Anonymous posted on Thursday, April 28, 2005 - 12:39 pm
Hello. I am attempting to run a path analysis will all variables in my model treated as directly observed. Since I am not including a measurement model, I would like to correct for measurement error. I am aware that this can be achieved by multiplying the variance of an observed variable by 1 - relability.
My first question is that I only want to employ this correction with exogenous variables in the model, not endogenous variables - correct?
My second question is how do I fix the variances in MPlus? I have tried using the @ function (e.g., x@.09) following the model command but this drastically worsens rather than improves model fit. Do I need to create single indicator latent variables to employ this correction? For example:
This seems to help model fit, but I am not sure it is proper procedure.
Finally, I am using the define command to examine interaction terms in my model. Does it matter if I fix the variance of a variable that represents one of interaction terms?
Any help you can provide will be very much appreciated. Thank you.
bmuthen posted on Thursday, April 28, 2005 - 6:30 pm
As for your first question, the correction is most important for exogenous variables given that parameter estimate biases will occur otherwise. But you may want to do it also for dependents, to separate measurement error and other residual sources.
You answered your second question yourself. I think this is posted somewhere on Mplus Discussion. Note that you are fixing the residual variance and that you should fix it to
(1 - reliability)*sample variance.
For you final question, I don't know why you would want to fix the variance - unless you are referring to the second question above in which case you want to do the interaction using the factor you define.
Anonymous posted on Thursday, April 28, 2005 - 6:56 pm
Thank you for your help. Your answers to my first two questions I followed, and I also found the other posting and it was very helpful.
I would like to follow-up/clarify your response to my third question. Assume I create a latent variable for an observed variable (x) in order to fix the residual variance of that variable. Also assume I want to create a third variable that represents the interaction (xz) of this variable with another observed variable (z). Do I create the interaction term using the original observed variable (x) or the latent variable I created for x? If I have to now use the latent variable, do I need to change from using the define command to create the interaction to using the XWITH command?
Thanks again for your help.
bmuthen posted on Thursday, April 28, 2005 - 8:00 pm
You would use XWITH, not Define.
Timothy posted on Monday, April 26, 2010 - 7:16 pm
Hi Prof. Muthen
I am using the same approach stated in the above to run a path analysis. Even thought I used the two commands, the model fit is still drastically worsens, rather than improved. I then used LISREL to run the path analysis with the same approach and had good fit of the data. I am wondering if I have done something wrong in the Mplus command. Can I send you the outputs to you and see if I have any problems with the commands?
I am trying to run a path analysis with all variables in my model treated as directly observed. Since I am not including a measurement model, I would like to correct for measurement error. I am aware that this can be achieved by multiplying the variance of an observed variable by 1 - relability. Could you pl. tell me where the sample variance is in the output?
If you ask for SAMPSTAT or use TYPE=BASIC, the variances are on the diagonal of the variance/covariance matrix.
Bee Jay posted on Monday, March 26, 2012 - 4:09 pm
I am using this equation as well, to fix residual variance for single indicators - as discussed in another thread. So the sample variance is in the SAMPSTAT output. Is the "reliability" you're talking about the variance explained for the indicator? R^2?
And when I have completed the equation, will I just enter it into my model, e.g. F1@__;