Lisrel 8.8 is the pioneering software for structural equation modeling, now including statistical methods for complex survey data. During the last thirty 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.

Lisrel 8.8 - new features

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.

LISREL is no longer limited to SEM

Today, the LISREL software is no longer limited to SEM. It is a comprehensive data analysis application, which also includes statistical methods such as linear and nonlinear multilevel modeling (hierarchical linear modeling), generalized linear modeling, univariate and multivariate censored regression and formal inference-based recursive modeling. Many of these statistical methods are also available for the analysis of complex survey data. These and future developments in the software are intended to provide researchers with an analytic tool that is at the cutting edge of the statistical methodology for analyzing multivariate data.

Lisrel supplies the researcher in the social sciences with state-of-the-art tools for data analysis using mathematical modeling. Both the novice user and the experienced modeler will find the program highly accessible and useful. Data can be imported from more than 100 external sources, including all major statistical packages. Exploratory techniques include formal inference-based recursive (FIRM) modeling, factor and principal component analysis, as well as a variety of charting options.

LISREL handles the analysis of categorical variables, continuous variables, or mixtures of variables. It features modern missing data treatment with efficient Full Information Maximum Likelihood (FIML) or multiple imputation and has various options for handling non-normality in variables.