LISREL can be used to fit:

• measurement models,
• structural equation models based on continuous or ordinal data,
• multilevel models for continuous and categorical data using a number of link functions,
• generalized linear models based on complex survey data.

Additional statistical analyses than can be performed include, to name a few:

• exploratory factor analysis (EFA),
• multivariate analysis of variance (MANOVA),
• logistic and probit regression,
• censored regression,
• survival analysis.

To facilitate learning how to use LISREL or teaching with LISREL, an extensive collection of completely worked examples are available for download. Preview the example by clicking on each of the topics below, or download the entire example (PDF, data and syntax files) by clicking on the link in parentheses after the topic name. 

SEMS for complete continuous variable data:

• A measurement model    (complete example)
• A structural equation model  (complete example)
• A generalized linear model  (complete example)
• Non-normal data   (complete example)
• Practical applications   (complete example)

SEMS for incomplete continuous variable data:

• Multiple imputation and FIML (complete example)
• Assessment of invariance   (complete example)

SEMS and regression models for ordinal variable data

• A measurement model   (complete example)
• A structural equation model  (complete example)
• A generalized linear model   (complete example)
• CFA and MIMIC models   (complete example)
• Logistic and probit regression   (complete example) 

Multilevel data:

• A measurement model  (complete example)
• A structural equation model   (complete example)
• A generalized linear model   (complete example)
• Multilevel models for air traffic control data   (complete example)
• Multilevel CFA models   (complete example)
• 3-level analysis of health expenditure data   (complete example)
• 3-level saturated model for simulated data   (complete example)
• 4-level model for assessment data   (complete example)
• 3-level analysis of simulated data   (complete example)
• 2-level nonlinear regression model   (complete example)

Complex survey data with continuous normally distributed variables:

• A measurement model   (complete example)
• A structural equation model   (complete example)
• A generalized linear model   (complete example)
• A structural equation model for the 2001 monitoring the future data   (complete example)
• Implementation of sampling weights in a linear growth curve model   (complete example)
• Simulation study based on a linear growth curve model   (complete example)
• Latent curve analysis with main and interaction effects  (complete example)
• Replicate weights   (complete example)
• Confirmatory factor analysis model   (complete example)
• Confirmatory factor analysis model with latent variable relationship and latent variable means   (complete example)

Complex survey data with categorical, count, and continuous variables that are not normally distributed:

• GLIMS for count data using substance abuse data   (complete example)
• GLIMS for continuous responses   (complete example)
• GLIMS for binary responses   (complete example)
• GLIMS for ordinal responses using substance abuse data   (complete example)
• GLIMS for nominal responses using NHIS data   (complete example)

Multilevel Generalized Linear Modeling:

• Multilevel models for categorical and count data    (complete example)
• Binary model with logit link function   (complete example)
• Binary models with probit link function   (complete example)
• Bernoulli distribution with complementary log-log link function   (complete example)
• Models for count outcomes from the NESARC data   (complete example)
• Negative binomial model for the NESARC data   (complete example)
• Weighted 2-level models   (complete example)
• Models for count outcomes using ASPART data   (complete example)

Binary models with logit link function:

• Poisson log model with an offset variable   (complete example)
• Models for ordinal outcomes using NIMH data   (complete example)
• An ordinal regression model with random intercept   (complete example)
• Models for nominal outcomes using NHIS data    (complete example)
• Models for proportional and non-proportional odds   (complete example)

Survival analysis

• Binary case: a 2-level model    (complete example)
• The data for an ordinal approach   (complete example)
• Two-level survival analysis models   (complete example)