137
Views
3
CrossRef citations to date
0
Altmetric
Original Articles

A fuzzy clustering approach to evaluate individual competencies from REFLEX data

Pages 2513-2533 | Received 20 Nov 2015, Accepted 19 Oct 2016, Published online: 17 Nov 2016

References

  • J. Allen and R. van der Veldon (eds.), The Flexible Professional in the Knowledge Society: New Challenges for Higher Education, High Educ. Dyn., 35, Springer, Dordrecht, 2011.
  • A.G. Aracil and D.P. Montero, Fuzzy cluster analysis on Spanish public universities, Investigaciones de Economía de la Educación 49, 2010, pp. 976–994.
  • L. Bai, J. Liang, C. Dang, and F. Cao, A novel fuzzy clustering algorithm with between-cluster information for categorical data, Fuzzy Sets Syst. 215 (2013), pp. 55–73. doi: 10.1016/j.fss.2012.06.005
  • D.J. Bartholomew, Latent Variable Models and Factor Analysis, Charles Griffin & Co. Ltd., London, 1987.
  • R.E. Bellman and L.A. Zadeh, Decision-making in fuzzy environment, Manage. Sci. 17 (1970), pp. B141–B164. doi: 10.1287/mnsc.17.4.B141
  • S. Benati and S. Stefani, The academic journal ranking problem: A fuzzy-clustering approach, J. Classification 28 (2008), pp. 7–20. doi: 10.1007/s00357-011-9072-1
  • L. Berkman, B. Singer, and K.G. Manton, Black/white differences in health status and mortality among the elderly, Demography 26 (1989), pp. 661–678. doi: 10.2307/2061264
  • J. C. Bezdek, Mathematical models for systematics and taxonomy, in Proceedings of the 8th International Conference on Numerical Taxonomy, G. Estabrook, ed., W. H. Freeman, S. Francisco, 1974, pp. 143–166.
  • J.C. Bezdek, Partition structures: A tutorial, in The Analysis of Fuzzy Information, Author ed., Vol. 3, Chapter 6, CRC Press, Boca Raton, FL, 1987, pp. 81–107.
  • J.C. Bezdek and J.D. Harris, Convex decomposition of fuzzy partitions, J. Math. Anal. Appl. 67 (1979), pp. 490–512. doi: 10.1016/0022-247X(79)90039-8
  • W.C. Borman, Format and training effects on rating accuracy and raters errors, J. Appl. Psychol. 64 (1979), pp. 410–421. doi: 10.1037/0021-9010.64.4.410
  • W. Buntine and A. Jakulin, Applying discrete PCA in data analysis, UAI '04 Proceedings of the 20th Conference on Uncertainty in Artificial Intelligence, 2004, pp. 59–66. Available at http://portal.acm.org/citation.cfm?id=1036851.
  • J. Calmand, J-F. Giret, C. Guégnard, and J-J. Paul, Why Grande Écoles are so valued? 2009. Available at http://www.decowe.org/static/uploaded/htmlarea/files/Why_Grande_Ecoles_are_so_valued.pdf.
  • G. Casella, An introduction to empirical Bayes data analysis, Amer. Stat. 39 (1985), pp. 83–87.
  • F. Cassidy, C.F. Pieper, and B.J. Carroll, Subtypes of mania determined by grade of membership analysis, Neuropsychopharmacology 25 (2001), pp. 373–383. doi: 10.1016/S0893-133X(01)00223-8
  • Cedefop, Skill mismatch identifying priorities for future research, Cedefop working paper No. 3, 2009. Available at http://www.cedefop.europa.eu/EN/Files/6103_en.pdf.
  • E.C. Cuervo and J.M.P. Andrade, Modeling abilities in 3-IRT models, Rev. Columbiana. Estad. 27 (2004), pp. 27–41.
  • G. Cumming and S. Finch, Inference by eye: Confidence intervals and how to read pictures of data., Am. Psychol. 60 (2005), pp. 170–180. doi: 10.1037/0003-066X.60.2.170
  • T. DeAngelis, Why we overestimate our competence, Am. Psychol. Assoc. 34 (2003), pp. 60. Available at http://www.apa.org/monitor/feb03/overestimate.aspx.
  • Decision System, Inc., User Documentation for DSIGoM, Version 1.0, 1999. Available at http://dsisoft.com/grade_of_membership.html.
  • P. D'Urso and R. Massari, Fuzzy clustering of human activity patterns, Fuzzy Sets Syst. 215 (2013), pp. 29–54. doi: 10.1016/j.fss.2012.05.009
  • E.A. Erosheva, Comparing latent structures of grade of membership, Rasch, and latent class models, Psychometrika 70 (2005), pp. 619–628. doi: 10.1007/s11336-001-0899-y
  • E. Erosheva, S. Fienberg, and C. Joutard, Describing disability through individual-level mixture models for multivariate binary data, Ann. Appl. Stat. 1 (2007), pp. 502–537. doi: 10.1214/07-AOAS126
