```
<- '
myModel # latent variables
ind60 =~ x1 + x2 + x3
dem60 =~ y1 + y2 + y3 + y4
dem65 =~ y5 + y6 + y7 + y8
# regressions
dem60 ~ ind60
dem65 ~ ind60 + dem60
# residual covariances
y1 ~~ y5
y2 ~~ y4 + y6
y3 ~~ y7
y4 ~~ y8
y6 ~~ y8
'
<- sem(model = myModel,
fit data = PoliticalDemocracy)
summary(fit)
```

# About lavaan

### News:

- (pinned): Exciting things are being planned for lavaan’s future development! Click here to read more, or use the donate button below to donate to our efforts:
- (25 March 2024): slides for my presentation at the joint quantitative brownbag (JQBB) speaker series can be found here; the corresponding R can found here.
- (1 March 2024): The SEMlab research group at the Department of Data Analysis (Ghent University) is recruiting 1 postdoctoral researcher for a period of three years. See the job ad for more information. The deadline to apply is May 1st, 2024. To interpret the salary scales, you need to know that the current index is 203,99%.
- (19 Dec 2023): lavaan version 0.6-17 has been released on CRAN. See Version History for more information.

### What is lavaan?

The lavaan package is developed to provide useRs, researchers and teachers a free open-source, but commercial-quality package for latent variable modeling. You can use lavaan to estimate a large variety of multivariate statistical models, including path analysis, confirmatory factor analysis, structural equation modeling and growth curve models.

The official reference to the lavaan package is the following paper:

Yves Rosseel (2012). lavaan: An R Package for Structural Equation Modeling. *Journal of Statistical Software*, 48(2), 1-36. URL http://www.jstatsoft.org/v48/i02/

### First impression

To get a first impression of how lavaan works in practice, consider the following example of a SEM model. The figure below contains a graphical representation of the model that we want to fit.

This is the corresponding lavaan model syntax: