Not quite. Remember the diagrams of a CFA. The arrows go from the latents to the items not the other way around. Hence, correlation between the items is not the issue here but the correlation between the latents.
Factor loadings in a CFA with multiple correlated latent factors are akin to regression estimates not correlations, so having a loading over 1 is possible. Whether or not it is a concern is hard to say without more context.
So your aim should be to apply your underlying theory of measurement to your data. Creating a latent variable only makes sense if your theory implies that your four variables of interest are indicators of a latent factor. So I think the best thing for you to do is provide more detail on what you...
Here is a worked example using the R dataset iris:
# This will get the marginal means
#This is for ploting
m1 <- lm(Sepal.Length ~ Sepal.Width + Species, iris)
# Get the marginal means. By default will be evaluated at the mean for...
I have a repeated measures dataset with baseline and 12 and 24 month follow-up. I need to fit a mediation model to this data. I am unsure whether my approach (see below) makes sense. The questions I have are:
1. If the model has Poisson link function for the outcome is the traditional a*b...
TL;DR; I am looking for workflow ideas to dynamically import tables and figures into a google doc that I am working on collaboratively with multiple people.
I find myself working a lot with colleagues on google docs who are not sufficiently familiar with R or markdown to use .Rmd files. I am...
I have a binary variable y which I am aiming to model using instrumental variable regression. Both X and the IV are continuous. ivpak in R does not seem to provide a means for accounting for a binary outcome. Is there a means of accounting for this in R?
So there has been a movement back toward linear probability models in cases in which multi-group comparisons are of focal interest. The rational for this is that cross-group comparisons of parameter estimates get shaky in probit and logit models requiring a bunch of finessing to get right...