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A mixed longitudinal and cross-sectional model to forecast the journal impact factor in the field of Dentistry

Author

Listed:
  • Pilar Valderrama

    (University of Granada)

  • Manuel Escabias

    (University of Granada)

  • Evaristo Jiménez-Contreras

    (University of Granada)

  • Mariano J. Valderrama

    (University of Granada)

  • Pilar Baca

    (University of Granada)

Abstract

In order to estimate the impact factor value for a journal in Dentistry, two sets of variables were considered in this study: the first takes in the longitudinal behavior of the process specified in the slope and intercept of the straight line fitted to the trend of the last years, whereas the second considers the percentage of review papers published each year and the adhesion degree of the journal to ICMJE guidelines. The final estimated model showed a high determination coefficient (99.3%) and its performance was tested on a new set of journals randomly sampled from the list of journal citation reports.

Suggested Citation

  • Pilar Valderrama & Manuel Escabias & Evaristo Jiménez-Contreras & Mariano J. Valderrama & Pilar Baca, 2018. "A mixed longitudinal and cross-sectional model to forecast the journal impact factor in the field of Dentistry," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(2), pages 1203-1212, August.
  • Handle: RePEc:spr:scient:v:116:y:2018:i:2:d:10.1007_s11192-018-2801-z
    DOI: 10.1007/s11192-018-2801-z
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    References listed on IDEAS

    as
    1. Pilar Valderrama & Manuel Escabias & Evaristo Jiménez-Contreras & Alberto Rodríguez-Archilla & Mariano J. Valderrama, 2018. "Proposal of a stochastic model to determine the bibliometric variables influencing the quality of a journal: application to the field of Dentistry," Scientometrics, Springer;Akadémiai Kiadó, vol. 115(2), pages 1087-1095, May.
    2. Sungmin Park, 2015. "The R&D logic model: Does it really work? An empirical verification using successive binary logistic regression models," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(3), pages 1399-1439, December.
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    4. Tian Yu & Guang Yu & Peng-Yu Li & Liang Wang, 2014. "Citation impact prediction for scientific papers using stepwise regression analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(2), pages 1233-1252, November.
    5. Liping Yu & Houqiang Yu, 2016. "Does the average JIF percentile make a difference?," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(3), pages 1979-1987, December.
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    7. Wah Yun Low & Kwan Hoong Ng & M. A. Kabir & Ai Peng Koh & Janaki Sinnasamy, 2014. "Erratum to: Trend and impact of international collaboration in clinical medicine papers published in Malaysia," Scientometrics, Springer;Akadémiai Kiadó, vol. 100(2), pages 607-607, August.
    8. Xingchen Li & Qiang Wu & Yuanyuan Liu, 2017. "A quantitative analysis of researcher citation personal display considering disciplinary differences and influence factors," Scientometrics, Springer;Akadémiai Kiadó, vol. 113(2), pages 1093-1112, November.
    9. Claudia Contreras & Gonzalo Edwards & Alejandra Mizala, 2006. "The Current Impact Factor and the long-term impact of scientific journals by discipline: A logistic diffusion model estimation," Scientometrics, Springer;Akadémiai Kiadó, vol. 69(3), pages 689-696, December.
    10. Samreen Ayaz & Nayyer Masood & Muhammad Arshad Islam, 2018. "Predicting scientific impact based on h-index," Scientometrics, Springer;Akadémiai Kiadó, vol. 114(3), pages 993-1010, March.
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    Cited by:

    1. Wanjun Xia & Tianrui Li & Chongshou Li, 2023. "A review of scientific impact prediction: tasks, features and methods," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(1), pages 543-585, January.
    2. Pilar Valderrama & Evaristo Jiménez-Contreras & Manuel Escabias & Mariano J. Valderrama, 2022. "Introducing a bibliometric index based on factor analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(1), pages 509-522, January.

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