Confirmatory Factor Analysis and Measurement Models

Hello, my name is christiane and I am a new user of Open MX.
In our study, we are looking for possibilites to analyse a combined data set with lots of missing data (we have combined data from 4 studies, just a few of our items were administered in each study). Can anyone tell me, if there is a covariance coverage limit implemeted in Open MX for CFA with Full-Information Maximum-likelihood (FIML)? Or how does Open MX deal with this?
Thank you and best wishes,
christiane

Hello

I want to examine the relationships between three latent variables namely: Emotional Intelligence, Self Efficacy and Geography Achievement using SEM with Amos. I used three instruments to collect data. Emotional intelligence was tested using scale consists of 39 items divided to seven subscales, self efficacy was tested using scale consists of 22 items divided to two subscales and geography achievement was tested using test consists of 40 items divided by three subscales.

What element of model output or summary stores the factor scores from an EFA or CFA in OpenMx?

I was hoping to find out if there was any documentation on multilevel CFA - I have a sample of children clustered within families. The model I am trying to fit has 12 observed categorical variables (0,1,2) and 4 latent variables. Could you point me in the direction of some examples?

Hello all,

I'm trying to estimate the reliability of a sub-scale using a procedure outlined in Raykov (2004), which is attached. If I understand it correctly, I need to constrain the path from each error term to its respective indicator to be equal to the sum of factor loadings. That is, the path from E1 to Y1 must equal L1+L2+...+Lk .

Further, I need to specify dummy latent variables with variances that are equal to my constrained residual variances.

Hello all,

Preemptive apologies for the rudimentary question, but I'm having trouble specifying correlated errors for a confirmatory factor model. It is possible that I am specifying them wrong, or that there is some problem with my model (I'm hoping it is the former). Currently, I am using a path specification, which indicates a double-headed arrow between my first and fifth indicator:

#-------------------------
mxPath(from = c("ac1"),
to = c("ac5"),
arrows = 2,
free = T,
values = 1,
labels = ("cov15"))
#-------------------------

In LISREL, it is possible to get a matrix of parameter variances and covariances. I need values from this matrix to compute some reliability estimates and am wondering if there is a way to get them from OpenMx. Is this matrix already computed? Is there a good way to access it? I'd appreciate any help!

Hi
When running the demo script 'ThreeFactorScale2Test.R' (http://openmx.psyc.virginia.edu/repoview/1/trunk/models/passing) on OpenMx version 1.1.12 I get the following error message:
Error in mxPath(from = latents1, to = indicators1, arrows = 1, connect = "all.pairs", :
unused argument(s) (connect = "all.pairs")

I guess this is the part of the script where the error appears:

threeFactorOrthoRaw1 <- mxModel("threeFactorOrthogonal",
type="RAM",
manifestVars=indicators,
latentVars=latents,
mxPath(from=latents1, to=indicators1,
arrows=1, all=TRUE,

Hi,
this is probably not the best place to ask this question but I desperately need your help :).
I need to write a paper about Confirmatory Factor Analysis and Measurement Models. My major problem is that I don't have access to database where I could find some articles about the topic. I would be very, very grateful if someone could send me an article on this topic or recommend me a book in which I could find something useful (delmae28@gmail.com).

Many thanks in advance,
Delmae

I am new to OpenMx and trying to resolve a problem with non-positive definite covariance matrix. I have looked at other postings in this forum and it looks like it is a starting value problem although not sure. I have tried several combinations and keep getting the same error. I am listing my code and would appreciate any help.

Thanks

I keep getting the following error:

Running Political Democracy CFA