Modelling two types of heterogeneity in the analysis of student success

References

• H. Akaike, Statistical predictor identification, Ann. Inst. Stat. Math. 22 (1970), pp. 203–217. doi: 10.1007/BF02506337
   Web of Science ®Google Scholar
• S. Ameri, M.J. Fard, R.B. Chinnam, and C.K. Reddy, Survival analysis based framework for early prediction of student dropouts, Proceedings of the 25th ACM International on Conference on Information and Knowledge Management. ACM, 2016, pp. 903–912.
   Google Scholar
• P.K. Andersen, R.B. Geskus, T. de Witte, and H. Putter, Competing risks in epidemiology: Possibilities and pitfalls, Int. J. Epidemiol. 41 (2012), pp. 861–870. doi: 10.1093/ije/dyr213
   PubMed Web of Science ®Google Scholar
• F. Araque, C. Roldán, and A. Salguero, Factors influencing university drop out rates, Comput. Educ. 53 (2009), pp. 563–574. doi: 10.1016/j.compedu.2009.03.013
   Web of Science ®Google Scholar
• N. Atienza, J. Garcia-Heras, and J. Munoz-Pichardo, A new condition for identifiability of finite mixture distributions, Metrika 63 (2006), pp. 215–221. doi: 10.1007/s00184-005-0013-z
   Web of Science ®Google Scholar
• M. Berger, T. Welchowski, S. Schmitz-Valckenberg, and M. Schmid, A classification tree approach for the modeling of competing risks in discrete time, in Advances in Data Analysis and Classification, 2018. Available at https://doi.org/10.1007/s11634-018-0345-y.
   Google Scholar
• J. Berkson and R.P. Gage, Survival curve for cancer patients following treatment, J. Am. Stat. Assoc. 47 (1952), pp. 501–515. doi: 10.1080/01621459.1952.10501187
   Web of Science ®Google Scholar
• P.J. Bickel and K.A. Doksum, Mathematical Statistics: Basic Ideas and Selected Topics, Vol. I, CRC Press, Boca Raton, FL, 2015.
   Google Scholar
• J.W. Boag, Maximum likelihood estimates of the proportion of patients cured by cancer therapy, J. R. Statist. Soc. Ser. B (Meth.) 11 (1949), pp. 15–53.
   Google Scholar
• R. Clerici, A. Giraldo, and S. Meggiolaro, The determinants of academic outcomes in a competing risks approach: Evidence from Italy, Stud. High. Educ. 40 (2015), pp. 1535–1549. doi: 10.1080/03075079.2013.878835
   Web of Science ®Google Scholar
• G. Di Pietro and A. Cutillo, Degree flexibility and university drop-out: The italian experience, Econ. Educ. Rev. 27 (2008), pp. 546–555. doi: 10.1016/j.econedurev.2007.06.002
   Web of Science ®Google Scholar
• N.R. Draper and H. Smith, Applied Regression Analysis, John Wiley & Sons, New York, 2014.
   Google Scholar
• L. Hanin and L.S. Huang, Identifiability of cure models revisited, J. Multivar. Anal. 130 (2014), pp. 261–274. doi: 10.1016/j.jmva.2014.06.002
   Web of Science ®Google Scholar
• INEP, Censo da educação superior 2014, Tech. Rep., Instituto Nacional de Estudos e Pesquisas Educacionais Anísio Teixeira, Brasília, 2016.
   Google Scholar
• J.C. Juajibioy, Study of university dropout reason based on survival model, Open J. Stat. 6 (2016), pp. 908–916. doi: 10.4236/ojs.2016.65075
   Google Scholar
• E.L. Lehmann and G. Casella, Theory of Point Estimation, Springer Science & Business Media, Springer-Verlag, Ney York, 2006.
   Google Scholar
• W. Lehmann, “I just didn't feel like I fit in”: The role of habitus in university dropout decisions, Canad. J. High. Educ. 37 (2007), pp. 89–110.
   Google Scholar
• C.S. Li, J.M. Taylor, and J.P. Sy, Identifiability of cure models, Stat. Probab. Lett. 54 (2001), pp. 389–395. doi: 10.1016/S0167-7152(01)00105-5
   Web of Science ®Google Scholar
• P. Lima Júnior, F.L.d. Silveira, and F. Ostermann, Análise de sobrevivência aplicada ao estudo do fluxo escolar nos cursos de graduação em física: Um exemplo de uma universidade brasileira, Revista Brasileira de Ensino de Física 34 (2012), pp. 1–10.
   Google Scholar
• S. Meggiolaro, A. Giraldo, and R. Clerici, A multilevel competing risks model for analysis of university students' careers in italy, Stud. High. Educ. 42 (2017), pp. 1259–1274. doi: 10.1080/03075079.2015.1087995
   Web of Science ®Google Scholar
• P.A. Murtaugh, L.D. Burns, and J. Schuster, Predicting the retention of university students, Res. High. Educ. 40 (1999), pp. 355–371. doi: 10.1023/A:1018755201899
   Web of Science ®Google Scholar
• E.A. Ortis and C. Dehon, The roads to success: Analyzing dropout and degree completion at university, Tech. Rep., Working Papers ECARES 2011-025, ULB-Universite Libre de Bruxelles, 2011.
   Google Scholar
• R Core Team, R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria, 2013. Available at http://www.R-project.org/.
   Google Scholar
• M.A. Scott and B.B. Kennedy, Pitfalls in pathways: Some perspectives on competing risks event history analysis in education research, J. Educ. Behav. Stat. 30 (2005), pp. 413–442. doi: 10.3102/10769986030004413
   Web of Science ®Google Scholar
• R. Sittichai, Why are there dropouts among university students? Experiences in a Thai University, Int. J. Educ. Dev. 32 (2012), pp. 283–289. doi: 10.1016/j.ijedudev.2011.04.010
   Web of Science ®Google Scholar
• E.W. Stacy, A generalization of the gamma distribution, Ann. Math. Stat. 33 (1962), pp. 1187–1192. doi: 10.1214/aoms/1177704481
   Google Scholar
• G. Tontini and S.A. Walter, Pode-se identificar a propensão e reduzir a evasão de alunos? Ações estratégicas e resultados táticos para instituições de ensino superior, Avaliação: Revista da Avaliação da Educação Superior 19 (2014), pp. 89–110.
   Google Scholar
• C.A. Vallejos and M.F. Steel, Bayesian survival modelling of university outcomes, J. R. Statist. Soc. Ser. A (Stat. Soc.) 180 (2017), pp. 613–631. doi: 10.1111/rssa.12211
   Web of Science ®Google Scholar
• J.J. Vossensteyn, A. Kottmann, B.W. Jongbloed, F. Kaiser, L. Cremonini, B. Stensaker, E. Hovdhaugen, and S. Wollscheid, Dropout and completion in higher education in Europe, Tech. Rep., Publications Office of the European Union, European Union, Luxembourg, 2015.
   Google Scholar