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.