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Latent Ignorability and Item Selection for Nursing Home Case-Mix Evaluation

Author

Listed:
  • Francesco Bartolucci

    (University of Perugia)

  • Giorgio E. Montanari

    (University of Perugia)

  • Silvia Pandolfi

    (University of Perugia
    University of Perugia)

Abstract

In the social, behavioral, and health sciences it is often of interest to identify latent or unobserved groups in the population with the group membership of the individuals depending on a set of observed variables. In particular, we focus on the field of nursing home assessment in which the response variables typically come from the administration of questionnaires made of categorical items. These types of data may suffer from missing values and the use of lengthy questionnaires may be problematic as a large number of items could have a negative impact on the responses. In such a context, we introduce an extended version of the Latent Class (LC) model aimed at dealing with missing values, by assuming a form of latent ignorability. Moreover, we propose an item selection algorithm, based on the LC model, for finding the smallest subset of items providing an amount of information close to that of the initial set. The proposed approach is illustrated through an application to a dataset collected within an Italian project on the quality-of-life of nursing home patients.

Suggested Citation

  • Francesco Bartolucci & Giorgio E. Montanari & Silvia Pandolfi, 2018. "Latent Ignorability and Item Selection for Nursing Home Case-Mix Evaluation," Journal of Classification, Springer;The Classification Society, vol. 35(1), pages 172-193, April.
    • Handle: RePEc:spr:jclass:v:35:y:2018:i:1:d:10.1007_s00357-017-9227-9
    DOI: 10.1007/s00357-017-9227-9
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    References listed on IDEAS

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    1. 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.
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    5. Formann, Anton K., 2007. "Mixture analysis of multivariate categorical data with covariates and missing entries," Computational Statistics & Data Analysis, Elsevier, vol. 51(11), pages 5236-5246, July.
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    8. Francesco Bartolucci & Giorgio E. Montanari & Silvia Pandolfi, 2016. "Item selection by latent class-based methods: an application to nursing home evaluation," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 10(2), pages 245-262, June.
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    Cited by:

    1. Robitzsch, Alexander, 2023. "About Still Nonignorable Consequences of (Partially) Ignoring Missing Item Responses in Large-scale Assessment," OSF Preprints hmy45_v1, Center for Open Science.
    2. Simone Del Sarto & Michela Gnaldi, 2022. "Spare time use: profiles of Italian Millennials (beyond the media hype)," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(5), pages 1403-1428, December.
    3. Robitzsch, Alexander, 2020. "About Still Nonignorable Consequences of (Partially) Ignoring Missing Item Responses in Large-scale Assessment," OSF Preprints hmy45, Center for Open Science.

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