To fit models in HLM, 8 statistical applications are used:
HLM2, which fits 2-level linear and nonlinear (HGLM) models
HLM3, which fits 3-level linear/nonlinear models
HLM4, which fits 4-level linear/nonlinear models
HMLM, which fits hierarchical multivariate 2-level linear models
HMLM2, which fits hierarchical multivariate 3-level models
HCM2, which fits 2-level crossed-and-nested models
HCM3, which fits 3-level crossed-and-nested models
HLMHCM, which fits linear models with crossed-and-nested random effects.
For specific examples for the different modules, please see below.
Two-level models based on the HSB data Download data and command files
Creating an MDM file and a command file for a 2-level model
Checking homogeneity of level-1 variance assumption
Modeling heterogeneous level-1 variance
Exploratory analysis of potential level-2 predictors
General linear hypothesis testing
Two-level spatial analysis model
download data and command files
Three-level models based on the EG data (download data and command files)
Creating an MDM file and a command file for an unconditional 3-level model
Linear growth model
Four-level models based on the literacy data (download data and command files)
Creating an MDM and command file for an unconditional four-level model for the literacy data
Conditional four-level model for the literacy data
HGLM models based on the Thai data (download data and command files)
Two-level Bernoulli model
Two-level binomial model
Two-level Poisson model (constant exposure)
Two-level Poisson model (variable exposure)
HGLM models based on the Teacher data (download data and command files)
Two-level multinomial model
Two-level ordinal model
HMLM models based on the NYS data (download data and command files)
HMLM model for the NYS data
HMLM model with log-linear model for level-1 variance
HMLM model with first-order autoregressive model for level-1 variance
Latent variable analysis using HMLM
HMLM2 model based on the EG data (download data and command files)
HCM2 models based on the Scotland data (download data and command files)
Unconditional model
Conditional model
Comparison of models
HCM3 model for the Growth data (download data and command files)
HLMHCM models for the Growth data (download data and command files)
Unconditional linear growth model
Level-2 and a row-factor prediction model
Graphing (download data and command files)
Data based graphs:
Box-and-whisker plots, which can be used to display univariate distributions of level-1 variables for each level-2 unit, with and without a level-2 classification variable.
Line plots, where, for example, level-1 repeated measures observations are joined by lines to describe changes or developments over time during the course of the research study.
Scatter plots, which can be used to explore bivariate relationships between level-1 variables for individual or a group of level-2 units, with and without controlling level-2 variables.
Model based graphs
Model-based graphs
Plots for individual level-2 units using the level-1 equation instead of the entire model (Level-1 equation modeling)
Level-1 residual box-and-whisker plots
Level-1 residual vs predicted value plots
Level-2 EB/OLS coefficient confidence intervals
Three-level graphing
FIRC models (download data and command files)
A 2-level FIRC model
A 3-level FIRC model
Augmented imputation models (download data and command files)
An annotated example of HLM2 analysis of incomplete data