  • S.E. Fienberg and A. Rinaldo, Three centuries of categorical data analysis: Log-linear models and maximum likelihood estimation, J. Statist. Plann. Inference 137 (2007), pp. 3430–3445. doi: 10.1016/j.jspi.2007.03.022
  • A. Flores-Sintas, J.M. Cadenas, and F. Martin, Membership functions in the fuzzy C-means algorithm, Fuzzy Sets Syst. 101 (1999), pp. 49–58. doi: 10.1016/S0165-0114(97)00062-6
  • P.A. Games and J.F. Howell, Pairwise multiple comparison procedures with unequal N's and/or variances: A Monte Carlo study, J. Educ. Study. 1 (1976), pp. 113–125.
  • I.C. Gormley and T.B. Murphy, A grade of membership model for rank data, Bayesian Anal. 4 (2009), pp. 265–295. doi: 10.1214/09-BA410
  • Gutierrez, R.G., StataCorp., A stata module for grade of membership (GoM) analysis, SBIR grant number 1R43AG025710, 2004. Available at https://www.sbir.gov/sbirsearch/detail/317523#.
  • S.J. Haberman, Book review on statistical applications using fuzzy sets, J. Amer. Statist. Assoc. 90 (1995), pp. 1131–1133. doi: 10.2307/2291362
  • D. Harris, Comparison of 1-, 2-, and 3-Parameter IRT Models, Instructional Topics in Educational Measurement, Published by National Council on Measurement in Education, Springer, 1989.
  • S.J. Heine, D.R. Lehman, H.R. Markus, and S. Kitayama, Is there a universal need for positive self-regard? Psychol. Rev. 106 (1999), pp. 766–794. doi: 10.1037/0033-295X.106.4.766
  • J.A. Jacquez and P. Greif, Numerical parameters identifiability and estimability: Integrating identifiability, estimability and optimal sampling design, Math. Biosci. 77 (1985), pp. 201–227. doi: 10.1016/0025-5564(85)90098-7
  • K. Kasumov, Metric properties of fuzzy partitions, Fuzzy Sets Syst. 81 (1996), pp. 365–378. doi: 10.1016/0165-0114(95)00203-0
  • A. Krishnamurthy, High-dimensional clustering with sparse Gaussian mixture models, 2011. Available at http://www.cs.cmu.edu/∼akshaykr/files/sgmm_paper.pdf
  • B. Li and D. Wang, Application of fuzzy cluster analysis for academic title evaluation, in Springer Computer and Information Science (CCIS), Proceedings 243, International Conference on Information and Computer Applications, Part I, C. Liu, J. Chang, and A. Yang, eds., Springer-Verlag, Berlin, 2011, pp.  221–226.
  • O. Luaces, J. Diez, A. Alonso-Betanzos, A. Troncoso, and A. Bahamonde, A factorization approach to evaluate open-response assignments in MOOCs using preference learning on peer assessment, Knowledge-Based Syst. 85 (2015), pp. 322–328. doi: 10.1016/j.knosys.2015.05.019
  • K.G. Manton, M.A. Woodbury, and H.D. Tolley, Statistical Applications Using Fuzzy Sets, John Wiley & Sons Inc., New York, 1994.
  • K.G. Manton and X. Gu, Disability declines and trends in medicare expenditures, Ageing Horiz. 2 (2005), pp. 25–34.
  • M.M. Marini, X. Li, and P.L. Fan, Characterizing latent factor analytic and grade of membership models, Sociol. Methods 26 (1996), pp. 133–164. doi: 10.2307/271021
  • S. McGuiness and P.J. Sloane, Labour market mismatch among UK graduates: An analysis using REFLEX data, Econ. Educ. Rev. 30 (2011), pp. 130–145. doi: 10.1016/j.econedurev.2010.07.006
  • G. McLachlan and D. Peel, Finite Mixture Models, John Wiley & Sons, Inc., New York, 2000.
  • P. McNamee, A comparison of grade of membership measure with alternative health indicators in explaining costs for older people, Health Econ. 13 (2004), pp. 379–395. doi: 10.1002/hec.833
  • E. Namey, G. Guest, L. Thairu, and L. Johnson, Data reduction techniques for large qualitative data sets, 2007. Available at http://www.stanford.edu/∼thairu/07_184.Guest.1sts.pdf.
  • S. Nascimento, Fuzzy Clustering via Proportional Membership Model, IOS Press, Amsterdam, 2005.
  • K. Petersson, Graduates from higher education in Europe, Stat. Swed, 2007. Available at http://www.fdewb.unimaas.nl/roa/reflex/documents%20public/publications/REFLEX_Sweden.pdf.
  • REFLEX Master Questionnaire. Available at http://www.fdewb.unimaas.nl/roa/reflex/index.htm.
  • REFLEX project. Available at http://www.fdewb.unimaas.nl/roa/reflex/.
  • REFLEX. Available at http://roa.sbe.maastrichtuniversity.nl/?page_id=3727.
