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A General Multivariate Latent Growth Model With Applications to Student Achievement

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  • Silvia Bianconcini
  • Silvia Cagnone

Abstract

The evaluation of the formative process in the University system has been assuming an ever increasing importance in the European countries. Within this context, the analysis of student performance and capabilities plays a fundamental role. In this work, the authors propose a multivariate latent growth model for studying the performances of a cohort of students of the University of Bologna. The model proposed is innovative since it is composed by (a) multivariate growth models that allow the capture of different dynamics of student performance indicators over time and (b) a factor model that allows measurement of the general latent student capability. The flexibility of the model proposed allows its applications in several fields such as socioeconomic settings in which personal behaviors are studied using panel data.

Suggested Citation

  • Silvia Bianconcini & Silvia Cagnone, 2012. "A General Multivariate Latent Growth Model With Applications to Student Achievement," Journal of Educational and Behavioral Statistics, , vol. 37(2), pages 339-364, April.
    • Handle: RePEc:sae:jedbes:v:37:y:2012:i:2:p:339-364
    DOI: 10.3102/1076998610396886
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    References listed on IDEAS

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    1. Jeremy P. Smith & Robin A. Naylor, 2001. "Dropping out of university: A statistical analysis of the probability of withdrawal for UK university students," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 164(2), pages 389-405.
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    3. Silvia Bianconcini & Silvia Cagnone & Stefania Mignani & paola.monari@unibo.it, 2007. "A latent curve analysis of unobserved heterogeneity in university achievements," Statistica, Department of Statistics, University of Bologna, vol. 67(1), pages 55-67.
    4. David Draper & Mark Gittoes, 2004. "Statistical analysis of performance indicators in UK higher education," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 167(3), pages 449-474, August.
    5. Yoshio Takane & Jan Leeuw, 1987. "On the relationship between item response theory and factor analysis of discretized variables," Psychometrika, Springer;The Psychometric Society, vol. 52(3), pages 393-408, September.
    6. Jason Roy & Xihong Lin, 2000. "Latent Variable Models for Longitudinal Data with Multiple Continuous Outcomes," Biometrics, The International Biometric Society, vol. 56(4), pages 1047-1054, December.
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    Cited by:

    1. Chun Wang & Gongjun Xu & Xue Zhang, 2019. "Correction for Item Response Theory Latent Trait Measurement Error in Linear Mixed Effects Models," Psychometrika, Springer;The Psychometric Society, vol. 84(3), pages 673-700, September.
    2. Anna Simonetto & Emma Zavarrone, 2015. "A micro approach to cognitive skills’ growth in a university context," Quality & Quantity: International Journal of Methodology, Springer, vol. 49(3), pages 1013-1022, May.
    3. J. R. Lockwood & D. McCaffrey, 2020. "Using hidden information and performance level boundaries to study student–teacher assignments: implications for estimating teacher causal effects," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(4), pages 1333-1362, October.
    4. Page, Garritt L. & San Martin, Ernesto & Torres Irribarra, David & Van Bellegem, Sébastien, 2024. "Temporally Dynamic, Cohort-Varying Value-Added Models," LIDAM Discussion Papers CORE 2024009, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    5. J. R. Lockwood & Daniel F. McCaffrey, 2014. "Correcting for Test Score Measurement Error in ANCOVA Models for Estimating Treatment Effects," Journal of Educational and Behavioral Statistics, , vol. 39(1), pages 22-52, February.
    6. Garritt L. Page & Ernesto San Martín & David Torres Irribarra & Sébastien Van Bellegem, 2024. "Temporally Dynamic, Cohort-Varying Value-Added Models," Psychometrika, Springer;The Psychometric Society, vol. 89(3), pages 1074-1103, September.

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