IDEAS home Printed from https://ideas.repec.org/a/spr/jclass/v36y2019i3d10.1007_s00357-018-9298-2.html
   My bibliography  Save this article

A Note on Applying the BCH Method Under Linear Equality and Inequality Constraints

Author

Listed:
  • L. Boeschoten

    (Tilburg University
    Statistics Netherlands)

  • M. A. Croon

    (Tilburg University)

  • D. L. Oberski

    (Utrecht University)

Abstract

Researchers often wish to relate estimated scores on latent variables to exogenous covariates not previously used in analyses. The BCH method corrects for asymptotic bias in estimates due to these scores’ uncertainty and has been shown to be relatively robust. When applying the BCH approach however, two problems arise. First, negative cell proportions can be obtained. Second, the approach cannot deal with situations where marginals need to be fixed to specific values, such as edit restrictions. The BCH approach can handle these problems when placed in a framework of quadratic loss functions and linear equality and inequality constraints. This research note gives the explicit form for equality constraints and demonstrates how solutions for inequality constraints may be obtained using numerical methods.

Suggested Citation

  • L. Boeschoten & M. A. Croon & D. L. Oberski, 2019. "A Note on Applying the BCH Method Under Linear Equality and Inequality Constraints," Journal of Classification, Springer;The Classification Society, vol. 36(3), pages 566-575, October.
    • Handle: RePEc:spr:jclass:v:36:y:2019:i:3:d:10.1007_s00357-018-9298-2
    DOI: 10.1007/s00357-018-9298-2
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00357-018-9298-2
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s00357-018-9298-2?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Vermunt, Jeroen K., 2010. "Latent Class Modeling with Covariates: Two Improved Three-Step Approaches," Political Analysis, Cambridge University Press, vol. 18(4), pages 450-469.
    2. Bolck, Annabel & Croon, Marcel & Hagenaars, Jacques, 2004. "Estimating Latent Structure Models with Categorical Variables: One-Step Versus Three-Step Estimators," Political Analysis, Cambridge University Press, vol. 12(1), pages 3-27, January.
    3. Boeschoten Laura & Oberski Daniel & de Waal Ton, 2017. "Estimating Classification Errors Under Edit Restrictions in Composite Survey-Register Data Using Multiple Imputation Latent Class Modelling (MILC)," Journal of Official Statistics, Sciendo, vol. 33(4), pages 921-962, December.
    Full references (including those not matched with items on IDEAS)

    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. Jennifer Oser & Marc Hooghe & Zsuzsa Bakk & Roberto Mari, 2023. "Changing citizenship norms among adolescents, 1999-2009-2016: A two-step latent class approach with measurement equivalence testing," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(5), pages 4915-4933, October.
    2. Lecegui, Antonio & Olaizola, Ana María & López-i-Gelats, Feliu & Varela, Elsa, 2022. "Implementing the livelihood resilience framework: An indicator-based model for assessing mountain pastoral farming systems," Agricultural Systems, Elsevier, vol. 199(C).
    3. Aely Park & Youngmi Kim & Jennifer Murphy, 2023. "Adverse Childhood Experiences and Substance Use Among Korean College Students: Different by Gender?," Child Indicators Research, Springer;The International Society of Child Indicators (ISCI), vol. 16(4), pages 1811-1825, August.
    4. Zsuzsa Bakk & Jouni Kuha, 2018. "Two-Step Estimation of Models Between Latent Classes and External Variables," Psychometrika, Springer;The Psychometric Society, vol. 83(4), pages 871-892, December.
    5. Bakk, Zsuzsa & Kuha, Jouni, 2020. "Relating latent class membership to external variables: an overview," LSE Research Online Documents on Economics 107564, London School of Economics and Political Science, LSE Library.
    6. Gugerty, Mary Kay & Mitchell, George E. & Santamarina, Francisco J., 2021. "Discourses of evaluation: Institutional logics and organizational practices among international development agencies," World Development, Elsevier, vol. 146(C).
    7. Paweł A. Atroszko & Bartosz Atroszko & Edyta Charzyńska, 2021. "Subpopulations of Addictive Behaviors in Different Sample Types and Their Relationships with Gender, Personality, and Well-Being: Latent Profile vs. Latent Class Analysis," IJERPH, MDPI, vol. 18(16), pages 1-29, August.
    8. Konte M., 2014. "Do remittances not promote growth? : a bias-adjusted three-step mixture-of-regressions," MERIT Working Papers 2014-075, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    9. F. J. Clouth & S. Pauws & F. Mols & J. K. Vermunt, 2022. "A new three-step method for using inverse propensity weighting with latent class analysis," 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. 16(2), pages 351-371, June.
    10. Aurélie Bertrand & Christian Hafner, 2014. "On heterogeneous latent class models with applications to the analysis of rating scores," Computational Statistics, Springer, vol. 29(1), pages 307-330, February.
    11. Lucy Prior, 2021. "Allostatic Load and Exposure Histories of Disadvantage," IJERPH, MDPI, vol. 18(14), pages 1-17, July.
    12. Bakk, Zsuzsa & Kuha, Jouni, 2018. "Two-step estimation of models between latent classes and external variables," LSE Research Online Documents on Economics 85161, London School of Economics and Political Science, LSE Library.
    13. Yajing Zhu & Fiona Steele & Irini Moustaki, 2020. "A multilevel structural equation model for the interrelationships between multiple latent dimensions of childhood socio‐economic circumstances, partnership transitions and mid‐life health," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(3), pages 1029-1050, June.
    14. Martin Lukac & Nadja Doerflinger & Valeria Pulignano, 2019. "Developing a Cross-National Comparative Framework for Studying Labour Market Segmentation: Measurement Equivalence with Latent Class Analysis," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 145(1), pages 233-255, August.
    15. Jason V. Chen & Kurt H. Gee & Jed J. Neilson, 2021. "Disclosure Prominence and the Quality of Non‐GAAP Earnings," Journal of Accounting Research, Wiley Blackwell, vol. 59(1), pages 163-213, March.
    16. Lekkas, Peter & Howard, Natasha J & Stankov, Ivana & daniel, mark & Paquet, Catherine, 2019. "A Longitudinal Typology of Neighbourhood-level Social Fragmentation: A Finite Mixture Model Approach," SocArXiv 56x9c, Center for Open Science.
    17. Zhu, Yajing & Steele, Fiona & Moustaki, Irini, 2017. "A general 3-step maximum likelihood approach to estimate the effects of multiple latent categorical variables on a distal outcome," LSE Research Online Documents on Economics 81850, London School of Economics and Political Science, LSE Library.
    18. Seohee Park & Seongeun Kim & Ji Hoon Ryoo, 2020. "Latent Class Regression Utilizing Fuzzy Clusterwise Generalized Structured Component Analysis," Mathematics, MDPI, vol. 8(11), pages 1-16, November.
    19. Brewsaugh, Katrina & Masyn, Katherine E. & Salloum, Alison, 2018. "Child welfare workers' sexism and beliefs about father involvement," Children and Youth Services Review, Elsevier, vol. 89(C), pages 132-144.
    20. Kim, Hyun Ju & Chung, Jae Young, 2020. "Factors affecting youth citizenship in accordance with socioeconomic background," Children and Youth Services Review, Elsevier, vol. 111(C).

    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:spr:jclass:v:36:y:2019:i:3:d:10.1007_s00357-018-9298-2. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.