Sozialwissenschaftliche oder psychologische Daten haben häufig eine verschachtelte (nested) Struktur. Werden z.B. wiederholt Beobachtungen an einer bestimmten Gruppe von Individuen gesammelt. So sind die Bedingungen nicht für alle Personen identisch. Die korrekte Bezeichung eines derartigen Beobachtungsmodells wäre "geschachtelt innerhalb der Person". Jede Person könnte wiedrum verschachtelt innerhalb einer Organiation (z.B. Schule oder Arbeibeitsplatz) sein. Diese Organisation könnte weiterhin in einer geographischen Einheit (z.B. Kommune, Land, Staat) verschachtelt sein. Hierarische lineare Modelle repräsentieren jedes Niveau der Datenstruktur durch ein formal eigenständiges Untermodell. Jedes Untermodell repräsentiert die strukturellen Beziehungen auf genau einem Niveau - einschliesslich der Residualvarainz auf diesem Niveau.

New features in Version 6 include:

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 generalized 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.

What's new in Version 7.0?

HLM 7 offers unprecedented flexibility in modeling multilevel and longitudinal data. With the same full array of graphical procedures and residual files along with the speed of computation, robustness of convergence, and user-friendly interface of HLM 6, HLM 7 highlights include three new procedures that handle binary, count, ordinal and multinomial (nominal) response variables as well as continuous response variables for normal-theory hierarchical linear models:

HLM 7 also offers new flexibility in estimating hierarchical generalized linear models through the use of Adaptive Gauss-Hermite Quadrature (AGH) and high-order Laplace approximations to maximum likelihood. The AGH approach has been shown to work very well when cluster sizes are small and variance components are large. the high-order Laplace approach requires somewhat larger cluster sizes but allows an arbitrarily large number of random effects (important when cluster sizes are large)

New HTML output that supplies elegant notation for statistical models including visually attractive tables is also now available, allowing the user to cut and paste output of interest into manuscripts.