HLM 6.0
Introduction
HLM handles both hierarchical linear and nonlinear models. All models can be easily formulated using the step-by-step process which guides you through the specification of the model at the respective levels.
HLM 6 greatly broadens the range of hierarchical models that can be estimated. It also offers greater convenience of use than previous versions. Here is a quick overview of key new features and options:
- All new graphical displays of data: group-specific scatter plots, line plots, and cubic splines that can be color coded by values of predictor variables; box-plots displayed for overall data and data grouped within higher-level units.
- Greater expanded graphics for fitted models: graphing of group-specific equations, box-plots of level-1 residuals for each group, plots of residuals by predicted values for each group, posterior credibility intervals for random coefficients. For three-level models, level-1 trajectories are displayed in separate graphs or grouped by level-3 units. Graphs can be color coded by values of predictor variables.
- Model equations displayed in hierarchical or mixed-model format with or without subscripts - easy to save for publication. Distribution assumptions and link functions are presented in detail.
- Cross-classified random effects models for linear models and non-linear link functions with convenient Windows interface.
- High-order Laplace approximation with EM algorithm for stable convergence and accurate estimation in two-level hierarchical generalised linear models (HGLM).
- Multinomial and ordinal models for three-level data.
- New flexible and accurate sample design weighting for two- and three-level HLMs and HGLMs.
- Easier automated input from a wide variety of software packages, including the current versions of SAS, SPSS, and STATA.
- Residual files can be saved directly as SPSS (*.sav) or STATA (*.dta) files.
- Analyses are based on MDM files, replacing the older less flexible SSM format.