SSI, Inc. offers a technical support service via e-mail. This service is only available to registered end users of the SSI software products. Click here to view SSI’s policy and procedures for obtaining technical support.
This service applies to problems in using the software and does not include statistical consultation. If you need help with modeling concepts and interpretation, please consult our short list of relevant text books or post a question to Semnet: A structural equation modeling discussion network. If you require statistical consultation service, Vector Psychometric Group‘s Consulting Division would be pleased to be of assistance. Contact the VPG Sales Desk at firstname.lastname@example.org for more information.
A list of FAQs and additional technical documents are given below.
FAQs for LISREL 10
The FAQs for LISREL 10 overlap to a large extent with the FAQs for LISREL 8.80. In this regard, please note that the LSF of LISREL 10 is equivalent to the PSF of LISREL 8.80.
Can LISREL perform a multiple group analyses using a single data file?
In practice, many multivariate data sets are observations from several groups. Examples of these groups are genders, languages, political parties, countries, faculties, colleges, schools, etc.
LISREL may be used to fit multiple group structural equation models to multiple group data. Traditional statistical methods such as Maximum Likelihood (ML), Robust Maximum Likelihood (RML), Weighted Least Squares (WLS), Diagonally Weighted Least Squares (DWLS), Generalized Least Squares (GLS) and Un-weighted Least Squares (ULS) are available for complete multiple group data while the Full Information Maximum Likelihood (FIML) method is available for incomplete multiple group data.
In previous versions of LISREL, the user was required to create separate data files for each group. Suppose that the groups to be analyzed consisted of data collected in eight countries, the implication is that eight datasets must to be created in order to fit a multiple group structural equation model.
A new feature implemented in LISREL 10 allows researchers to use a single dataset that contains a group variable that can be defined by
- Using the Data Menu when a LISREL system file (.lsf) is opened
- By inserting the line
$GROUPS=<group variable name>anywhere in the syntax file. To use this dataset in a multiple group analysis, use the Data menu from the main menu bar and select the Group Variable… option (see below) Select COUNTRY from the list of variables and when done click the OK button. The LISREL Examples folder contains a sub-folder named MGROUPS that contains examples for the following statistical procedures:For a detailed example, see the Assessment of Invariance, Section 2 in the “Additional Topics Guide.pdf” .
Can one use the multilevel generalized linear (MGLIM) module to fit models for grouped- and discrete-time survival data?
Models for grouped-time survival data are useful for analysis of failure time data when subjects are measured repeatedly at fixed intervals in terms of the occurrence of some event, or when determination of the exact time of the event is only known within grouped intervals of time. Additionally, it is often the case that subjects are observed nested within clusters (i.e., schools, firms, clinics), or are repeatedly measured in terms of recurrent events. In this case, use of grouped-time models that assume independence of observations is problematic since observations from the same cluster or subject are usually correlated.
or data that are clustered and/or repeated, models including random effects provide a convenient way of accounting for association in correlated survival data.
Several authors have noted the relationship between ordinal regression models (using complementary log-log and logistic link functions) and survival analysis models for grouped and discrete time. In LISREL 10 a generalization of an ordinal random-effects regression model to handle correlated grouped-time survival data is implemented. This model accommodates multivariate normally-distributed random effects, and additionally, allows for a general form for model covariates.
Assuming a proportional or partial proportional, hazards or odds model, a maximum marginal likelihood solution is implemented using multi-dimensional quadrature to numerically integrate over the distribution of random-effects. The reference guide “Survival Models for grouped data.pdf” contains examples and references and is accessible via the online help menu.
Can one test the proportional odds versus non-proportional odds assumption for ordinal outcomes?
The term “ordinal” is applied to variables where the response measure of interest is measured in a series of ordered categories. Examples of such variables include Likert scales and psychiatric ratings of severity. Nominal and ordinal outcome models can be seen as generalizations of the binary outcome model. The ordinal model becomes important when the outcome variable is not dichotomous, or not truly continuous. If an ordinal outcome is analyzed within a continuous model, such a model can yield predicted values outside the range of the ordinal variable. As with binary data, some transformation or link function becomes necessary to prevent this from happening. The continuous model can also yield correlated residuals and regressors when applied to ordinal outcomes because the continuous model does not take the ceiling and floor effects of the ordinal outcome into account. This can then result in biased estimates of regression coefficients, and is most critical when the ordinal variable in question is highly skewed.
Extensive work on the development of methods for the analysis of ordinal response data has been undertaken by numerous researchers. These developments have focused on the extension of methods for dichotomous variables to ordinal response data, and have been mainly in terms of logistic and probit regression models. The proportional odds model is a common choice for the analysis of ordinal data. In LISREL 10, it is possible to fit both proportional and non-proportional odds models to verify the proportional odds assumption using a chi-square difference test. The reference guide “Models for proportional and non-proportional odds.pdf” contains examples and references and is accessible via the online help menu.
Can one use LISREL to fit full information maximum likelihood SEM models for a mixture of ordinal and continuous variables?
LISREL 10 supports Structural Equation Modeling for a mixture of ordinal and continuous variables for simple random samples and complex survey data.
