A Correlated Random Effects Model for Longitudinal Data with Non-ignorable Drop-Out: An Application to University Student Performance

Conference paper
Part of the Studies in Theoretical and Applied Statistics book series (STAS)

Abstract

Empirical study of university student performance is often complicated by missing data, due to student drop-out of the university. If drop-out is non-ignorable, i.e. it depends on either unobserved values or an underlying response process, it may be a pervasive problem. In this paper, we tackle the relation between the primary response (student performance) and the missing data mechanism (drop-out) with a suitable random effects model, jointly modeling the two processes. We then use data from the individual records of the faculty of Statistics at Sapienza University of Rome in order to perform the empirical analysis.

References

  1. Aitkin, M. (1999). A General Maximum Likelihood Analysis of Variance Components in Generalized Linear Models. Biometrics, 55:117–128.MathSciNetMATHCrossRefGoogle Scholar
  2. Alfò, M. and Maruotti, A. (2009). A selection model for longitudinal binary responses subject to non-ignorable attrition. Statistics in Medicine, 28: 2435–2450.MathSciNetCrossRefGoogle Scholar
  3. Alfò, M. and Trovato, G. (2004). Semiparametric mixture models for multivariate count data, with application. Econometrics Journal, 2:426–454.CrossRefGoogle Scholar
  4. Belloc, F., Maruotti, A. and Petrella, L. (2010). University drop-out: An Italian experience. Higher Education, 60: 127–138.CrossRefGoogle Scholar
  5. Belloc, F., Maruotti, A. and Petrella, L. (2011). How Individual Characteristics Affect University Students Drop-out: a Semiparametric Mixed-Effects Model for an Italian Case Study. Journal of Applied Statistics, 38(10): 2225–2239.MathSciNetCrossRefGoogle Scholar
  6. Devadoss, S. and Foltz, J. (1996). Evaluation of Factors Influencing Student Class Attendance and Performance, American Journal of Agricultural Economics, 78:499–507.CrossRefGoogle Scholar
  7. Elmore, P.B. and Vasu, E.S. (1980). Relationship between Selected Variables and Statistics Achievement: Building a Theoretical Model, Journal of Educational Psychology, 72:457–467.CrossRefGoogle Scholar
  8. Gao, S. (2004). A shared random effect parameter approach for longitudinal dementia data with non-ignorable missing data. Statistics in Medicine, 23:211–219.CrossRefGoogle Scholar
  9. Little, R.J.A. (2008). Selection and Pattern-Mixture Models, in Advances in Longitudinal Data Analysis, G. Fitzmaurice, M. Davidian, G. Verbeke and G. Nolenberghs (eds.), London: CRC Press.Google Scholar
  10. Little, R.J.A. and Rubin, D.B. (2002). Statistical Analysis with Missing Data, 2nd edition, New York: Wiley.MATHGoogle Scholar
  11. Rubin, D.B. (2000). The Utility of Counterfactuals for Causal Inference - Discussion of Causal Inference Without Counterfactuals by A. P. Dawid, Journal of the American Statistical Association, 95:435438.Google Scholar
  12. Schram, C.M. (1996). A Meta-Analysis of Gender Differences in Applied Statistics Achievement, Journal of Educational and Behavioral Statistics, 21:55–70.Google Scholar
  13. Verzilli, C.J. and Carpenter, J.R. (2002). A Monte Carlo EM algorithm for random coefficiente-based dropout models. Journal of Applied Statistics, 29:1011–1021MathSciNetMATHCrossRefGoogle Scholar
  14. Yuan, Y. and Little, R.J.A. (2009). Mixed-Effect Hybrid Models for Longitudinal Data with Non-ignorable Dropout, Biometrics, 65:478–486.MathSciNetMATHCrossRefGoogle Scholar
  15. Winkelmann, R. (2000). Seemingly unrelated negative binomial regression. Oxford Bullettin of Economics and Statistics, 62:553–560.CrossRefGoogle Scholar
  16. Zimmer, J. and Fuller, D. (1996). Factors Affecting Undergraduate Performance in Statistics: A Review of the Literature, paper presented at the Annual Meeting of the Mid-South Educational Research Association, Tuscalosa (AL), November.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Filippo Belloc
    • 1
  • Antonello Maruotti
    • 2
  • Lea Petrella
    • 3
  1. 1.European University InstituteFiesoleItaly
  2. 2.Dip. di Istituzioni Pubbliche, Economia e SocietàUniversità di Roma TreRomaItaly
  3. 3.Dip. di Metodi e Modelli per l’Economia il Territorio e la FinanzaSapienza Università di RomaRomaItaly

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