IDEAS home Printed from https://ideas.repec.org/a/sae/jedbes/v37y2012i2p339-364.html
   My bibliography  Save this article

A General Multivariate Latent Growth Model With Applications to Student Achievement

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.3102/1076998610396886
    Download Restriction: no
    File URL: https://libkey.io/10.3102/1076998610396886?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    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.
    2. 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.
    3. 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.
    4. Dunson, David B., 2003. "Dynamic Latent Trait Models for Multidimensional Longitudinal Data," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 555-563, January.
    5. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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).
    6. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. F. Cugnata & G. Perucca & S. Salini, 2017. "Bayesian networks and the assessment of universities' value added," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(10), pages 1785-1806, July.
    2. Ziyue Liu & Anne R. Cappola & Leslie J. Crofford & Wensheng Guo, 2014. "Modeling Bivariate Longitudinal Hormone Profiles by Hierarchical State Space Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(505), pages 108-118, March.
    3. Cécile Proust & Hélène Jacqmin-Gadda & Jeremy M. G. Taylor & Julien Ganiayre & Daniel Commenges, 2006. "A Nonlinear Model with Latent Process for Cognitive Evolution Using Multivariate Longitudinal Data," Biometrics, The International Biometric Society, vol. 62(4), pages 1014-1024, December.
    4. David I. Ohlssen & Linda D. Sharples & David J. Spiegelhalter, 2007. "A hierarchical modelling framework for identifying unusual performance in health care providers," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(4), pages 865-890, October.
    5. Paul Hewson & Keming Yu, 2008. "Quantile regression for binary performance indicators," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 24(5), pages 401-418, September.
    6. Silvia Bianconcini, 2014. "Comments on: Latent Markov models: a review of a general framework for the analysis of longitudinal data with covariates," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(3), pages 466-468, September.
    7. Emilio Augusto Coelho-Barros & Jorge Alberto Achcar & Josmar Mazucheli, 2010. "Longitudinal Poisson modeling: an application for CD4 counting in HIV-infected patients," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(5), pages 865-880.
    8. Francesco Bartolucci & Ivonne Solis-Trapala, 2010. "Multidimensional Latent Markov Models in a Developmental Study of Inhibitory Control and Attentional Flexibility in Early Childhood," Psychometrika, Springer;The Psychometric Society, vol. 75(4), pages 725-743, December.
    9. Yang Yixin & Lü Xin & Ma Jian & Qiao Han, 2014. "A Robust Factor Analysis Model for Dichotomous Data," Journal of Systems Science and Information, De Gruyter, vol. 2(5), pages 437-450, October.
    10. Aina, Carmen & Baici, Eliana & Casalone, Giorgia & Pastore, Francesco, 2018. "The Economics of University Dropouts and Delayed Graduation: A Survey," IZA Discussion Papers 11421, Institute of Labor Economics (IZA).
    11. Vignoles Anna F & Powdthavee Nattavudh, 2009. "The Socioeconomic Gap in University Dropouts," The B.E. Journal of Economic Analysis & Policy, De Gruyter, vol. 9(1), pages 1-36, April.
    12. Zhang, Q. & Ip, E.H., 2014. "Variable assessment in latent class models," Computational Statistics & Data Analysis, Elsevier, vol. 77(C), pages 146-156.
    13. Chun Wang & Steven W. Nydick, 2020. "On Longitudinal Item Response Theory Models: A Didactic," Journal of Educational and Behavioral Statistics, , vol. 45(3), pages 339-368, June.
    14. Thum, Anna-Elisabeth, 2013. "Psychology in econometric models: conceptual and methodological foundations," MPRA Paper 52293, University Library of Munich, Germany.
    15. Michael Edwards, 2010. "A Markov Chain Monte Carlo Approach to Confirmatory Item Factor Analysis," Psychometrika, Springer;The Psychometric Society, vol. 75(3), pages 474-497, September.
    16. Royce Anders & William Batchelder, 2015. "Cultural Consensus Theory for the Ordinal Data Case," Psychometrika, Springer;The Psychometric Society, vol. 80(1), pages 151-181, March.
    17. Francesca Giambona & Mariano Porcu & Isabella Sulis, 2023. "Does education protect families' well-being in times of crisis? Measurement issues and empirical findings from IT-SILC data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(1), pages 299-328, March.
    18. Contini, Dalit & Salza, Guido & Scagni, Andrea, 2017. "Dropout and Time to Degree in Italian Universities Around the Economic Crisis," Department of Economics and Statistics Cognetti de Martiis. Working Papers 201716, University of Turin.
    19. Yikun Luo & Qipeng Chen & Jianyong Chen & Peida Zhan, 2024. "Development and validation of two shortened anxiety sensitive index-3 scales based on item response theory," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-7, December.
    20. Yang Liu, 2020. "A Riemannian Optimization Algorithm for Joint Maximum Likelihood Estimation of High-Dimensional Exploratory Item Factor Analysis," Psychometrika, Springer;The Psychometric Society, vol. 85(2), pages 439-468, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:jedbes:v:37:y:2012:i:2:p:339-364. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.