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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:
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 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.
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HLM 6 Produktupdates
Es ist ein freies Upgrade für HLM6.0 und höher auf HLM
6.08 verfügbar. Sie können es hier
herunterladen.
Zum Download der Updates ist eine Anmeldung in unserem Shop
erforderlich!
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HLM 7 ist Windows 7 kompatibel (32/64 Bit)

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