Contents
Does SEM require normality?
A normal distribution assumes a skew and kurtosis of zero, but truly normal distributions are rare in practice. So “the SEM” doesn’t assume normality, but the widely-used normal-theory maximum likelihood (ML) estimator does.
How do you find the latent factor?
The measure of the degree to which an indicator is associated with a latent variable is the indicator’s loading on the latent variable. An inspection of the pattern of loadings and other statistics is used to identify latent variables and the observed variables that are associated with them.
What is a latent factor in statistics?
In statistics, latent variables (from Latin: present participle of lateo (“lie hidden”), as opposed to observable variables) are variables that are not directly observed but are rather inferred (through a mathematical model) from other variables that are observed (directly measured).
How is the latent variable indicated in a model?
Here, the latent variable is indicated by the circle and the single indicator variable x x is indicated by the square box, as are all observed variables. You’ll note a few curiosities compared to observed-variable models.
How is the direction of causality reversed in latent variable modeling?
First, the direction of causality is reversed from what you might expect: from the latent variables to the observed variable. This is because the indicator variable is an emergent manifestation of the underlying phenomenon represented by the latent variable. Second, there is an error δ δ associated with the indicator.
Having now described both exogenous and endogenous latent variables now allows us to fit a structural model, or one with directed paths between latent variables. This is in contrast to a measurement model, which focuses solely on relating indicators to latent variables.