Evaluation of websites for biomedical postgraduate courses in Spanish

El objeto de este trabajo es la creación de una herramienta para la evaluación de la calidad de la información contenida en los sitios web de Postgrado de ámbito biosanitario en las universidades españolas. Se ha diseñado y desarrollado una hoja de evaluación (checklist) que ha sido validada y aplicada a los 131 sitios web de Postgrado con Mención de Calidad de tema biosanitario de las universidades españolas. Se han analizado las valoraciones obtenidas por los sitios web y se han aplicado técnicas de clustering y de análisis de componentes principales. Los datos recogidos por la checklist permiten establecer un ranking según la calidad de la información de los sitios web. Además, se observa la existencia de tres grandes grupos de sitios web según sus características y prestaciones. La aplicación de las herramientas diseñadas indica que los sitios web alcanzan valores aceptables, si bien presentan algunos defectos comunes. No obstante, se constata la existencia de varios niveles de calidad de los mismos.The aim of this work is to create a tool for assessing the quality of the information on postgraduate course websites at Spanish universities. An evaluation checklist was developed and applied to the 131 websites of postgraduate biomedical courses with quality accreditation in Spanish universities. The website evaluations were analysed with the application of clustering and principal component analysis techniques. While the average of all the sites is ‘acceptable’ there remain some clear weaknesses in aspects such as accessibility, lack of an internal search engine, or forms - for obtaining the views of current students and lecturers- and evaluation tests - for analysing the results. The tool developed provides a new instrument for evaluating postgraduate course websites. This evaluation enables website comparison, helps identify their strengths and weaknesses, and facilitates their improvement

Mathematical properties of weighted impact factors based on measures of prestige of the citing journals

The final publication is available at Springer via http://dx.doi.org/10.1007/s11192-015-1741-0An abstract construction for general weighted impact factors is introduced. We show that the classical weighted impact factors are particular cases of our model, but it can also be used for defining new impact measuring tools for other sources of information as repositories of datasets providing the mathematical support for a new family of altmet- rics. Our aim is to show the main mathematical properties of this class of impact measuring tools, that hold as consequences of their mathematical structure and does not depend on the definition of any given index nowadays in use. In order to show the power of our approach in a well-known setting, we apply our construction to analyze the stability of the ordering induced in a list of journals by the 2-year impact factor (IF2). We study the change of this ordering when the criterium to define it is given by the numerical value of a new weighted impact factor, in which IF2 is used for defining the weights. We prove that, if we assume that the weight associated to a citing journal increases with its IF2, then the ordering given in the list by the new weighted impact factor coincides with the order defined by the IF2. We give a quantitative bound for the errors committed. We also show two examples of weighted impact factors defined by weights associated to the prestige of the citing journal for the fields of MATHEMATICS and MEDICINE, GENERAL AND INTERNAL, checking if they satisfy the increasing behavior mentioned above.Ferrer Sapena, A.; Sánchez Pérez, EA.; González, LM.; Peset Mancebo, MF.; Aleixandre Benavent, R. (2015). Mathematical properties of weighted impact factors based on measures of prestige of the citing journals. Scientometrics. 105(3):2089-2108. https://doi.org/10.1007/s11192-015-1741-0S208921081053Ahlgren, P., & Waltman, L. (2014). The correlation between citation-based and expert-based assessments of publication channels: SNIP and SJR vs. Norwegian quality assessments. Journal of Informetrics, 8, 985–996.Aleixandre Benavent, R., Valderrama Zurián, J. C., & González Alcaide, G. (2007). Scientific journals impact factor: Limitations and alternative indicators. El Profesional de la Información, 16(1), 4–11.Altmann, K. G., & Gorman, G. E. (1998). The usefulness of impact factor in serial selection: A rank and mean analysis using ecology journals. Library Acquisitions-Practise and Theory, 22, 147–159.Arnold, D. N., & Fowler, K. K. (2011). Nefarious numbers. Notices of the American Mathematical Society, 58(3), 434–437.Beliakov, G., & James, S. (2012). Using linear programming for weights identification of generalized bonferroni means in R. In: Proceedings of MDAI 2012 modeling decisions for artificial intelligence. Lecture Notes in Computer Science, Vol. 7647, pp. 35–44.Beliakov, G., & James, S. (2011). Citation-based journal ranks: The use of fuzzy measures. Fuzzy Sets and Systems, 167, 101–119.Buela-Casal, G. (2003). Evaluating quality of articles and scientific journals. Proposal of weighted impact factor and a quality index. Psicothema, 15(1), 23–25.Dorta-Gonzalez, P., & Dorta-Gonzalez, M. I. (2013). Comparing journals from different fields of science and social science through a JCR subject categories normalized impact factor. Scientometrics, 95(2), 645–672.Dorta-Gonzalez, P., Dorta-Gonzalez, M. I., Santos-Penate, D. R., & Suarez-Vega, R. (2014). Journal topic citation potential and between-field comparisons: The topic normalized impact factor. Journal of Informetrics, 8(2), 406–418.Egghe, L., & Rousseau, R. (2002). A general frame-work for relative impact indicators. Canadian Journal of Information and Library Science, 27(1), 29–48.Gagolewski, M., & Mesiar, R. (2014). Monotone measures and universal integrals in a uniform framework for the scientific impact assessment problem. Information Sciences, 263, 166–174.Garfield, E. (2006). The history and meaning of the journal impact factor. JAMA, 295(1), 90–93.Habibzadeh, F., & Yadollahie, M. (2008). Journal weighted impact factor: A proposal. Journal of Informetrics, 2(2), 164–172.Klement, E., Mesiar, R., & Pap, E. (2010). A universal integral as common frame for Choquet and Sugeno integral. IEEE Transaction on Fuzzy System, 18, 178–187.Leydesdorff, L., & Opthof, T. (2010). Scopus’s source normalized impact per paper (SNIP) versus a journal impact factor based on fractional counting of citations. Journal of the American Society for Information Science and Technology, 61, 2365–2369.Li, Y. R., Radicchi, F., Castellano, C., & Ruiz-Castillo, J. (2013). Quantitative evaluation of alternative field normalization procedures. Journal of Informetrics, 7(3), 746–755.Moed, H. F. (2010). Measuring contextual citation impact of scientific journals. Journal of Informetrics, 4, 265–277.NISO. (2014). Alternative metrics initiative phase 1. White paper. http://www.niso.org/apps/group-public/download.php/13809/Altmetrics-project-phase1-white-paperOwlia, P., Vasei, M., Goliaei, B., & Nassiri, I. (2011). Normalized impact factor (NIF): An adjusted method for calculating the citation rate of biomedical journals. Journal of Biomedical Informatics, 44(2), 216–220.Pinski, G., & Narin, F. (1976). Citation influence for journal aggregates of scientific publications: Theory, with application to the literature of physics. Information Processing and Management, 12, 297–312.Pinto, A. C., & Andrade, J. B. (1999). Impact factor of scientific journals: What is the meaning of this parameter? Quimica Nova, 22, 448–453.Raghunathan, M. S., & Srinivas, V. (2001). Significance of impact factor with regard to mathematics journals. Current Science, 80(5), 605.Ruiz Castillo, J., & Waltman, L. (2015). Field-normalized citation impact indicators using algorithmically constructed classification systems of science. Journal of Informetrics, 9, 102–117.Saha, S., Saint, S., & Christakis, D. A. (2003). Impact factor: A valid measure of journal quality? Journal of the Medical Library Association, 91, 42–46.Torra, V., & Narukawa, Y. (2008). The h-index and the number of citations: Two fuzzy integrals. IEEE Transaction on Fuzzy System, 16, 795–797.Torres-Salinas, D., & Jimenez-Contreras, E. (2010). Introduction and comparative study of the new scientific journals citation indicators in journal citation reports and scopus. El Profesional de la Información, 19, 201–207.Waltman, L., & van Eck, N. J. (2008). Some comments on the journal weighted impact factor proposed by Habibzadeh and Yadollahie. Journal of Informetrics, 2(4), 369–372.Waltman, L., van Eck, N. J., van Leeuwen, T. N., & Visser, M. S. (2013). Some modifications to the SNIP journal impact indicator. Journal of Informetrics, 7, 272–285.Zitt, M. (2011). Behind citing-side normalization of citations: some properties of the journal impact factor. Scientometrics, 89, 329–344.Zitt, M., & Small, H. (2008). Modifying the journal impact factor by fractional citation weighting: The audience factor. Journal of the American Society for Information Science and Technology, 59, 1856–1860.Zyczkowski, K. (2010). Citation graph, weighted impact factors and performance indices. Scientometrics, 85(1), 301–315

