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Two-step estimation of models between latent classes and external variables

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  • Bakk, Zsuzsa
  • Kuha, Jouni

Abstract

We consider models which combine latent class measurement models for categorical latent variables with structural regression models for the relationships between the latent classes and observed explanatory and response variables. We propose a two-step method of estimating such models. In its first step the measurement model is estimated alone, and in the second step the parameters of this measurement model are held fixed when the structural model is estimated. Simulation studies and applied examples suggest that the two-step method is an attractive alternative to existing one-step and three-step methods. We derive estimated standard errors for the two-step estimates of the structural model which account for the uncertainty from both steps of the estimation, and show how the method can be implemented in existing software for latent variable modelling

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  • 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.
    • Handle: RePEc:ehl:lserod:85161
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    References listed on IDEAS

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    Cited by:

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    2. 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.
    3. Roberto Mari & Antonello Maruotti, 2022. "A two-step estimator for generalized linear models for longitudinal data with time-varying measurement error," 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 273-300, June.
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    6. 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.
    7. Di Mari, Roberto & Bakk, Zsuzsa & Oser, Jennifer & Kuha, Jouni, 2023. "A two-step estimator for multilevel latent class analysis with covariates," LSE Research Online Documents on Economics 119994, London School of Economics and Political Science, LSE Library.
    8. Keefe Murphy & T. Brendan Murphy & Raffaella Piccarreta & I. Claire Gormley, 2021. "Clustering longitudinal life‐course sequences using mixtures of exponential‐distance models," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(4), pages 1414-1451, October.
    9. Giorgio Eduardo Montanari & Marco Doretti & Maria Francesca Marino, 2022. "Model-based two-way clustering of second-level units in ordinal multilevel latent Markov models," 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 457-485, June.
    10. John M. Abowd & William R. Bell & J. David Brown & Michael B. Hawes & Misty L. Heggeness & Andrew D. Keller & Vincent T. Mule Jr. & Joseph L. Schafer & Matthew Spence & Lawrence Warren & Moises Yi, 2020. "Determination of the 2020 U.S. Citizen Voting Age Population (CVAP) Using Administrative Records and Statistical Methodology Technical Report," Working Papers 20-33, Center for Economic Studies, U.S. Census Bureau.
    11. Kuha, Jouni & Zhang, Siliang & Steele, Fiona, 2023. "Latent variable models for multivariate dyadic data with zero inflation: analysis of intergenerational exchanges of family support," LSE Research Online Documents on Economics 116006, London School of Economics and Political Science, LSE Library.

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