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Maximum Likelihood Estimation of Multilevel Structural Equation Models with Random Slopes for Latent Covariates

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  • Nicholas J. Rockwood

    (Loma Linda University)

Abstract

A maximum likelihood estimation routine for two-level structural equation models with random slopes for latent covariates is presented. Because the likelihood function does not typically have a closed-form solution, numerical integration over the random effects is required. The routine relies upon a method proposed by du Toit and Cudeck (Psychometrika 74(1):65–82, 2009) for reformulating the likelihood function so that an often large subset of the random effects can be integrated analytically, reducing the computational burden of high-dimensional numerical integration. The method is demonstrated and assessed using a small-scale simulation study and an empirical example.

Suggested Citation

  • Nicholas J. Rockwood, 2020. "Maximum Likelihood Estimation of Multilevel Structural Equation Models with Random Slopes for Latent Covariates," Psychometrika, Springer;The Psychometric Society, vol. 85(2), pages 275-300, June.
    • Handle: RePEc:spr:psycho:v:85:y:2020:i:2:d:10.1007_s11336-020-09702-9
    DOI: 10.1007/s11336-020-09702-9
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    References listed on IDEAS

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    2. Nicholas J. Rockwood, 2021. "Efficient Likelihood Estimation of Generalized Structural Equation Models with a Mix of Normal and Nonnormal Responses," Psychometrika, Springer;The Psychometric Society, vol. 86(2), pages 642-667, June.

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