The LISREL implementation allows for the use of design weights to fit SEM models to a mixture of continuous and ordinal manifest variables with or without missing values with optional specification of stratum and/or cluster variables. It also deals with the issue of robust standard error estimation and the adjustment of the chi-square goodness of fit statistic.
This method is based on adaptive quadrature and a user can specify any one of the following four link functions:
- Complementary Log-log
Examples to illustrate this feature are given in the folders \orfimlex and \ls9ex.
Can one fit three-level Multilevel Generalized Linear Models with LISREL 10?
Cluster or multi-stage samples designs are frequently used for populations with an inherent hierarchical structure. Ignoring the hierarchical structure of data has serious implications. The use of alternatives such as aggregation and disaggregation of information to another level can induce an increase in co-linearity among predictors and large or biased standard errors for the estimates.
The collection of models called Generalized Linear Models (GLIMs) have become important, and practical, statistical tools. The basic idea of GLIMs is an adaption of standard regression to quite different kinds of data. The variables may be dichotomous, ordinal (as with a 5-point Likert scale), counts (number of arrest records), or nominal. The motivation is to tailor the regression relationship connecting the outcome to relevant independent variables so that it is appropriate to the properties of the dependent variable. The statistical theory and methods for fitting Generalized Linear Models (GLIMs) to survey data was implemented in LISREL 8.8.
Researchers from the social and economic sciences are often applying these methods to multilevel data and consequently, inappropriate results are obtained. The LISREL statistical module for the analysis of multilevel data allows for design weights. Two estimation methods, MAP (maximization of the posterior distribution) and QUAD (adaptive quadrature) for fitting generalized linear models to multilevel data are available. The LISREL Multilevel Generalized Linear models module (MGLIM) allows for a wide variety of sampling distributions and link functions.
The LISREL 10 MGLIM module also include zero-inflated Poisson and zero-inflated Negative-Binomial models and prints results for unit-specific and population-average estimates of the fixed effects.
Examples in the folder \mglimex illustrate these features.
Can one specify more than three levels in the case of multilevel linear models for continuous outcome variables?
Social science research often entails the analysis of data with a hierarchical structure. A frequently cited example of multilevel data is a data set containing measurements on children nested within schools, with schools nested within education departments.
The need for statistical models that take account of the sampling scheme is well recognized and it has been shown that the analysis of survey data under the assumption of a simple random sampling scheme may give rise to misleading results.
Multilevel models are particularly useful in the modeling of data from complex surveys. Cluster or multi-stage samples designs are frequently used for populations with an inherent hierarchical structure. Ignoring the hierarchical structure of data has serious implications. The use of alternatives such as aggregation and disaggregation of information to another level can induce an increase in co-linearity among predictors and large or biased standard errors for the estimates. In order to address concerns regarding the appropriate analyses of survey data, the LISREL multilevel module for continuous data now also handles up to five levels and features an option for users to include design weights on levels 1, 2 , 3, 4 or 5 of the hierarchy.
Examples are given in the \mlevelex folder.
Do you have additional multilevel models material cont contained in the Multilevel Guide?
Four additional documents are available:
- Multilevel modeling
- Multilevel structural equation modeling
- Multilevel nonlinear modeling
- Multilevel modeling with weights
What are the default filename extensions used in LISREL 10?
All LISREL syntax files have extension .lis (since LISREL 9, previously .ls8), all PRELIS syntax files have extension .prl (since LISREL 9, previously .pr2). The LISREL spreadsheet has been renamed LISREL data system file and has extension .lsf (since LISREL 9, previously .psf).
To ensure backwards compatibility, users can still run previously created syntax files using a .psf file, but to open an existing .psf file using the graphical user’s interface, the user has to rename it to .lsf.
Can LISREL 10 be run in batch mode?
Any of the LISREL programs can be run into batch mode by using a .bat file with the following script:
"c:\program files (x86)\LISREL10\MLISREL64_10" <program name> <syntax file> <output file>
Program name = LISREL, PRELIS, MULTILEV, MAPGLIM or SURVEYGLIM
Syntax File = "c:\LISREL examples\ls9ex\npv1a.spl"
Output File = "c:\LISREL examples\ls9ex\npv1a.out"
Examples of batch files (RunLISREL.bat and RunSIMPLIS.bat) are given in the \ls9ex folder. These batch files will run all the LISREL and SIMPLIS syntax files in this folder.
Documents discussing various theoretical aspects are listed. All these are available as PDF files and may be accessed by clicking on the appropriate link.
Continuous variable data based on a complex survey
Continuous variable data based on a simple random sample
- Full Information Maximum Likelihood (FIML) for Data with Missing Values
- Multiple Imputation for Data with Missing Values
- Latent Variable Scores
- Standardized Coefficients
- Equal t-values
- The Interpretation of R2
- The Interpretation of R2 Revisited
- MINRES Exploratory Factor Analysis
- Scale Reliability
Complete ordinal variable data based on a simple random sample
- Structural Equation Modeling
Complete censored variable data based on a simple random sample
- Univariate Censored Regression