(19 July 2021): slides for my presentation (21 July) at the 9th European Congress of Methodology (Valencia, Spain) are available here.
(18 July 2021): a first draft of the paper: ‘Using bounded estimation to avoid nonconvergence in small sample structural equation modeling’ is now available on the OSF repository.
(27 June 2021): lavaan version 0.6-9 has been released on CRAN. See Version History for more information.
(16 June 2021): a first draft of the paper: ‘The Structural-After-Measurement (SAM) approach to SEM’ is now available on the OSF repository.
(7 June 2021): new (technical) paper describing the multilevel + fiml approach as used in lavaan 0.6-9 is published in Psych.
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/
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.
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)