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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! 


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. 

janemcready posted on Thursday, April 24, 2014  12:00 am



As you stated above using STDY or STDYX depends on the scale of the dependent and independent variable involved. How can I calculate and find which one can be used. What is the common things we should note in such cases. snoring solutions 


It depends on the independent variable. StdYX with continuous. StdY with binary. 

Leah Lipsky posted on Tuesday, September 08, 2015  11:56 am



I'm confused as to which standardized estimate to report for a path in a structural equation model with several latent and observed variables including 1 binary observed variable (b1regimen). The input statement is pasted below, and the path I am unsure about is indicated by the statement: l1a1c on ... b1regimen. Thanks very much. VARIABLE: NAMES are MASTERID AGE C1DEMAND C1DFR C1PCC C1PI_P1 C1PI_P2 C1PSYCTL C1RESPON G1DFR G1PCC L1A1C P1DEM21 P1DFR P1DSMP P1PCC B1REGIMEN; MISSING are all .; USEV are age c1respon C1DEMAND C1PCC C1PI_P1 C1PI_P2 C1PSYCTL L1A1C P1DEM21 P1DSMP P1PCC B1REGIMEN; CATEGORICAL are b1regimen; ANALYSIS: PARAMETERIZATION=THETA; DIFFTEST=model10full MODEL: pcc by C1PCC* P1PCC; pcc@1; cpi by c1pi_p1* c1pi_p2; cpi@1; auth by c1respon* c1demand c1psyctl; auth@1; c1respon@0; l1a1c on p1dem21 p1dsmp b1regimen; b1regimen on p1dem21; p1dsmp on p1dem21 pcc cpi; pcc on p1dem21 auth; cpi on auth age; auth on p1dem21; c1pi_p1 on age; c1pi_p2 on age; p1pcc on age; c1respon on age; 


You use WLSMV so your b1regimen predictor is its underlying continuous latent response variable. Therefore you should use STDY, standardizing relationships among DVs, which in fact is the same as STDYX in this case. 

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