A key question is if the latent class variable is a within- or a between-level variable. If the classes represent subjects, the latent class variable should be on the Between = list in a Type=Twolevel analysis. The User's Guide shows several such examples.
Thank you Dr. Muthen. I've attempted the Type=Twolevel analysis, but am unable to identify the latent classes in the output. If it helps, I am only looking at LCA to determine classes and class membership in the measurement model at this point.
When you put a variable on the Within list, you cannot estimate a variance for it on Between - that is, you are saying that it has zero variation across clusters. Typically, a variable is observed for (say) a student in (say) a school and therefore has variation on both levels. In a factor analysis setting, the factor indicators on between are the latent, random intercepts of the observed variables measured on the student.
With this as background, it sounds like you don't want to put your variables on the between list, you want to have factors on between, and you want to freely correlate the variables on within. So maybe you want to say something like
1. Ordinal data and EFA: Is there a particular approach to EFA given the 7 indicators of social support from each network member are measured on a Likert scale from: 1) never; 2) a little; 3) sometimes; 4) frequently; 5) always?
2. Frequency of responses: I understand ordinal data is not normally distributed, but is it problematic that the frequency of '5) always' responses is high across all 7 indicators compared to 1) through 4)?
Generating a binary variable doesn't seem meaningful given Always compared to Never to Frequently doesn't make sense.
3. The eigenvalues Within: 3.68, 0.79, 0.692, 0.559, 0.498, 0.418 and 0.361. Between: 4.547, 0.606, 0.604, 0.375, 0.336, 0.297 and 0.234.
This would indicate only one factor.
Is factor analysis useful with only one factor? I'm thinking it would provide a factor "score" for each individual (between level) indicating the degree to which they receive this latent construct of social support.
I've read that using factor scores in regression can lead to inconsistent coefficient estimators and I'm unsure how to address this if indeed factor analysis is more appropriate than latent class analysis (generating binary indicators from the Likert scale)