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Strukturgleichungsmodelle bieten als multivariate
Forschungsmethode die Möglichkeit komplexer Datenanalysen. Sie
verbinden konfirmatorische Faktorenanalysen mit der linearen
Regression und erlauben so die Analyse latenter Strukturen.
Hypothetische Konstrukte werden in diesem Ansatz als latente
Variablen aufgefaßt. Sie werden mittels mehrerer Indikatoren
operationalisiert. So ist es möglich, die Meßfehler der einzelnen
Indikatoren zu bestimmen und die ?fehlerfreien? regressiven
Beziehungen zwischen den hypothetischen Konstrukten zu
analysieren, was irreführend auch als Kausalanalyse bezeichnet
wird.
Strukturgleichungsmodelle werden in lineare Gleichungssysteme
umgesetzt. Die unbekannten Parameter können unter bestimmten
Voraussetzungen aus den beobachteten Daten geschätzt werden.
Eine besondere Rolle spielt dabei die Maximum-Likelihood-Methode,
die gleichzeitig die globale Überprüfung eines
Strukturgleichungsmodells anhand eines c2-Wertes
ermöglicht, der sich aus dem Minimum der Fitfunktion ergibt. (aus
Lisrel
Einführung von Lars Satow).
Today, however, LISREL for Windows is no longer limited to SEM.
The latest LISREL for Windows includes the following statistical
applications.
- LISREL for structural equation modeling.
- PRELIS for data manipulations and basic statistical analyses.
- MULTILEV for hierarchical linear and non-linear modeling.
- SURVEYGLIM for generalized linear modeling.
- CATFIRM for formative inference-based recursive modeling for
categorical response variables.
- CONFIRM for formative inference-based recursive modeling for
continuous response variables.
- MAPGLIM for generalized linear modeling for multilevel data.
The PRELIS, LISREL and SIMPLIS manuals (as PDF) are included with
the LISREL program.
New features in LISREL 8.8 for Windows
- Structured latent curve models
The LISREL CO command has been extended to include the
exponential (EXP) and natural logarithm (LOG) operators as well
as parentheses. This allows LISREL users to fit, for example,
the structured latent curve models outlined in Browne (1993).
- Factor analysis of ordinal variables
Classical exploratory factor analysis assumes that the observed
variables are continuous. The PRELIS OFA command implements
exploratory factor analysis of ordinal variables as described in
Jöreskog & Moustaki (2006).
- Generalized linear models (GLIMs) for multilevel data
The new statistical application MAPGLIM fits generalized linear
models to multilevel data. Users can select from the
multinomial, Bernoulli, Poisson, binomial, negative binomial,
Normal, Gamma and inverse Gaussian sampling distributions. The
corresponding link functions include the log, cumulative logit,
cumulative probit, complementary log-log and logit link
functions.
- Observational residuals
Bollen and Arminger (1991) introduced observational residuals
for structural equation models. LISREL 8.8 for Windows allows
users to compute observational residuals along with latent
variable scores for the latent variables of the model. This
implementation is described and illustrated in Jöreskog, Sörbom
& Wallentin (2006)
- Writing parameter estimates, standard error estimates and
measures of fit to a PSF
The PV, SV and GF keywords on the LISREL OU command or the
SIMPLIS LISREL output command have been extended to allow users
to save the parameter estimates, standard error estimates and
measures of fit to a PSF. This is especially useful for Monte
Carlo studies.
- Changes to the graphical user interface (GUI)
The main window of LISREL 8.8 for Windows is now entitled LISREL
for Windows. The revised Export Data option on the File menu of
the main window allows users to export data to various data
formats such as SPSS, SAS, SYSTAT, Statistica, etc.
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