General SEM Discussions

Hello everyone,

I am struggling at the moment to build the model for my research on value creation in academia-business relationships (so companies working with universities). I would like to use structural equation modelling for this.

On one hand, I have "relationship factors" (such as trust and commitment) which influence the "perceived value" of the researcher which again influences "satisfaction" and "intentions"

So, to put it simple:

Relationship factors -> perceived value -> Satisfaction / intentions

ran two models fitting the same model to the same data. only difference is that one has no parameter bounds while the other does. here are the confidence limits for the two:
> summary(testNoBounds)$CI
lbound estimate ubound
testCI.parms[1,1] 0.2229001 0.6259667 1.0243803
testCI.parms[2,1] -0.4849764 -0.1423273 0.1882364
> summary(testBounds)$CI
lbound estimate ubound
testCI.parms[1,1] 0.1880914 0.463186 0.5739613
testCI.parms[2,1] 0.0000040 0.000000 0.2158373

Hi.
I am running a bivariate sex-limitation model with an age variable as a moderator on both means and variances/covariances. All models (full and nested) seem to work pretty well, estimates are reasonable and there is no error while converging (not even code GREEN). However, when I look at the standard erros, I can see that some of them turn out to be NAN's. What could be a reason of it and how can I fix it?

Thank you beforehand.
Julia

Hello everyone,

Marker variables are variables that previous research has shown to load highly on the factor being considered
and very little on any other factor (Cattell, 1988; Gorsuch, 1974).

I am having difficulty understanding what marker variables to use for need recognition, information search, evaluation of alternatives, purchase and post-purchase factors of the buying-decisions process.

Thank you very much in advance.

Sorry for posting this again.

I am trying to adapt the sample script on FIML estimation for missing data. In the script below (from OpenMx documentation) how do I change the component of the "firstHalfCalc" expression of the likelihood function (log2pi %*% 2) to represent the number of variables that has nonmissing data for that observation? Will the sample code work for all situations without modification?

Sorry if this is a silly question. I am quite new to OpenMx.

#simulate data
require(MASS)
set.seed(200)
rs <- .5

Hello,
I am testing a model in e-commerce adoption and like to use SEM. However, I have problem with the method as I am not sure I can do it. I enclosed the model and would like to ask you to help me for the initial steps.

Hi everybody,

I am new to the methodology of SEM, and though I do have a basic understanding of regression and factor analysis, I have not come to grips with one question:
Instead of adding a measurement model to the system of regression equations that contains one or more latent independent variables, could I instead feed the measurement items to the regression equations directly?

I would like to fit a survival model using OpenMx. The types I would like to fit are discrete-time survival, cox regression, and cox proportional hazard. If possible, please include an example of the mxModel in the response. Thank you in advance for your help.

Hi
I thought the listwise deletion approach to dealing with missingness in definition variables used in clasicMX was carried over to OpenMX. Some teaching scripts which used to run are now yeilding the following errors (openmx version number: 1.0.7-1706)
Error in runHelper(model, frontendStart, intervals, silent, suppressWarnings, :
Error: NA value for a definition variable is Not Yet Implemented.

Is there an argument to mxRun or mxData that I can include to get old-school automatic listwise deletion?
thanks
Sarah

Hi :-)

First I'm so happy that this group has written SEM software for R - Mplus is just way out of my budget!! Second I'm sort of new to this stuff, so I have a few beginner sorts of questions and I hope you'll bear with me - sorry.

I've never really understood modeling means in SEM. I know how it's done in OpenMx after reading through Dr. Boker's course notes, but I just don't understand exactly how the means are being estimated and why one would want to estimate them. I can't find any reference to this in any texts, so I would love some help or a point in the right direction.