  • E. Reis, A. Suleman, J.G. Dias, D. Nogueira, and C.M. Borges, A new complexity measure to classify ambulatory patients in rehabilitation facilities for financing purposes, Proceedings of the 59th ISI World Statistics Congress, Hong Kong, China, 25–30 August 2013, pp. 4497–4502.
  • E.H. Ruspini, A new approach to clustering, Inf. Control. 15 (1969), pp. 22–32. doi: 10.1016/S0019-9958(69)90591-9
  • N.B. Sahah, J. Bradley, S. Balakrishnan, A. Parekh, K. Ramchandran, and M. Wainwright, KDD workshop on data mining for educational assessment and feedback, 2014. Available at https://www.google.pt/?gfe_rd=cr&ei=-2pAV6K9JZLY8gfx_YPIAg#q=some+scallin+laws+for+mooc+assess.
  • N. Sánchez-Sánches and S. McGuinness, Decomposing the impacts of overeducation and overskilling on earnings and job satisfaction: An analysis using REFLEX data, Educ. Econ. 23 (2015), pp. 419–432. doi: 10.1080/09645292.2013.846297
  • D. Sathler, R. Monte-Mór, J.A.M. Carvalho, C.J. Machado, and A. Costa, Application of the grade of membership model (GoM) to delineate an urban hierarchy in Brazilian Amazonia, XXVI IUSSP International Population Conference, Marrakech 2009, 27 September – 2 October. Available at http://iussp2009.princeton.edu/download.aspx?submissionId=93326.
  • D.O. Sawyer, I.C. Leite, and R. Alexandrino, Perfis de Utilizaç ao de Serviços de Saúde no Brasil, Cienc & Saúde Coletiva 7 (2002), pp. 757–776. doi: 10.1590/S1413-81232002000400012
  • J. Serrano-Guerrero, F.P. Romero, and J.A. Olivas, Hiperion: A fuzzy approach for recommendation educational activities based on the acquisition of competences, Inf. Sci. 248 (2013), pp. 114–129. doi: 10.1016/j.ins.2013.06.009
  • B. Singer, Grade of membership representations: Concept and problems, on Anderson, in Probability, Statistics and Mathematics: Essays in Honor of Samuel Karlin, T.W. Athreya, K.B. and Iglechardt, D., eds., Academic Press, San Diego, 1989, pp. 317–334.
  • H. Suen, Peer assessment for massive open online courses (MOOCs), The Int. Rev. of Res. in open and Distrib. Learning, 15 (2014). Available at http://www.irrodl.org/index.php/irrodl/article/view/1680/2904.
  • A. Suleman, A convex semi-nonnegative matrix factorisation approach to fuzzy c-means clustering, Fuzzy Sets Syst. 270 (2015), pp. 90–110. doi: 10.1016/j.fss.2014.07.021
  • A. Suleman and F. Suleman, Ranking by competence using a Fuzzy approach, Qual. Quant. 46 (2012), pp. 323–339. doi: 10.1007/s11135-010-9357-1
  • B.G. Talbot and L.M. Talbot, Business radar: Opportunity analysis and metric estimation using a fuzzy grade of membership model, Technol Rev. J. (2003), pp. 87–112.
  • A.C. Tamhane, A comparison of procedures for multiple comparisons of means with unequal variances, J. Amer. Statist. Assoc. 74 (1979), pp. 471–480.
  • G.L. Thorpe and A. Favia, Data analysis using item response theory methodology: An introduction to selected programs and applications, Psychol. Faculty Scholarship, Paper 20, 2012.
  • R. van der Veldon, Acknowledgements, in The Flexible Professional in the Knowledge Society: New Challenges for Higher Education, J. Allen and R. van der Veldon, eds., High Educ. Dyn., 35. Springer, Dordrecht, 2011.
  • K.W. Wachter, Grade of membership models in low dimensions, Statist. Paper 40 (1999), pp. 439–457. doi: 10.1007/BF02934635
  • M.A. Woodbury and J. Clive, Clinical pure types as a fuzzy partition, J. Cybern. 4 (1974), pp. 111–121. doi: 10.1080/01969727408621685
  • M.A. Woodbury, J. Clive, and A. Garson, Jr, Mathematical typology: A grade of membership technique for obtaining disease definition, Comp. Biomed. Res. 11 (1978), pp. 277–298. doi: 10.1016/0010-4809(78)90012-5
  • H. Xiong, M. Steinbach, P.-N. Tan, and V. Kumar, HICAP: Hierarchical clustering with pattern preservation, Proceedings of the Fourth SIAM International Conference on Data Mining, 2004, pp. 279–290.
  • L.A. Zadeh, Fuzzy sets, Inf. Control 8 (1965), pp. 338–353. doi: 10.1016/S0019-9958(65)90241-X
  • M. Zarinbal, M.H.F. Zarandi, and I.B. Turksen, Relative entropy Fuzzy c-means clustering, Inf. Sci.260 (2014), pp. 74–97. doi: 10.1016/j.ins.2013.11.004

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.