Overview

MultiLevelOptimalBayes (MLOB) is designed for estimating two-level latent variable models, particularly in small sample settings. This is especially useful in psychology, education, and other fields with hierarchical or nested data structures. We present the R package MultiLevelOptimalBayes (MLOB) for estimating between-group effects in multilevel latent variable models. MLOB employs a regularised Bayesian estimator devised by Dashuk, Hecht, Lüdtke, Robitzsch, and Zitzmann (2024), which was subsequently enhanced for additional covariates by the same authors in 2025. This estimator chooses prior parameters to minimise the mean squared error (MSE) of the between-group effect by effectively balancing bias and variance. The regularised Bayesian estimator provides MSE-optimal estimations due to the mean-variance tradeoff, especially in scenarios of small sample sizes and poor intraclass correlation (ICC). The MLOB software supports imbalanced group sizes through integrated data-balancing methods and offers comprehensive inference, including point estimates, standard errors, p-values, and confidence intervals for both primary regressors and covariates. To gain comprehensive understanding, we initially examine the theoretical underpinnings of the regularised Bayesian estimator (Dashuk et al. 2024, 2025), followed by a discussion of its implementation in MLOB, namely the core function mlob(). We illustrate the application of mlob() using real datasets. Consequently, we provide researchers in psychology, education, and related disciplines a robust, user-friendly instrument for dependable multilevel latent variable estimation, particularly in contexts characterised by small sample sizes and low ICCs.

The core function mlob() estimates the between-group coefficient (beta_b) using a regularized Bayesian approach, and applies a data balancing procedure if the groups are unbalanced.

Function Usage

mlob(
  formula,
  data,
  group,
  balancing.limit = 0.2,
  conf.level = 0.95,
  jackknife = FALSE,
  punish.coeff = 2,
  ...
)

Arguments:

• formula: A formula (e.g., Y ~ X + C) where Y is the outcome, X is the context variable of interest, and C represents covariates.
• data: A data.frame containing all variables in the formula and the grouping variable.
• group: A string naming the grouping variable.
• balancing.limit: Proportion (0–1) of the dataset that can be removed to balance group sizes. Default is 0.2.
• conf.level: Confidence level for confidence intervals. Default is 0.95 (95% CI).
• jackknife: Logical. If TRUE, standard errors and CIs are computed via jackknife resampling. Default is FALSE.
• punish.coeff: Multiplier for penalizing removal of entire groups during balancing. Higher values discourage full-group deletions.

Balancing Procedure: The mlob() function also verifies whether the data is balanced, that is each group consist of exactly the same number of individuals. If the data is unbalanced, the balancing procedure comes into effect and identifies the optimal number of individuals and groups to delete based on the punishment coefficient. If the amount of data to be deleted is more than the threshold (balancing.limit), the regularized Bayesian estimator is not calculated and mlob() produces an error. This forces the user to increase the balancing limit manually and warns that the results should be interpreted with caution. # Examples

result_iris <- mlob(
  Sepal.Length ~ Sepal.Width + Petal.Length,
  data = iris,
  group = "Species",
  conf.level = 0.99,
  jackknife = FALSE
)

summary(result_iris)

result_chick <- mlob(
  weight ~ Diet,
  data = ChickWeight,
  group = "Time",
  punish.coeff = 1.5,
  jackknife = FALSE
)

print(result_chick)
summary(result_chick)

result_mtcars <- mlob(
  mpg ~ hp + wt + am + hp:wt + hp:am,
  data = mtcars,
  group = "cyl",
  balancing.limit = 0.35
)

summary(result_mtcars)

Output

The output is an object of class mlob_result, which contains:

• Estimated coefficients (beta_b and gamma values)
• Standard errors and confidence intervals
• Unregularized ML estimates for comparison
• Information about the data balancing procedure (if used)

Limitations

While MultiLevelOptimalBayes provides a robust solution for regularized estimation in two-level models, users should be aware of the following limitations:

• Local Grid Search:
  The optimization is restricted to a local 5σ region around the ML estimate. Although this region captures the true MSE minimum with high probability (≥ 0.999 for J = 5), it does not guarantee identification of the global minimum.

• Assumption of Equal Group Sizes:
  The estimator assumes equal group sizes to simplify the model. While averaging group sizes is a proposed solution, the method does not yet handle unbalanced group sizes natively, but find the optimal reduction to the balanced size.

• Jackknife Computational Cost:
  Jackknife resampling improves interval coverage in small samples. Note that it may be computationally demanding for larger samples.

• Limited Model Scope:
  Currently, MLOB only handles two-level models with continuous outcomes. Extensions to support generalized linear mixed models, three- and more-level structures, or multivariate outcomes are not yet available.

References

Dashuk, V., Hecht, M., Luedtke, O., Robitzsch, A., & Zitzmann, S. (2024).
An Optimally Regularized Estimator of Multilevel Latent Variable Models, with Improved MSE Performance.
https://doi.org/10.13140/RG.2.2.18148.39048

Luedtke, O., Marsh, H. W., Robitzsch, A., et al. (2008).
The multilevel latent covariate model: A new, more reliable approach to group-level effects in contextual studies.
https://doi.org/10.1037/a0012869

Authors

• Valerii Dashuk
• Binayak Timilsina
• Martin Hecht
• Steffen Zitzmann

Contact:
vadashuk@gmail.com binayak.timilsina001@gmail.com martin.hecht@hsu-hh.de steffen.zitzmann@medicalschool-hamburg.de