segunda-feira, 11 de junho de 2012

LISREL for structural equation models / Aplicação LISREL para modelos de equações estruturais - Structural equation modeling (SEM)

Maximum Likelihood (ML), Robust Maximum Likelihood (RML), Generalized Least Squares (GLS), Un-weighted Least Squares (ULS), Weighted Least Squares (WLS), Diagonally Weighted Least Squares (DWLS) and Full Information Maximum Likelihood (FIML) methods to fit structural equation models to data (...)

In practice, the variables of interest are often latent (unobservable) variables, such as intelligence, job satisfaction, organizational commitment, socio-economic status, ambition, alienation, verbal ability, etc.  These latent variables are modeled by specifying a measurement model and a structural model.  The measurement model specifies the relationships between the observed indicators and the latent variables while the structural model specifies the relationships amongst the latent variables.  However, it is also possible and often desired to include observed variables as part of the structural model. 

"Structural equation modeling (SEM). SEM allows researchers in the social sciences, management sciences, behavioral sciences, biological sciences, educational sciences and other fields to empirically assess their theories. These theories are usually formulated as theoretical models for observed and latent (unobservable) variables. If data are collected for the observed variables of the theoretical model, the LISREL program can be used to fit the model to the data.
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. "