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Anonymous posted on Monday, March 12, 2007  9:23 pm



Hi, I have a construct that has 3 items that are dichotomous and other 4 items that are measured on a 3point scale. My measurement model for that factor has all the items for that particular construct as is. To bring this items on the same scale, I standardized these variables. When I run the Mplus model with Standardize variables, I am not able to get the model to converge. However, when I run the unstandardized variables, I can get it to converge. What should I be doing? I am not very comfortable putting items with different scales on the same construct with out standardization. Thank you 


You should not standardize these variables. Just use them as is. Put them on the CATEGORICAL list. The numbers for dichotomous and 3category variables have no numerical meaning. They just represent categories so standardizing them has no meaning. 


Good afternoon, I apologize if this questions has previously been addressed, but I had a question regarding a CFA analysis I am performing. I have a continuous latent variable measured by 8 indicator that are both continuous and categorical (4 ordered categories). When I look at the factor loading and the loading standardized using the the variance of the continuous latent variable (Std), there seems to be a distinct quantitative difference between the categorical and continuous variables. Would you recommend I use the StdYX standardization instead? If so, can you please explain why. Thank you! 


Are your categorical items declared as such in the Mplus input? 


Yes, they are declared as categorical. 


With categorical indicators you are considering probit or logit slopes which are on different metrics than slopes for continuous indicators. If you use StdYX you will get results that are more similar because then you consider an underlying continuous y* variable. In that scale, categorical items may nevertheless have smaller coefficients due to being less reliable indicators. 


Hello, In Mplus user's guide, it is said that STDY cannot be obtained for categorical outcomes and weighted least squares estimation. I run CFA on (only) categorical outcomes with WLSMV estimation (delta) on Mplus 5, and the output shows STDY estimates. Is that normal? Thank you! 


I think this is when there are covariates in the model. If you get estimates, you can trust them. 


Hello, I am running a factor analysis on categorical variables and would like to have standardized parameters. I have two questions. 1. In the Mplus documentation, I have not been able to find the definition of the standardized parameter arrays for tau, the threshold parameters. Are these defined similarly to the standardized parameter arrays for nu (intercepts)? 2. STDY is not available for categorical outcomes and WLS estimation. However, STDYX is available. In my model there are no background (X) variables. Is it correct that STDYX is equal to STDY when there are no X variables? And how can STDYX be available when STDY is not? Thank you in advance. 


1. Yes. 2. Yes. You would need to send the full output and your license number to support@statmodel.com. I can't replicate this result. I get all three standardizations with WLSMV. 


Hello, Is it possible to standardize variables in Mplus prior to starting your modeling? I know that there is the option to center variables prior to modeling. Can I also standardize variables prior to modeling (put them in zscore format)? Thanks! Lisa 


Yes, this was added to the DEFINE command in Version 6.1. The STANDARDIZE option standardizes continuous variables by subtracting the mean from each value and dividing by the standard deviation. Following is a example of how the STANDARDIZE option is used: STANDARDIZE y1 y5y10 y14; where the variables y1, y5 to y10, and y14 will be standardized. The order of variables for the list function is taken from the order in the USEVARIABLES statement. If there is no USEVARIABLES statement, the order is taken from the NAMES statement. When a variable on the STANDARDIZE list is used in other transformations, the original values of the variable are used. Standardization takes places after all other transformations have been completed. 

Joe King posted on Tuesday, December 20, 2011  11:44 am



how do you interpret standardized parameter estimates (STDY) when the variable associated with that parameter estimate is standardized. 


If the variable is standardized, it's standard deviation is 1 so there would be no difference. 


Dear Professor, I have consulted other posts concerning which standardization output to report but I am still confuse. I ran a CFA with 43 categorical variables (Likert scale 15). Should I report raw coefficients, STDYX or STD for the factor loadings and residual correlations? Thank you! Nicolas 


You can use either Std or StdY. An IRT person would standardized with respect to only the factor. A factor analysis person would standardized with respect to the factor and the factor indicator. 


