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HLM stands for "Hierarchical Linear Modeling" and describes statistical methods for the analysis of hierarchically structured data. Sociological and psychological studies are often based on nested data structures. Each of the nested levels is represented by a separate model. Easy to handle, HLM enables you to create quickly and easily nested models. HLM allows for multilevel models to analyze simultaneously hierarchically structured data.
Why you should use HLM:
- Applicable to measured, counted as well as ordinally and nominally scaled variables
- Modeling of a functional relationship between the expected value, the target variable and a linear combination of explanatory variables
- Longitudinal analyzes and repeated measuring experiments
HLM - Hierarchical Linear and Nonlinear Modeling
Behavioral and social data commonly have a nested structure. For example, if repeated observations are collected on a set of individuals and the measurement occasions are not identical for all persons, the multiple observations are properly conceived as nested within persons. Each person might also be nested within some organizational unit such as a school or workplace. These organizational units may in turn be nested within a geographical location such as a community, state, or country. Within the hierarchical linear model, each of the levels in the data structure (e.g., repeated observations within persons, persons within communities, communities within states) is formally represented by its own sub-model. Each sub-model represents the structural relations occurring at that level and the residual variability at that level.
|Operating System||Windows 95, 98, 2000, NT 4.0, XP, Vista, 7, 8, 10|
|Min. CPU||486 Processor or better|
|Min. RAM||32 MB RAM|
|Disk Space||20 MB|
For Information about running HLM on Linux feel free to contact us directly!
HLM 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 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:
- Four-level nested models:
- Four-level nested models for cross-sectional data (for example, models for item response within students within classrooms within schools).
- Four-level models for longitudinal data (for example items within time points within persons within neighborhoods).
- Four-way cross-classified and nested mixtures:
- Repeated measures on students who are moving across teachers within schools over time, or item responses nested within immigrants who are cross-classified by country of origin and country of destination.
- Repeated measures on persons who are simultaneously living in a given neighborhood and attending a given school.
- Hierarchical models with dependent random effects:
- Spatially dependent neighborhood effects.
- Social network interactions.
HLM 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.
Further features include:
HLM 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.