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Item selection by an extended Latent Class model: An application to nursing homes evaluation


  • Bartolucci, Francesco
  • Giorgio E., Montanari
  • Pandolfi, Silvia


The evaluation of nursing homes and the assessment of the quality of the health care provided to their patients are usually based on the administration of questionnaires made of a large number of polytomous items. In applications involving data collected by questionnaires of this type, the Latent Class (LC) model represents a useful tool for classifying subjects in homogenous groups. In this paper, we propose an algorithm for item selection, which is based on the LC model. The proposed algorithm is aimed at finding the smallest subset of items which provides an amount of information close to that of the initial set. The method sequentially eliminates the items that do not significantly change the classification of the subjects in the sample with respect to the classification based on the full set of items. The LC model, and then the item selection algorithm, may be also used with missing responses that are dealt with assuming a form of latent ignorability. The potentialities of the proposed approach are illustrated through an application to a nursing home dataset collected within the ULISSE project, which concerns the quality-of-life of elderly patients hosted in Italian nursing homes. The dataset presents several issues, such as missing responses and a very large number of items included in the questionnaire.

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  • Bartolucci, Francesco & Giorgio E., Montanari & Pandolfi, Silvia, 2012. "Item selection by an extended Latent Class model: An application to nursing homes evaluation," MPRA Paper 38757, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:38757

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    References listed on IDEAS

    1. Hans-Peter Kohler & Francesco C. Billari & José Antonio Ortega, 2002. "The Emergence of Lowest-Low Fertility in Europe During the 1990s," Population and Development Review, The Population Council, Inc., vol. 28(4), pages 641-680.
    2. Gilles Celeux & Gilda Soromenho, 1996. "An entropy criterion for assessing the number of clusters in a mixture model," Journal of Classification, Springer;The Classification Society, vol. 13(2), pages 195-212, September.
    3. Beth A Reboussin & Michael E Miller & Kurt K Lohman & Thomas R Ten Have, 2002. "Latent class models for longitudinal studies of the elderly with data missing at random," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 51(1), pages 69-90.
    4. France Portrait & Maarten Lindeboom & Dorly Deeg, 1999. "Health and mortality of the elderly: the grade of membership method, classification and determination," Health Economics, John Wiley & Sons, Ltd., vol. 8(5), pages 441-458.
    5. Galasso, Vincenzo & Profeta, Paola, 2007. "How does ageing affect the welfare state?," European Journal of Political Economy, Elsevier, vol. 23(2), pages 554-563, June.
    6. Erling Andersen, 1977. "Sufficient statistics and latent trait models," Psychometrika, Springer;The Psychometric Society, vol. 42(1), pages 69-81, March.
    7. Fraley C. & Raftery A.E., 2002. "Model-Based Clustering, Discriminant Analysis, and Density Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 611-631, June.
    8. C. O'Muircheartaigh & I. Moustaki, 1999. "Symmetric pattern models: a latent variable approach to item non-response in attitude scales," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 162(2), pages 177-194.
    9. Nema Dean & Adrian Raftery, 2010. "Latent class analysis variable selection," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 62(1), pages 11-35, February.
    10. Biernacki, Christophe & Celeux, Gilles & Govaert, Gerard, 2003. "Choosing starting values for the EM algorithm for getting the highest likelihood in multivariate Gaussian mixture models," Computational Statistics & Data Analysis, Elsevier, vol. 41(3-4), pages 561-575, January.
    11. Paul McNamee, 2004. "A comparison of the grade of membership measure with alternative health indicators in explaining costs for older people," Health Economics, John Wiley & Sons, Ltd., vol. 13(4), pages 379-395.
    12. Friedrich Breyer & Joan Costa-Font & Stefan Felder, 2010. "Ageing, health, and health care," Oxford Review of Economic Policy, Oxford University Press, vol. 26(4), pages 674-690, Winter.
    13. Karlis, Dimitris & Xekalaki, Evdokia, 2003. "Choosing initial values for the EM algorithm for finite mixtures," Computational Statistics & Data Analysis, Elsevier, vol. 41(3-4), pages 577-590, January.
    14. Bengt Muthén & David Kaplan & Michael Hollis, 1987. "On structural equation modeling with data that are not missing completely at random," Psychometrika, Springer;The Psychometric Society, vol. 52(3), pages 431-462, September.
    15. Ofer Harel & Joseph L. Schafer, 2009. "Partial and latent ignorability in missing-data problems," Biometrika, Biometrika Trust, vol. 96(1), pages 37-50.
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    Cited by:

    1. Pieroni, Luca & d'Agostino, Giorgio & Bartolucci, Francesco, 2013. "Identifying corruption through latent class models: evidence from transition economies," MPRA Paper 43981, University Library of Munich, Germany.

    More about this item


    Expectation-Maximization algorithm; Polytomous items; Quality-of-life; ULISSE project;

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • I11 - Health, Education, and Welfare - - Health - - - Analysis of Health Care Markets
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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