El sistema de habilitación nacional: criterios y proceso de evaluación

En este artículo se realiza un detallado análisis del sistema actual de evaluación y selección del profesorado universitario en España. En concreto se analizan los criterios y el proceso de evaluación, la fiabilidad y validez y los concursos de acceso. En primer lugar, se pone de manifiesto que no hay criterios operativos de evaluación predeterminados, dado que estos siempre quedan en manos de los miembros de las comisiones de evaluación, y por ello, ocurre que los criterios varían de una comisión a otra, tal como se demuestra con varios ejemplos de casos reales. En segundo lugar, se demuestra que el proceso de evaluación, aunque está reglamentado en parte, es bastante ambiguo en relación a como aplicar los criterios, pero aun más Importante es el problema que se produce por el hecho de que las comisiones puedan actuar hasta con cuatro miembros, es decir, aun faltando tres, dado que se constituye inicialmente con siete; esto es especialmente relevante, pues siempre se mantiene el nivel de obtener al menos cuatro votos para superar una prueba. Lo anterior, junto con el problema de la definición clara de criterios genera en la práctica algunos problemas de fiabilidad y validez. No obstante, se concluye que estas deficiencias son fácilmente subsanables y que por tanto, el Sistema de Habilitación Nacional es, sin duda alguna, un sistema mejor que el de la anterior LRU y que genera mucho menos endogamia en la selección del profesorado funcionario universitarioThis paper presents a detailed analysis of the current system for the assessment and recruitment of university professors in Spain. Specifically, assessment criteria, assessment process, reliability, validity and competitive/entrance procedures are examined. First, it is shown that there are not predefined assessment criteria since these criteria are established by the members belonging to the assessment boards. This explains that assessment criteria change from some boards to other ones, as some true-life examples will show. Second, although the assessment process is partially regulated , it is quite ambiguous regard ing the way of putting in to effect the criteria. More important is the fact that assessment boards work even when they consists of four members, that is, even when three members are missing, since they would consist theoretically of seven members. These issues may generate some problems in reliability and validity. Nevertheless, these limitations can be easily rectified, therefore the national accreditation system is considered a good system since it promotes that the university positions are reached with proper and clear procedure