On the Interpretation of Parameters in Multivariate Multilevel Models Across Different Combinations of Model...

Area of Science:

• Multivariate statistical modeling
• Quantitative psychology
• Educational statistics

Background:

• Multivariate multilevel models (multilevel structural equation models) are increasingly accessible.
• Model interpretation challenges arise from predictor centering and latent variable construction.
• Recent Mplus software changes affect parameter estimation with Bayesian versus maximum likelihood methods.

Purpose of the Study:

• To clarify how parameter interpretation in multilevel models varies with centering decisions.
• To explain the impact of predictor form (observed vs. latent) on model parameters.
• To detail how estimation methods and software syntax influence model results.

Main Methods:

• Explication of multilevel model parameter differences based on centering.
• Use of simulated data to demonstrate concepts in multivariate models with latent predictors.
• Analysis of Mplus software (Version 8.1) syntax and estimation method effects.

Main Results:

• Centering choices for lower-level predictors significantly alter higher-level effects.
• Latent lower-level predictors in multivariate models are subject to similar centering complexities.
• Bayesian estimation in Mplus 8.1 yields different lower-level predictors with random slopes compared to maximum likelihood.

Conclusions:

• Accurate interpretation of multilevel models requires careful consideration of centering and estimation choices.
• Understanding these nuances is crucial for researchers using complex multilevel modeling techniques.
• Availability of data and code facilitates practical application and learning.