How to use the lavaan package for CFA?
Note that in this default approach, the latent factors are permitted to covary and the model estimates this covariance. One R syntax note….. the format here to call the cfa function ( lavaan::cfa (….. )) is employed to ensure no ambiguity that the correct cfa function is the one from the lavaan package.
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.
When to use the normalized residual in lavaan?
One problem with Hausman’s (1978) approach to computing the residual variance is that sometimes the variance estimates given by [the above equations] can be negative. In that case the standardized residual is not computed and Mplus prints 999. Typically in such situation the normalized residual can be used. Similarly, lavaan returns NA.
When do I look at the residuals of CFA?
I am implementing CFA at the moment and looking at the residuals of model to determine how to improve the model fit When i take the raw scores from the model, i get residuals for everything When i take a look at the standardized/z-score residuals, I can see NA between some of the questions which have large residuals in the raw score
Why is the model syntax so short in lavaan?
The reason why this model syntax is so short, is that behind the scenes, the cfa () function will take care of several things. First, by default, the factor loading of the first indicator of a latent variable is fixed to 1, thereby fixing the scale of the latent variable. Second, residual variances are added automatically.
What is the header of the lavaan project?
The header contains the following information: a section called Model Test User Model: which provides a test statistic, degrees of freedom, and a p-value for the model that was specified by the user. The next section contains additional fit measures, and is only shown because we use the optional argument fit.measures = TRUE.