Multilevel models are a subclass of hierarchical Bayesian models, which are general models with multiple levels of random variables and arbitrary relationships among the different variables. Multilevel analysis has been extended to include multilevel structural equation modeling, multilevel latent class modeling, and other more general models.

Mplus is especially useful for (a) going beyond what most other MLM software can do (e.g., MSEM, multilevel mixture models, combining different variable types) and (b) power analysis. An introduction to Mplus is included at the end of the slides for those interested. The references at the end are much more complete. Here are the highlights:

Multilevel models recognise the existence of such data hierarchies by allowing for residual components at each level in the hierarchy. For example, a two-level model which allows for grouping of child outcomes within schools would include residuals at the child and school level.

2025年1月22日 · Multilevel modeling (MLM), also known as hierarchical or mixed-effects modeling, is a statistical technique designed to analyze data with nested or hierarchical structures.

2024年11月4日 · Multilevel modeling (also known as hierarchical linear modeling or mixed-effects modeling) analyzes data with a hierarchical or nested structure. This technique accounts for data points grouped or clustered within multiple levels, such as individuals within schools, patients within hospitals, or repeated measures within subjects.

2020年2月29日 · We use multilevel modelling when we are analysing data that are drawn from a number of different levels and when our outcome is measured at the lowest level.

Recognize when response variables and covariates have been collected at multiple (nested) levels. Apply exploratory data analysis techniques to multilevel data. Write out a multilevel statistical model, including assumptions about variance components, in both by-level and composite forms.

Mixed models (aka random effects models or multilevel models) are an attractive option for working with clustered data, and should be considered alongside alternatives such as generalized estimating equations.

2022年4月28日 · Multilevel modelling is a technique for dealing with grouped or clustered data for machine learning problems. A multilevel model in ML can be used to simulate the parameters that change at more than one level.

2022年11月16日 · Watch Multilevel models for survey data in Stata. Background: What does multilevel mean? In multilevel data, observations—subjects, for want of a better term—can be divided into groups that have something in common: