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A general 3-step maximum likelihood approach to estimate the effects of multiple latent categorical variables on a distal outcome

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  • Zhu, Yajing
  • Steele, Fiona
  • Moustaki, Irini

Abstract

The 3-step approach has been recently advocated over the simultaneous 1-step approach to model a distal outcome predicted by a latent categorical variable. We generalize the 3-step approach to situations where the distal outcome is predicted by multiple and possibly associated latent categorical variables. Although the simultaneous 1-step approach has been criticized, simulation studies have found that the performance of the two approaches is similar in most situations (Bakk & Vermunt, 2016). This is consistent with our findings for a 2-LV extension when all model assumptions are satisfied. Results also indicate that under various degrees of violation of the normality and conditional independence assumption for the distal outcome and indicators, both approaches are subject to bias but the 3-step approach is less sensitive. The differences in estimates using the two approaches are illustrated in an analysis of the effects of various childhood socioeconomic circumstances on body mass index at age 50.

Suggested Citation

  • 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.
    • Handle: RePEc:ehl:lserod:81850
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    References listed on IDEAS

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    1. Bakk, Zsuzsa & Oberski, Daniel L. & Vermunt, Jeroen K., 2014. "Relating Latent Class Assignments to External Variables: Standard Errors for Correct Inference," Political Analysis, Cambridge University Press, vol. 22(4), pages 520-540.
    2. 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.
    3. Schoon, Ingrid & Sacker, Amanda & Bartley, Mel, 2003. "Socio-economic adversity and psychosocial adjustment: a developmental-contextual perspective," Social Science & Medicine, Elsevier, vol. 57(6), pages 1001-1015, September.
    4. 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.
    5. Tarani Chandola & Paul Clarke & J. N. Morris & David Blane, 2006. "Pathways between education and health: a causal modelling approach," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(2), pages 337-359, March.
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    Cited by:

    1. Zhu, Yajing & Steele, Fiona & Moustaki, Irini, 2020. "A multilevel structural equation model for the interrelationships between multiple latent dimensions of childhood socio‐economic circumstances, partnership transitions and mid‐life health," LSE Research Online Documents on Economics 103104, London School of Economics and Political Science, LSE Library.
    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. 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.
    4. Ruoxuan Li & Meilin Yao & Hongrui Liu & Yunxiang Chen, 2020. "Chinese Parental Involvement and Adolescent Learning Motivation and Subjective Well-Being: More is not Always Better," Journal of Happiness Studies, Springer, vol. 21(7), pages 2527-2555, October.

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    More about this item

    Keywords

    latent class analysis; multiple latent variables; robustness; 3-step approach;
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    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

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