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Advanced Statistical Methods for the Analysis of Large Data-Sets pp 127–136Cite as

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

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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.

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Author information

Authors and Affiliations

  1. European University Institute, Fiesole, FI, Italy

    Filippo Belloc

  2. Dip. di Istituzioni Pubbliche, Economia e Società, Università di Roma Tre, Roma, Italy

    Antonello Maruotti

  3. Dip. di Metodi e Modelli per l’Economia il Territorio e la Finanza, Sapienza Università di Roma, Roma, Italy

    Lea Petrella

Authors
  1. Filippo Belloc
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  2. Antonello Maruotti
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  3. Lea Petrella
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Corresponding author

Correspondence to Antonello Maruotti .

Editor information

Editors and Affiliations

  1. , Dept. of Statistics, University of Roma "La Sapienza", P.le Aldo Moro 5, Roma, 00185, Italy

    Agostino Di Ciaccio

  2. , Dip. di Metodi Quantitativi e Teoria Eco, University "G. d'Annunzio" of Chieti-Pes, V.le Pindaro 42, Pescara, Italy

    Mauro Coli

  3. Fac. Ciencias, Depto. Estadística e Investigación Opera, Universidad de Granada, Avenida Fuente Nueva s/n, Granada, 18071, Spain

    Jose Miguel Angulo Ibanez

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Belloc, F., Maruotti, A., Petrella, L. (2012). A Correlated Random Effects Model for Longitudinal Data with Non-ignorable Drop-Out: An Application to University Student Performance. In: Di Ciaccio, A., Coli, M., Angulo Ibanez, J. (eds) Advanced Statistical Methods for the Analysis of Large Data-Sets. Studies in Theoretical and Applied Statistics(). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21037-2_12

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