A comparison of bibliometric indicators for evaluating the top 100 Open Access Journals

Authors

Dr. Abu Waris Assistant Professor, Deanship of Library Affairs, Imam Abdulrahman Bin Faisal University Post Box. No. 1982, Dammam, Saudi Arabia E-mail: abuwaris@iau.edu.sa, Mobile: 966-592512409
Dr. Mirza Muhammad Naseer Assistant Professor, Deanship of Library Affairs, Imam Abdulrahman Bin Faisal University, Post Box 1982, Dammam, Saudi Arabia, E-mail: mmnaseer@iau.edu.sa, Mobile: 966-563839278
Shakil Ahmad Lecturer, Deanship of Library Affairs, Imam Abdulrahman Bin Faisal University, Post Box. No. 1982, Dammam, Saudi Arabia, E-mail: shahmad@iau.edu.sa, Mobile: 966-558172752
Isam Mohammed Abdel-Magid, Professor of water resources and environmental engineering, Environmental Engineering Department, College of Engineering, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31451, KSA
Md Safiqur rahaman, Librarian, Deanship of Library Affairs, King Fahd University of Petroleum and Minerals, Dhahran, KSA mdsafiqur@kfupm.edu.sa Contact: 0138601308 ORCID: 0000-0003-1367-2618
Manuelraj Peter, Deanship of Library Affairs, Imam Abdulrahman Bin Faisal UniversityFollow

Date of this Version

Summer 5-7-2021

Document Type

Article

Abstract

A comparative study was performed based on scientific metrics for the year 2019 for the top 100 open access journals (OAJ) extracted from the web of science. The considered bibliometric indicators are Journal Impact Factor (JIF), Eugene Factor Score (ES), Scimago Journal Ranking (SJR), Source Normalized Impact per Paper (SNIP), Cite Score (CS), Google Scholar (GS), and H5 index. This study shows that journals have different ranks compared with their metrics of the various scientific indicators. Correlations were explained by applying Pearson's and spearman's statistical correlation using SPSS software 21.0 packages. Spearman's Pearson's (r) statistical correlation was found between JIF and CS (r=0.898).

Regarding spearman's (ρ) statistical correlations, the highest correlations were established between JIF and CS (ρ=0.690). It concludes that researchers and institutions cannot rely on a single indicator to measure the impact. It showed a direct linear co-relationship between the indicators, which concludes that performance measurement will be more reliable and accurate when more indicators are used for this purpose.