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Catching Up on Multilevel Modeling.

Lesa Hoffman1, Ryan W Walters2

  • 1Department of Psychological and Quantitative Foundations, University of Iowa, Iowa City, Iowa 52242, USA;

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Summary
This summary is machine-generated.

This review covers best practices for multilevel models in social sciences. It details handling clustered, longitudinal, and cross-classified data, plus predictor centering and novel location-scale models.

Keywords:
centeringhierarchical linear modelsmixed-effects location–scale modelsmixed-effects modelsrandom slopes

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Area of Science:

  • Psychology and Social Sciences
  • Statistical Modeling

Background:

  • Multilevel models are increasingly used in social sciences.
  • Best practices and controversies in model specification require clarification.
  • Understanding different data structures (clustered, longitudinal, cross-classified) is crucial.

Purpose of the Study:

  • To provide a comprehensive overview of current best practices in multilevel modeling.
  • To address key issues and controversies in specifying multilevel models.
  • To introduce novel extensions for analyzing variability.

Main Methods:

  • Review of common use cases for various multilevel designs.
  • Discussion of predictor centering techniques and their implications.
  • Introduction to mixed-effects location-scale models.

Main Results:

  • Detailed explanation of centering strategies for observed predictors.
  • Exploration of the relationship between centering and endogeneity.
  • Presentation of multivariate multilevel models and latent-centering.
  • Introduction to mixed-effects location-scale models for variability prediction.

Conclusions:

  • Effective specification of multilevel models requires careful consideration of design and predictor variables.
  • Centering strategies significantly impact the interpretability of effects and model assumptions.
  • Novel extensions like location-scale models offer advanced capabilities for analyzing complex data structures and variability.