Main Article Content

Abstract

The field of ‘Statistics and Probability’ has expanded its scope over the last few decades and have become an integral part of many fields with continuously increasing demand. This manuscript aimed for at a bibliometric analysis and comparison of all published documents during 2015 – 2019, from journals in the study topic category of ‘Statistics and Probability’ for Q4 Impact Factor (IF) journals and Emerging Source Citation Index (ESCI) of Web of Science (WoS). Sources with incomplete data for study timeframe were excluded and 31 sources from Q4 IF and 32 from ESCI journals were selected yielding 12808 and 4294 documents respectively. After data extraction from WoS, the bibliometric analysis at; source, author and document levels, were performed using “Bibliometrix” R-package. Q4-IF sources produced around 3 times more documents than ESCI sources. Articles were the main document type for both categories. China and USA were leading countries for Q4-IF while India, USA and Korea were dominant among ESCI documents. Two authors, namely, ‘Cordeiro GM’ and ‘Alizadeh M’ were among the 10 most productive authors in both categories. Sources “Communications in Statistics-Theory and Methods” and “Korean Journal of Applied Statistics” were leading contributors for Q4-IF and ESCI category respectively. For both categories, mainly similar trends were observed for keywords and topic coverage. In both Q4-IF and ESCI journals ‘Maximum likelihood’ and ‘Ordered statistics’ were observed to be most predominant keywords. A consistent publication trend with few similarities was observed in terms of documents production over the years for these two categories.

Keywords

Statistics and Probability Bibliomining Bibliometrics Web of Science ESCI Q4 Journals

Article Details

How to Cite
Butt, N. S. (2021). Bibliomining and Comparison of Q4 and ESCI Indexed journals under Statistics and Probability Category. Pakistan Journal of Statistics and Operation Research, 17(1), 25-34. https://doi.org/10.18187/pjsor.v17i1.3243

