LOCATION OF SCIENTIFIC JOURNALS IN QUARTILES ACCORDING TO SJR

PREDICTION FROM MULTIVARIATE STATISTICS

Authors

DOI: https://doi.org/10.6018/analesdoc.455951
Keywords: Discriminant analysis, neuronal networks, scientific magazines, impact, classification

Abstract

The quartile system for the classification of scientific journals is analyzed, through multivariate statistical classification, using data from the official Scopus website in 2019. A sample of 5740 records was taken and four indicators were extracted (CiteScore, Citation Percentage, SJR and Percentile) in addition to the location quartile (Q). The behavior of the indicators was analyzed through descriptive statistics by quartile, in addition to classification through Discriminant Analysis and Artificial Neural Networks. The quartile with the highest indicator dispersion was Q1 and the quartile with the most homogeneous indicators was Q4. Discriminant Analysis showed 97.82% of correctly classified quartiles and 97.23% with RNA. There are magazines that do not adjust to the quartile where it is, according to multivariate statistics. It was observed that the most influential factor in the classification is the Percentile and not the impact indicators.

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Author Biography

Dany Day Josefina Arriojas Tocuyo, Petróleos de Venezuela

Gerencia de calidad del dato. Petróleos de Venezuela

References

ALEIXANDRE-BENAVENT, R.; VALDERRAMA-ZURIÁN, J.C. y GONZÁLEZ-ALCAIDE, G. El factor de impacto de las revistas científicas: limitaciones e indicadores alternativos. El profesional de la información, 2007, vol. 16, n° 1, p. 4-11. Disponible en: http://doi.org/10.3145/epi.2007.jan.01.

BELTRÁN, O.A. Factor de Impacto. Revista Colombiana de Gastroenterología, 2006, vol. 21, n° 1, p. 57-61. Disponible en: <http://www.scielo.org.co/pdf/rcg/v21n1/v21n1a09.pdf> [Consulta: 11 de septiembre de 2020]

BELTER, C. Bibliometric indicators: opportunities and limits. Journal of the Medical Library Association, 2015, vol. 103, n° 4, p. 219-221. Disponible en: https://dx.doi.org/10.3163/1536-5050.103.4.014.

CAMPANARIO, J.M. Journals that Rise from the Fourth Quartile to the First Quartile in Six Years or Less: Mechanisms of Change and the Role of Journal Self-Citations. Publications, 2018, vol. 6, n° 47, p. 1-15. Disponible en: https://dx.doi.org/10.3390/publications6040047.

ENNAS, G. y DI GUARDO, M.C. Features of top-rated gold open access journals: An analysis of the scopus database. Journal of Informetrics, 2015, vol. 9, n° 1, p. 79-89. Disponible en: https://doi.org/10.1016/j.joi.2014.11.007.

GANDHI, N.; PETKAR, O. y ARMSTRONG, L.J. Rice crop yield prediction using artificial neural networks. En 2016 IEEE Technological Innovations in ICT for Agriculture and Rural Development (TIAR), 2016, Chennai, India (p. 105-110). Disponible en: https://doi.org/10.1109/TIAR.2016.7801222.

GARCÍA, P. y SANCHO, J. Estimación de densidad de probabilidad mediante ventanas de Parzen. En Jornadas de introducción a la investigación de la UPCT. 2010. Disponible en: <https://dialnet.unirioja.es/servlet/articulo?codigo=3709476> [Consulta: 14 de septiembre de 2020]

GARFIELD, E. The History and Meaning of the Journal Impact Factor. JAMA, 2006, vol. 295, n° 1, p. 90-93. Disponible en: http://doi.org/10.1001/jama.295.1.90.

GESTAL, M. Introducción a las redes neuronales [en línea]. 2013. Disponible en: <https://www.researchgate.net/publication/242099672> [Consulta: 24 de septiembre de 2020]

GÓMEZ-NÚÑEZ, A.J. et al. Optimizing SCImago Journal & Country Rank classification by community detection. Journal of Informetrics, 2014, vol. 8, n° 2, p. 369-383. Disponible en: https://doi.org/10.1016/j.joi.2014.01.011.

GRECH, V. y RIZK, D. Increasing importance of research metrics: Journal Impact Factor and h-index. International Urogynecology Journal, 2018, vol. 29, p. 619-620. Disponible en: https://doi.org/10.1007/s00192-018-3604-8.

