Message/Author 

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! 


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? 


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 


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 


No, not if there are x's in the model. 


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 pvalues  just the betas. The unstandardized results still include the SE's and pvalues 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; 


We don't provide these values for models with covariates and WLSMV. 

Back to top 