References

  1. Abramo, G., & D’Angelo, C. A. (2011). Evaluating research: from informed peer review to bibliometrics. Scientometrics, 87(3), 499-514. DOI: https://doi.org/10.1007/s11192-011-0352-7
  2. Anderlucci, L., Montanari, A., & Viroli, C. (2019). The Importance of Being Clustered: Uncluttering the Trends of Statistics from 1970 to 2015. Statistical Science, 34(2), 280-300. DOI: https://doi.org/10.1214/18-STS686
  3. Aria, M., & Cuccurullo, C. J. J. o. i. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. 11(4), 959-975. DOI: https://doi.org/10.1016/j.joi.2017.08.007
  4. Butt, N. S., Malik, A. A. & Shahbaz, M. Q. (2021). Bibliometric Analysis of Statistics Journals Indexed in Web of Science under Emerging Source Citation Index SAGE Open, 11(1) 1-8. DOI: https://doi.org/10.1177/2158244020988870 DOI: https://doi.org/10.1177/2158244020988870
  5. De Battisti, F., Ferrara, A., & Salini, S. (2015). A decade of research in statistics: A topic model approach. Scientometrics, 103(2), 413-433. DOI: https://doi.org/10.1007/s11192-015-1554-1
  6. Donoho, D. (2017). 50 years of data science. Journal of Computational and Graphical Statistics, 26(4), 745-766. DOI: https://doi.org/10.1080/10618600.2017.1384734
  7. Drummond, G. B., & Tom, B. D. (2011). Statistics, probability, significance, likelihood: words mean what we define them to mean. Advances in physiology education, 35(4), 361-364. DOI: https://doi.org/10.1152/advan.00060.2011
  8. Egghe, L. (2006). Theory and practise of the g-index. Scientometrics, 69(1), 131-152. DOI: https://doi.org/10.1007/s11192-006-0144-7
  9. ESCI-WoS. (2015). Emerging Sources Citation Index (ESCI), ISI Web of Knowledge by Clarivate Analytics (formerly known as Thomson Reuters). https://clarivate.com/webofsciencegroup/solutions/webofscience-esci/
  10. Eto, H. (2000). Bibliometric distance between methodology and application in statistics. Scientometrics, 48(1), 85-97. DOI: https://doi.org/10.1023/A:1005684419288
  11. Harzing, A.-W. (2010). The publish or perish book: Tarma Software Research Pty Limited.
  12. Hirsch, J. E. (2005). An index to quantify an individual's scientific research output. Proceedings of the National academy of Sciences, 102(46), 16569-16572. DOI: https://doi.org/10.1073/pnas.0507655102
  13. Hossain, M. M. (2020). Current status of global research on novel coronavirus disease (Covid-19): A bibliometric analysis and knowledge mapping. Available at SSRN 3547824. DOI: https://doi.org/10.2139/ssrn.3547824
  14. Jelercic, S., Lingard, H., Spiegel, W., Pichlhöfer, O., & Maier, M. (2010). Assessment of publication output in the field of general practice and family medicine and by general practitioners and general practice institutions. Family practice, 27(5), 582-589. DOI: https://doi.org/10.1093/fampra/cmq032
  15. Merigó, J. M., & Yang, J.-B. (2017). A bibliometric analysis of operations research and management science. Omega, 73, 37-48. DOI: https://doi.org/10.1016/j.omega.2016.12.004
  16. MJL-WoS. Master Journal List (MJL), Web of Science Group. Retrieved 11.02.2020 https://mjl.clarivate.com/search-results
  17. Ronda-Pupo, G. A., Díaz-Contreras, C., Ronda-Velázquez, G., & Ronda-Pupo, J. C. (2015). The role of academic collaboration in the impact of Latin-American research on management. Scientometrics, 102(2), 1435-1454. DOI: https://doi.org/10.1007/s11192-014-1486-1
  18. Ryan, T. P., & Woodall, W. H. (2005). The most-cited statistical papers. Journal of Applied Statistics, 32(5), 461-474. DOI: https://doi.org/10.1080/02664760500079373
  19. Secchi, P. (2018). On the role of statistics in the era of big data: A call for a debate. Statistics & Probability Letters, 136, 10-14. DOI: https://doi.org/10.1016/j.spl.2018.02.041
  20. Shieh, J. C. (2010). The integration system for librarians' bibliomining. The Electronic Library. DOI: https://doi.org/10.1108/02640471011081988
  21. Shukla, A. K., Muhuri, P. K., & Abraham, A. (2020). A bibliometric analysis and cutting-edge overview on fuzzy techniques in Big Data. Engineering Applications of Artificial Intelligence, 92, 103625. DOI: https://doi.org/10.1016/j.engappai.2020.103625
  22. Stigler, S. M. (1994). Citation patterns in the journals of statistics and probability. Statistical Science, 94-108. DOI: https://doi.org/10.1214/ss/1177010655
  23. Varin, C., Cattelan, M., & Firth, D. (2016). Statistical modelling of citation exchange between statistics journals. Journal of the Royal Statistical Society. Series A,(Statistics in Society), 179(1), 1. DOI: https://doi.org/10.1111/rssa.12124
  24. Vílchez-Román, C. (2014). Bibliometric factors associated with h-index of Peruvian researchers with publications indexed on Web of Science and Scopus databases. TransInformação, 26(2), 143-154. DOI: https://doi.org/10.1590/0103-37862014000200004
  25. WoS. Clarivate Analytics (Formerly Thomson Reuters), Web of Science. Retrieved from https://clarivate.com/webofsciencegroup/solutions/web-of-science/
  26. Yi, H., Ao, X., & Ho, Y.-S. (2008). Use of citation per publication as an indicator to evaluate pentachlorophenol research. Scientometrics, 75(1), 67-80. DOI: https://doi.org/10.1007/s11192-007-1849-y
  27. Yu, D., & He, X. (2020). A bibliometric study for DEA applied to energy efficiency: Trends and future challenges. Applied Energy, 268, 115048. DOI: https://doi.org/10.1016/j.apenergy.2020.115048
  28. Yu, D., Xu, Z., Pedrycz, W., & Wang, W. (2017). Information Sciences 1968–2016: a retrospective analysis with text mining and bibliometric. Information Sciences, 418, 619-634. DOI: https://doi.org/10.1016/j.ins.2017.08.031