LISREL stands for linear structural relation. The methodology of LISREL was first developed by Karl Joreskog in 1970. LISREL is statistical software that is used for structural regression modeling. Structural equation models are the system of linear equations. LISREL is the simultaneous estimation of the structural model and measurement model. Structural model assumes that all variables are measured without error. Factor analysis is the technique that deals with the measurement model. Factor analysis is of two types: one is the exploratory factor analysis, (where the computer determines the underlining factor) and the second type of factor analysis is confirmatory factor analysis (where the researcher determines the factor structure). LISREL makes it possible to combine the structural equation and factor analysis. LISREL can also generate path diagrams for structural equations. LISREL 8.8 is the latest version available. LISREL is not only used for structural equation modeling, but it also has several other program applications. In LISREL, the PRELIS (Lisrel pre-processor) option is used for data manipulation and basic statistics. In LISREL, the SURVEYGLIM option is used for generalized linear modeling. For categorical response variables, formative interface modeling is used in LIRSEL. For continuous response variables, the COMFIRM option is used. For multivariate data, the MAPGLIM option is used for generalized linear modeling. In business, psychology and medical research, most researchers use LISREL for structural equation modeling. LISREL was the first software that was used for structural equation modeling. Competing software for LISREL include AMOS, SAS, and EQS, etc. However, LISREL has its own importance due to unique features.
Statistics Solutions is the country's leader in LISREL consulting and dissertation consulting. Contact Statistics Solutions today for a free 30-minute consultation.
The following are some basic features of LISREL:
Starting of LISREL: Select “LISREL” from the start menu or create a shortcut and start from the short cut.
Importing data in to LISREL: To enter data into LISREL, select “import options” from the file menu.
Opening a new window: In LISREL file, the “new “option is used to open a new window. From the new option we can open syntax, output, path diagram or data window as required.
Data manipulation: In the “data” option of LISREL, there are options like the variable properties, select variable, sort case, insert variable, delete variable, assign weight, etc.
Transform option: Like SPSS, LISREL also has an option to record or compute a new variable by using the “transform” option.
Statistics option: In LISREL, by using the statistics option, we can perform all the statistical models. LISREL can handle a number of models that include measurement models, no recursive models, hierarchical linear models, confirmatory factor analysis models, ordinal regression models, multiple group comparisons model, etc.
Graph option: Like many other statistical software, LISREL also has the option for graphs. By using the “graph” option in LISREL, we can produce high quality univariate, bivariate and multivariate charts.
Advance modeling: In LISREL, the multilevel option provides the flexibility to perform advance level modeling. By using the multilevel option, we can perform advance level linear and non-linear statistical methods.
View and Window option: Like any other statistical software, LISREL also has the view and window option. View option has the basic features like the tool bar, status bar, etc. By using the window option, we can arrange the window in a horizontal or vertical manner.
Advantages of LISREL:
1. This software provides the full information about the model coefficient which increases the power of the model.
2. It provides good treatment to the missing value.
3. It provides significance testing for all the coefficients.
4. It imposes restrictions on models if that is what is wanted.
Drawbacks of LISREL:
1. It is complicated to handle when someone is a novice.
2. The interaction effects are hard to handle.
3. Correlation matrix is used in SEM and it is assumed that these correlations are derived from the multivariate normality distribution. This assumption does not look valid.