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September 12, 2025Open Access

Estimating Context Effects in Small Samples while Controlling for Covariates: An Optimally Regularized Bayesian Estimator for Multilevel Latent Variable Models

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This method improves estimation accuracy for covariate parameters in latent models, suggesting better outcomes in small samples.

The regularized bayesian estimator achieves lower mean squared error in small samples, showing enhanced performance.
Simulation results indicate improved accuracy compared to maximum likelihood estimators, especially when sample sizes are low.
The proposed strategies aim to address limitations in standard errors and coverage rates while estimating covariates.
A step-by-step tutorial is provided to facilitate the application of the estimator using the MLOB package in R.

Dashuk et al. (2025) studied this question.

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