Degree course change and student performance: a mixed-effect model approach

References

• G. Adelfio and G. Boscaino, The student talent in a random effect quantile regression model for university performance, in Proceedings of the 28th International Workshop on Statistical Modeling, V.M.R. Muggeo, V. Capursi, G. Boscaino, and G. Lovison, eds., Vol. 2, 2013, pp. 479–483. ISBN 978-88-96251-49-2.
   Google Scholar
• G. Adelfio, G. Boscaino, and V. Capursi, A new indicator for higher education student performance, Higher Educ. 68(5) (2014), pp. 653–668. doi: 10.1007/s10734-014-9737-x
   Google Scholar
• M. Attanasio, G. Boscaino, V. Capursi, and A. Plaia, May the students' career performance helpful in predicting an increase in universities income? in Statistical Models for Data Analysis. Series in Studies in Classification, Data Analysis, and Knowledge Organization, P. Giudici, S. Ingrassia, and M. Vichi, eds., Springer International Publishing, Switzerland, 2013. Available at http://link.springer.com/book/10.1007%2F978-3-319-00032-9.
   Google Scholar
• E.R. Birch and P.W. Miller, Student outcomes at university in Australia: A quantile regression approach. Aust. Econ. Pap. 45, 2006, pp. 1–17, 10.1111/j.1467-8454.2006.00274.x.
   Google Scholar
• G. Boscaino, V. Capursi, and F. Giambona, The careers' performance of a University students' cohort. DSSM Working paper, n. 2007.1, 2007.
   Google Scholar
• J.S. Cheesman, N. Simpson, and G. Wint, Determinants of student performance at university: Reflections from the Caribbean, 2006. Available at http://www.mona.uwi.edu/opair/research/student-performance-paperrevised.pdf.
   Google Scholar
• CNVSU, XI Rapporto sullo stato del sistema universitario, 2012. Available at http://www.cnvsu.it/_library/downloadfile.asp?id=11778.
   Google Scholar
• A. Gelman, J.B. Carlin, H.S. Stern, and D.B. Rubin, Bayesian Data Analysis: Texts in Statistical Science, 2nd ed., CRC Press, Boca Raton, FL, 2004. ISBN 1-58488-388-X.
   Google Scholar
• M. Gesmann and D. de Castillo, Interface between R and the Google Chart Tools. Package Version 0.4.3, 2013.
   Google Scholar
• L. Grilli, C. Rampichini, and R. Varriale, Predicting students academic performance: A challenging issue in statistical modelling, in Cladag 2013 Book of abstracts, Monerva, Morlini and Palumbo, eds., CLEUP, Padova, 2013, pp. 249–254.
   Google Scholar
• J.D. Hadfield, MCMC methods for multi-response generalized linear mixed models: The MCMCglmm R Package. J. Stat. Softw. 33(2) (2010), pp. 1–22. Available at http://www.jstatsoft.org/v33/i02/.
   Google Scholar
• D.B. Hall, Zero-inflated Poisson and binomial regression with random effects: A case study, Biometrics 56 (2000), pp. 1030–1039. doi: 10.1111/j.0006-341X.2000.01030.x
   PubMed Web of Science ®Google Scholar
• W.K. Hastings, Monte Carlo sampling methods using Markov chains and their applications, Biometrika 57 (1970), pp. 97–109. doi: 10.1093/biomet/57.1.97
   Web of Science ®Google Scholar
• P. Horn, A. Jansen, and D Yu, Factors explaining the academic success of second-year economics students: An exploratory analysis, South African Journal of Economics 79(2) (2011). doi: 10.1111/j.1813-6982.2011.01268.x
   Web of Science ®Google Scholar
• P.M. Kuhnert, T.G. Martin, K. Mengersen, and H.P. Possingham, Assessing the impacts of grazing levels on bird density in woodland habitat: A Bayesian approach using expert opinion, Environmetrics 16(7) (2005), pp. 717–747. doi: 10.1002/env.732
   Web of Science ®Google Scholar
• D. Lambert, Zero-inflated Poisson regression, with an application to defects in manufacturing, Technometrics 34(1) (1992), pp. 1–14. doi: 10.2307/1269547
   Web of Science ®Google Scholar
• N. Metropolis, A.W. Rosenbluth, M.N. Rosenbluth, A.H. Teller, and E. Teller, Equation of state calculations by fast computing machines, J. Chem. Phys. 21 (1953), pp. 1087–1092. doi: 10.1063/1.1699114
   Web of Science ®Google Scholar
• Y. Min and A. Agresti, Random effect models for repeated measures of zero-inflated count data. Statist. Model. 5, 1–19, 2005.
   Google Scholar
• MIUR, Osservatorio studenti-didattica, 2014, Available at http://anagrafe.miur.it/php5/cerca-laureati.php.
   Google Scholar
• V.M.R. Muggeo and G. Adelfio, Efficient change point detection for genomic sequences of continuous measurements, Bioinformatics 27(2011), pp. 161–166. ISSN 1367-4803. doi: 10.1093/bioinformatics/btq647
   PubMed Web of Science ®Google Scholar
• R Core Team, R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, 2013, http://www.R-project.org/.
   Google Scholar
• M. Rodrigues-Motta, D. Gianola, and B. Heringstad, A mixed effects model for overdispersed zero inflated Poisson data with an application in animal breeding, J. Data Sci. 8 (2010), pp. 379–396.
   Google Scholar
• C. Tattersall, W. Waterink, P. Höppener, and R. Koper, A case study in the measurement of educational efficiency in open and distance learning, Distance Educ. 27 (2006), pp. 391–404. doi: 10.1080/01587910600940463
   Google Scholar
• C.A.C. Van Bragt, A.W.E.A. Bakx, T.C.M. Bergen, and M.A. Croon, Looking for students' personal characteristics predicting study outcome, Higher Educ. 61 (2011), pp. 59–75. doi: 10.1007/s10734-010-9325-7
   Google Scholar
• Q.H. Vuong, Likelihood ratio tests for model selection and non-nested hypotheses, Econometrica 57(2) (1989), pp. 307–333. doi: 10.2307/1912557
   Web of Science ®Google Scholar
• K. Wang, K.K.W. Yau, and A.H. Lee, A zero-inflated Poisson mixed model to analyze diagnosis related groups with majority of same-day hospital stays, Comput. Methods Programs Biomed. 68 (2002), pp. 195–203. doi: 10.1016/S0169-2607(01)00171-7
   PubMed Web of Science ®Google Scholar
• A.H. Welsh, R.B. Cunningham, C.F. Donnelly, and D.B. Lindenmayer, Modelling the abundance of rare species: Statistical models for counts with extra zeros, Ecol. Model. 88(1) (1996), pp. 297–308. doi: 10.1016/0304-3800(95)00113-1
   Web of Science ®Google Scholar