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Mplus Discussion > Structural Equation Modeling >
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 jmaslow posted on Thursday, December 08, 2011 - 12:24 pm
I am estimating a multiple group model using wlsm estimation because some indicators are categorical. It seems from other responses that I should use STD, not STDYX coefficients for standardized results. However, STD produces factor loadings greater than 1. Does this mean STDYX is the appropriate choice? Thank you!
 Bengt O. Muthen posted on Thursday, December 08, 2011 - 8:26 pm
Try STDY or STDYX for the factor loadings.
 jmaslow posted on Tuesday, December 13, 2011 - 10:07 am
Thank you, Dr. Muthen. And for path coefficients, I would still use STD?
 Linda K. Muthen posted on Wednesday, December 14, 2011 - 12:01 pm
The depends on the scale of the dependent and independent variable involved. for example,

factor ON factor STD
factor ON binary x STDY or STD
factor on continuous x StdYX

I am assuming the dependent variable is a factor. If it is an observed variable,

observed ON binary x Stdy
observed ON continuous x StdYX
 Jaime Puccioni posted on Thursday, November 08, 2012 - 8:14 am
Hello.

I have estimated a latent growth curve model. I estimate two latent intercepts and two latent slopes. I have two latent factors that predict the latent intercepts and slopes. i also include a set of binary (dummy codes for race and gender) and a continous SES variable as predictors of all the latent factors in the model. Based upon Linda's previously post that says,

"... depends on the scale of the dependent and independent variable involved. for example,

factor ON factor STD
factor ON binary x STDY or STD
factor on continuous x StdYX

I am assuming the dependent variable is a factor. If it is an observed variable,

observed ON binary x Stdy
observed ON continuous x StdYX"

I would use a combination of STD and StdYX.

If this is the case, can i compare STD and StdYX estimates to each other?

thank you,

Jaime
 Linda K. Muthen posted on Thursday, November 08, 2012 - 2:11 pm
No, not if there are x's in the model.
 AliceAnn Crandall posted on Thursday, June 27, 2013 - 11:28 am
I have categorical factor loadings on my latent variables but the covariates that I'm controlling for are continuous. Once I add them into the model, the output for the standardized results does NOT include the standard errors or p-values - just the betas. The unstandardized results still include the SE's and p-values for each of the betas. Can you tell me how I can fix the problem of the standardized output not including the full information? Here is the syntax for my model:

use variables are bri1_r bri12_r bri19_r bri28_r bri33_r bri42_r
bri51_r bri57_r bri69_r bri72_r d8 d9 d10 d13 d15 age;
categorical are bri1_r bri12_r bri19_r bri28_r bri33_r bri42_r
bri51_r bri57_r bri69_r bri72_r d8 d9 d10 d13 d15;
Missing are all (-9999);
Model: EMOCTRL by bri1_r bri12_r bri19_r bri28_r bri33_r bri42_r
bri51_r bri57_r bri69_r bri72_r;
HARDIS by d8 d9 d10 d13 d15;
HARDIS on EMOCTRL;
EMOCTRL on age;
d13 with d15 (p);
output: standardized;
modindices;
 Linda K. Muthen posted on Thursday, June 27, 2013 - 2:03 pm
We don't provide these values for models with covariates and WLSMV.
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