I have a tricky data set for factor analysis with 71 ordered likert-type items. The items have five levels. I'm using psych::fa.
I use ``avaan::lavCorto get a smoothed covariance matrix. to avoid errors withpolychoric`.
The base analysis I've tried is:
test1 <- fa(postImputedCor,
nfactors = 11,
n.obs = nrow(ddItemsOrdered),
rotate = "oblimin",
fm = "pa",
smooth = TRUE,
oblique.scores = TRUE,
cor = "poly")
This uses the default "regression" method of weighting the scores, but I have tried them all ("Anderson", "tenBerge", "Thurstone"). I have also tried "uls" and "wls" in addition to "pa" as factoring methods.
The warning I get is:
Warning message:
In fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Are there other options I should try in order to get reliable factor scores?
psychand I don't know its options, but classic f. score estimation methods are not meant for the analysis of inferred correlations such as tetrachoric or polychoric. Special methods are needed for that, and I don't know if psych offers them. – ttnphns Feb 21 '23 at 13:14psych::fadoes have an option for a polycoric correlation. It has functions for calculating these correlations, but they are generating errors when I try to use the data as input (rather than a a correlation matrix; both should be allowed according to the docs). – hare Feb 22 '23 at 21:18,cor="poly"https://stats.stackexchange.com/questions/618250/factors-or-components-in-stata-and-r-psych-lavaan-confusing-method-labels-an – Johan Jun 09 '23 at 09:37