Anonymous posted on Wednesday, October 20, 2004 - 9:57 am
I am interested in running a vanishing tetrad test to determine if the items on a scale are effect or causal indicators. To run this test, I need to obtain the implied covariance matrix for a model with causal indicators. However, I am unsure of how to do this in MPlus. I would definitely appreciate any guidance. Thank you!
The RESIDUAL option of the OUTPUT command is used to request model estimated means, variances, and covariances.
Anonymous posted on Wednesday, October 20, 2004 - 10:09 am
Thanks for getting back to me so quickly! I'm actually wonder how to set up the model. I have no problem specifying an effect indicators model where the paths go from the latent variable to the individual items. (It's just a CFA model.) However, I'm not clear on how to switch the direction so that the paths go from the individual items to the latent variable.
"In some applications it may be more natural to let a factor be defined as being influenced by indicators, rather than influencing the indicators. An example is SES. Mplus handles this modeling by the following model statements.
I am trying to run a cfa with 3 latent variables and a categorical outcome variable.
As part of this model, I have one "latent" variable with 2 categorical indicators (hs grad, minority) and 1 continuous indicator (age). I want to use these 3 indicators as effect indicators of background risk.
However, I am not clear how to accomplish this using Mplus (again, switching the arrows). Any help would be appreciated!
I am trying to run the following causal indicator model with four continuous indicators (api_kop, api_tijd, api_mee and api_and) for the latent variable action_i, and with smerenbn as the only dependent variable related to action_i.
Although this model above runs without problems, I run into trouble when adding any other paths with smerenbn as dependent variable (which is necessary for my model). I keep getting error messages: THE DEGREES OF FREEDOM FOR THIS MODEL ARE NEGATIVE. THE MODEL IS NOT IDENTIFIED. NO CHI-SQUARE TEST IS AVAILABLE. CHECK YOUR MODEL.
Is it not possible to use the y variable (smerenbn) in any other instance than to define the factor?
Looks like you are doing formative indicator analysis which is fine as you have specified it, although all you have to say is
to define the action_i factor (nothing needs to follow BY).
There is no problem in letting your y (smerenbn) be a dependent variable for other predictors as well. If you can't see the problem, send your input, output, data, and license number to firstname.lastname@example.org.
I have a latent variable model with one latent variable being determined by 3 causal (or formative) indicators. There are no effect indicators in this model. I also have this latent variable determining 3 endogenous variables. For the purpose of scaling, can I set any of these 6 paths to 1? Also, in addition to this, do I have to constrain the error variance of this latent variable in any way?
Not sure where to post this--I was wondering if there were any plans to include the vanishing tetrad test with future versions of Mplus (rather than having to grab the output and stick it in Hipp's SAS macro).
I should be able to distinguish between casual and effect indicators using theory (Blalock's 'mental experiments'). But I've found that I'm increasingly getting into situations where it's hard to know whether some variables are causing something or the effect of it. I think I know (based on background knowledge and trying out different model structures), but it would be nice to test for it in an easy way.
The best example from my own work is using variables related to job quality; I have several job-related variables (e.g., having to with work autonomy, or pay) and I think I have a pretty good sense of which ones are causing a job-related outcome (e.g., work autonomy) and which ones are reflections of the job-related outcome. But it would be nice to be able to test it to make sure.
To be fair, I can test it, using the suggestions in this thread, it just requires using multiple programs and takes a fair amount of time. It would be great if I could do it all within Mplus.
I am trying to run a causal indicator model where latent SES has causal indicators educate and income. latent SES predicts observed colohad, and this relation is mediated by latent variables benefits and barriers. Global fit statistics are good, but when I run the following syntax, I get the message:
WARNING: THE LATENT VARIABLE COVARIANCE MATRIX (PSI) IS NOT POSITIVE DEFINITE. THIS COULD INDICATE A NEGATIVE VARIANCE/RESIDUAL VARIANCE FOR A LATENT VARIABLE, A CORRELATION GREATER OR EQUAL TO ONE BETWEEN TWO LATENT VARIABLES, OR A LINEAR DEPENDENCY AMONG MORE THAN TWO LATENT VARIABLES. CHECK THE TECH4 OUTPUT FOR MORE INFORMATION. PROBLEM INVOLVING VARIABLE SES.
I ran tech4 and there is not a correlation greater than or equal to 1 or negative residual variances, but SES has a residual variance of 0.
Is there something else I should be looking at to fix this? I am testing this out with only benefits and barriers as mediators, but I would like to run a larger models with a few more latent mediators.