lavaan

Hi Yves, It's a little bit strange. I ran 100 simulated datasets. Mplus, lavaan, and psych::

In this model I used theta parameterization, so it's the residual variance of Y1* that has unit

Thank you Mauricio and Yves! I think I've just made a rooky mistake! By following Yves's

Thank you Yves, the new beta version of lavaan solved the problem! Adriaan On Tuesday, 29 September

Hi, Thank you very much for the useful reference. I totally agree with Henrique, it is really great

There is an interesting paper comparing DWLS and WLSMV: DiStefano, C., & Morgan, GB (2014). A

Great! Thank you so much for your help. I'm glad to know that this was just a user error on my

I want to conduct a 2-factor CFA with ordinal data(due to nonnormality) Nonnormality does not imply

No, we are using theta, but I couldn't come up with a reproducible example, I think it could be

Sorry for such a long response. I was gone on vacations for the last 3 weeks. In turned out that it

I assume then that if your Likert scale points are coded numerically, you should use estimators for

Age might be a problem, because its scale seems huge relative to other variables. Is it measured in

Dear Mr. Jorgensen, Thank You for Your reply. You cleared a lot of my confusion. I posted it here

Is it in such cases (WLSMV and, hence, a subset of DWLS estimates) that robust fit criteria are

I wonder, what I should do now: discussion changed it's merit (thanks Mr Kaur!) and ended up with

Thanks for a very illuminating answer, I've already started reading the Wu & Estabrook paper!

Is there any way i dont have to run multiple Imputation? missing="pairwise" assumes MCAR,

Dear Terrence, thank you so much! It finally worked when I tried it at home! Thank you for all your

Dear Terrence, thank you so much for your incredibly fast answer! Even though it seriously melted my

Thank you very much! Am Dienstag, 19. Februar 2019 07:53:25 UTC+1 schrieb Mauricio Garnier-Villarreal

List, As I understand the implementation of the WLSMV estimator in lavaan, one can think of binary

Error in vnames(FLAT, type = "ov.x", ov.x.fatal = TRUE) : lavaan ERROR: model syntax

I see that when we use WLSMV, the observed and model-implied covariances are actually correlations

" Error in MASS::mvrnorm(n = sample.nobs[g], mu = Mu.hat[[g]], Sigma = COV, : 'Sigma' is

Is my baseline (configural) model already very poorly fitting because of its high RMSEA and low CFI?

Did lavaan automatically treat them as numerics? Sounds like it does. But the warning is probably to

Dear Mr Jorgensen, thank you so much for your quick and helpful answer, this is all that I needed and

yet the problem remains when I use the MLR (or even the ML) estimator. Well, using the latest

Results using "NA*" > myModel11 <- "FA =~ NA*T1_OD1a + T1_OR1a + V1_OD1a +

Thanks a lot for your response, Terrence! Since you are controlling the false discovery rate (FDR)