LISREL for Windows

LISREL for Windows - A brief overview

During the last thirty eight years, the LISREL model, methods and software have become synonymous with 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.

MAPGLIM for generalized linear modeling for multilevel data.

L  LISREL

The 32-bit application LISREL is intended for:

Standard structural equation modeling

Multilevel structural equation modeling

These methods are available for the following data types:

Complete and incomplete complex survey data on categorical and continuous variables

Complete and incomplete simple random sample data on categorical and continuous variables

PRELIS

PRELIS is a 32-bit application which can be used for:

Data manipulation

Data transformation

Data generatiion

Computing moment matrices

Computing asymptotic covariance matrices of sample moments

Imputation by matching

Multiple imputation

Multiple linear regression

Logistic regression

Univariate and multivariate censored regression

ML and MINRES exploratory factor analysis

M  MULTILEV

MULTILEV fits multilevel linear and nonlinear models to multilevel data from simple random and complex survey designs. It allows for models with continuous and categorical response variables.

 

S  SURVEYGLIM

SURVEYGLIM fits Generalized LInear Models (GLIMs) to data from simple random and complex survey designs.

Models for the following sampling distributions are available.

 

Multinomial

Bernoulli

Binomial

Negative Binomial

Poisson

Normal

Gamma

Inverse Gaussian