2021/6351836 |
| spellingShingle | Mohammad Reza Mahmoudi Marzieh Rahmati Zulkefli Mansor Amirhosein Mosavi Shahab S. Band A Statistical Approach to Model the H-Index Based on the Total Number of Citations and the Duration from the Publishing of the First Article Complexity |
| title | A Statistical Approach to Model the H-Index Based on the Total Number of Citations and the Duration from the Publishing of the First Article |
| title_full | A Statistical Approach to Model the H-Index Based on the Total Number of Citations and the Duration from the Publishing of the First Article |
| title_fullStr | A Statistical Approach to Model the H-Index Based on the Total Number of Citations and the Duration from the Publishing of the First Article |
| title_full_unstemmed | A Statistical Approach to Model the H-Index Based on the Total Number of Citations and the Duration from the Publishing of the First Article |
| title_short | A Statistical Approach to Model the H-Index Based on the Total Number of Citations and the Duration from the Publishing of the First Article |
| title_sort | statistical approach to model the h index based on the total number of citations and the duration from the publishing of the first article |
| url | http://dx.doi.org/10.1155/2021/6351836 |
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