HOPKINS, W. A New View of Statistics [en línea]. 2014. Disponible en: <https://complementarytraining.net/wp-content/uploads/2013/10/Will-Hopkins-A-New-View-of-Statistics.pdf> [Consulta: 25 de septiembre de 2020]

LINS, A.J.C.C. et al. Using artificial neural networks to select the parameters for the prognostic of mild cognitive impairment and dementia in elderly individuals. Computer Methods and Programs in Biomedicine, 2017, vol. 152, p. 93-104. Disponible en: https://doi.org/10.1016/j.cmpb.2017.09.013.

MEHDI, Z.; ALI, M.; ABOLGHASEM, K.R. y FARAMARZ, D.A. Classification of environmental geochemical data using discriminant analysis and neural network in carbonate-sulfide waste dumps of lead and zinc mines. Iranian Journal of Mining Engineering (IRJME), 2019, vol. 14, n° 44, p. 12-25. Disponible en: <https://www.sid.ir/en/journal/ViewPaper.aspx?ID=727041> [Consulta: 18 de septiembre de 2020]

MEYERHOLZ, D.K. y FLAHERTY, H.A. The Evolving Significance and Future Relevance of the Impact Factor. Veterinary Pathology, 2017, vol. 54, n° 4, p. 721-722. Disponible en: https://doi.org/10.1177/0300985817690209.

MIRÓ, Ò. y BURBANO, P. El factor de impacto, el índice h et al. indicadores bibliométricos. Anales del Sistema Sanitario de Navarra, 2013, vol. 36, n° 3, p. 371-377. Disponible en: http://dx.doi.org/10.4321/S1137-66272013000300001.

MURES, M.J.; GARCÍA, A. y VALLEJO, M.E. Aplicación del análisis discriminante y Regresión Logística en el estudio de la morosidad en las entidades financieras. Comparación de resultados. Pecvnia, 2005, vol. 1, p. 75-199.

NATURE EDITORIAL. Time to remodel the journal impact factor. Nature, 2016, vol. 535. Disponible en: https://doi.org/10.1038/535466a.

ORBAY, K.; MIRANDA, R. y ORBAY, M. Building Journal Impact Factor Quartile into the Assessment of Academic Performance: A Case Study. Participatory Educational Research (PER), 2020, vol. 7, n° 2, p. 1-13. Disponible en: http://dx.doi.org/10.17275/per.20.26.7.2.

RAHAYU, W.; SANTI, V.M. y PUTRI, B.S. Classification of diabetes events using discriminant analysis. En Journal of Physics: Conference Series 1402, 077102. 2019. Disponible en: https://dx.doi.org/10.1088/1742-6596/1402/7/077102.

RODRÍGUEZ, R.; SOCORRO, A. y ESPINOZA, C. Análisis de Scimago Journal & Country Rank, utilidad para el desarrollo bibliométrico en la Universidad Metropolitana del Ecuador. Revista Publicando, 2019, vol. 6, n° 21, p. 58-68.

SHARMA, M. et al. Journal Impact Factor: Its Use, Significance and Limitations. World Journal of Nuclear Medicine, 2014, vol. 13, n° 2, p. 146. Disponible en: http://doi.org/10.4103/1450-1147.139151.

SILVA, E.G. et al. Cotton genotypes selection through artificial neural networks. Genetics and Molecular Research, 2017, vol. 16, n° 3, gmr16039798. Disponible en: http://dx.doi.org/10.4238/gmr16039798.

VASEN, F. y LUJANO, I. Sistemas nacionales de clasificación de revistas científicas en América Latina: tendencias recientes e implicaciones para la evaluación académica en ciencias sociales. Revista mexicana de ciencias políticas y sociales, 2017, vol. 62, n° 231, p. 199-228. Disponible en: <http://www.scielo.org.mx/pdf/rmcps/v62n231/0185-1918-rmcps-62-231-00199.pdf> [Consulta: 30 de septiembre de 2020]

Published
18-02-2021
How to Cite
Marín Velásquez, T., & Arriojas Tocuyo, D. D. J. (2021). LOCATION OF SCIENTIFIC JOURNALS IN QUARTILES ACCORDING TO SJR: PREDICTION FROM MULTIVARIATE STATISTICS. Information Science Journal, 24(1). https://doi.org/10.6018/analesdoc.455951
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