Confirmatory Factor Analysis and Measurement Models

Hi,
Does anyone know how to estimate latent scores in the following model?
What package should I use?
best regards,
Krzysztof

Hi all,
I am VERY new to SEM and also to OpenMx and I am trying to run a very simple CFA in OpenMx, which has 1 latent factor (F1) and 3 indicators of A, B, and C with C as my scaling variable and I have a couple of questions about the output that I got:

Guys,

I Have a basic question...though I am confused :( I wanted to know whether EFA and CFA is applied construct wise or it is applied on overall model by considering all the construct together. Please help me in this and also share if you have any reference material for this.

Dear All,
I need your help, using OpenMx for CFA (non normal data). Now, I am very enjoy with OpenMx than the other SEM-Software.
Thank you very much

Greetings!

Is a total measurement model always needed? I have a model with 14 latent variables, with 5-9 indicators for each variables. A total measurement model is such a huge total measurement model. Can I just simply use individual measurement models for each construct and then use path analysis (to use the mean of the indicators for the latent variables, which means, transform the latent variables to observed variables)?

Many thanks!

Best regards

Dan

Hi,
I am non-expert SEM user. I am trying to run a CFA with the EQS 6.2 program. After creating the EQS input file, I tried to run EQS but in the output file I found the following sentence:
*** ERROR *** VARIABLE NUMBER 1, A DEPENDENT VARIABLE, EXCEEDS THE NUMBER OF VARIABLES READ. and so on for all other variables. Could you help me to solve the problem?
Thanks

Hi,

I'm trying to reproduce an analysis of a congeneric two- factor- model from a textbook and I can't figure out where I am going wrong. The model has two latent factors (selfevaluation and foreignevaluation scales) with three indicators each. The output I get looks like this:

> summary(congeneric)
free parameters:
[1] lbound ubound
<0 Zeilen> (oder row.names mit Länge 0)

observed statistics: 0
estimated parameters: 0
degrees of freedom: 0
-2 log likelihood: NA
saturated -2 log likelihood: NA
number of observations: 0
chi-square: NA
p: NA

Hello,
my question is quite a general newby one on whether to use FIML, and therefore OpenMX. I'd appreciate any comments on which direction to take.

I'm considering using it (and therefore using OpenMX) because I understood it is very well adapted to use with binary data, compared to conventional techniques (using tetrachoric correlation is often problematic, I believe, and WLS requires large numbers). However, the OpenMx Manual cites handling of missing data (which didn't worry me that much, as I was expecting to use listwise deletion):

i have four observed vaiables and fit one factor model on these four observed variable.when i am try to fit one factor model on my data which consist of ordered categorical variable with fiv ecategories. when r code run the whole programme warning message appear that "NPSOL is returned a non zero status code 1.The final iterate satisfies the optimality conditions to the accuracy requesed but the sequence of iterates has not yet converged. NPSOL was terminated because no further improvement could be made in the merit function (Mx status GREEN). "