index.qmd

---
title: "About lavaan"
format: html
---

### News:

- (pinned): Exciting things are being planned for lavaan’s future development!
Click [here](https://lavaan.ugent.be/about/donate.html) to read more, or
use the donate button below to donate to our efforts:
<form action="https://actieplatform.ugent.be/en-GB/p/lavaan-project/step-1" method="get" target="_blank"><button class="btn btn-outline-warning" type="submit">DONATE for lavaan</button></form>

- (26 September 2024): lavaan version 0.6-19 has been released on
[CRAN](https://cran.r-project.org/web/packages/lavaan/). See [Version
History](http://lavaan.ugent.be/history/dot6.html) for more information.
- (1 Aug 2024): the lavaan discussion group is back online.
- (15 July 2024): the lavaan discussion group was taken offline (by google) 
after a spam attack.
- (7 June 2024): lavaan version 0.6-18 has been released on
[CRAN](https://cran.r-project.org/web/packages/lavaan/). See [Version
History](http://lavaan.ugent.be/history/dot6.html) 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.

::: callout-tip
## 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.
![](/figures/sem.png){fig-alt="lavaan example" width="70%"}

This is the corresponding lavaan model syntax:
```{r, eval=FALSE}
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
'
fit <- sem(model = myModel, 
           data = PoliticalDemocracy) 
summary(fit)
```