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How to calculate CFA and SEM with lavaan?
The calculation of a CFA with lavaan in done in two steps: in the first step, a model defining the hypothesized factor structure has to be set up; in the second step this model is estimated using cfa (). This function takes as input the data as well as the model definition. Model definitions in lavaan all follow the same type of syntax.
How is scaling of a latent variable achieved?
For example, by default the scaling of the latent variable is achieved by fixing the loading of the first indicator (manifest variable) for a certain latent variable to the value of 1. =~ means that the latent variable (here named Factor1) to the left of the operator is defined by all variables to the right of it.
What does the lavinspect function do in lavaan?
The lavInspect () function allows extracting information from a lavaan object. The argument what specifies which information should be extracted. The value sampstat of this argument stands for “sample statistics”, the empirical variance-covariance matrix: The variance-covariance matrix implied by the model is obtained with what = ‘implied’:
Who is the creator of the lavaan package?
Lavaan is a free open source package for latent variable modeling in R. The lavaan package is developed and maintained by Yves Rosseel (Rosseel, 2012; see also http://lavaan.ugent.be ). The name lavaan refers to la tent va riable an alysis.
What should I know about factor analysis in lavaan?
We will understand concepts such as the factor analysis model, basic lavaan syntax, model parameters, identification and model fit statistics. These concepts are crucial to deciding how many items to use per factor, as well how to successfully fit a one-factor, two-factor and second-order factor analysis.
How to describe a model in lavaan syntax?
The figure below contains a graphical representation of the model that we want to fit. The corresponding lavaan syntax for specifying this model is as follows:
Is the RMSEA similar to the 4 factor model?
Overall, the pattern of loadings is quite heterogeneous. CFI = 0.958 and NNFI/TLI = 0.94 are very similar to the 4-factor model. The RMSEA = 0.053 is slightly lower than with the 4-factor model and not significantly higher than 0.05 (90 %-CI [0.027; 0.077]).