I ran a 2factor CFA. Factor 1 (F1) had 4 items, and factor 2 (F2) had 5 items. Both factors were latent continuous. The model fit indices were all good. All of the factor loadings were greater than .3. I see the coeffecient for F1 with F2 was 1.057 using both the STDYX and STD. I am unable to find a discussion that addresses what to do with this coeffecient greater than 1. Can you provide some counsel? Thanks 


See our FAQ: Standardized coefficient greater than 1. 

JPower posted on Tuesday, April 09, 2013  9:25 am



Hello, I have been examining a CFA with 5 latent factors, each with multiple ordinal indicators. The model is estimated using WLSMV. I have now extended the CFA to a MIMIC model and have included a dichotomous variable. I have regressed each of the latent factors ON this new dichotomous variable. I would like to be able to report STDY for these new relationships (because the variable is dichotomous I only want to standardize with respect to the latent factors). However these estimates do not appear in my output. Prior to including the dichotomous variable, my CFA outputs did include STD, STDY and STDYX estimates. In this new MIMIC model, STD and STDYX estimates are output, but there are no associated SE, Est/SE or pvalues. Additionally, STDY estimates no longer appear at all. Do you know why this might be? I have included STAND in the OUTPUT section of my code. Thanks. 


With WLSMV and covariates, limited standardized output is given. You will need to create StdY by dividing StdYX by the standard deviation of x. 

JPower posted on Friday, April 12, 2013  10:19 am



Hello, I have a couple of additional questions about standardization in CFAs and MIMIC models estimated using WLSMV. 1) In a CFA model with all ordinal indicators, I add a "with" statement between 2 indicators. For this estimate, the raw and STD estimates are the same. The STDYX and STDY estimates are the same. Am I correct in understanding that the raw and STD estimates for this relationship represent the correlation between the y* indicators (the underlying continuous indicators that are standardized by default in WLSMV)? Does it follow then that the STDYX and STDY represent the correlation between the observed ordinal indicators? Am I correct in assuming that it is the corr between the y*'s that I want to report? 2) In a MIMIC model, I have regressed a latent variable and one of its indicators on a continuous covariate. For the path from the covariate to the latent variable, I am reporting the STDYX estimate (both latent variable and covariate standardized). For the effect of my covariate on the indicator, I am unclear what the estimates represent in terms of y* and what is being standardized. Again, the raw and STD estimates are the same, but the STDYX is different. Is it correct that the raw and STD estimates represent the effect of the covariate on y*? And that the STDYX refers to the effect of the covariate on the observed indicators? Which should be reported? Thanks again. 


1) WITH between 2 factor indicators (or any DVs) refers to their residual covariance. So, standardizing wrt Y gives the correlation between the residuals for the 2 indicators. That's worth reporting. 2) Yes and yes. It is easy to understand what goes on if you recall that STD is standardization wrt the latent variables only. The latent variables are not involved in the residual covariance, nor in the direct effect of a covariate on a factor indicator. 

RuoShui posted on Friday, September 27, 2013  9:53 pm



Hello, I extended my SEM model into a MIMIC by including a continuous observed score (on the same scale as the other factors)and a z score of socioeconomic status. However, there is no Est/SE or Pvalue any more for the stdyx output. Would you please let me know why this happens? Thank you very much. 


It sounds like you are using WLSMV. We don't give standard errors for the standardized estimates for conditional models. This will be added in the next update. 

RuoShui posted on Saturday, September 28, 2013  12:34 pm



HI Linda, Thank you. Yes, it is true. I was using WLSMV because the independent variable has binary indicators. 1) My independent variable, mediators and outcomes are all on different scales, that is why I want to use stdyx, is this correct? 2) If no pvalue is reported for stdyx and std, can I use the pvalue for the understandardized model results? Thank you! 


The scale of the independent variables is not an issue. It is only the scale of the dependent variables. You should not put observed exogenous variables on the CATEGORICAL list. 1. Use StdY for binary independent variables. Use StdYX for continuous independent variables. 2. No. 

RuoShui posted on Saturday, September 28, 2013  1:58 pm



Thank you Linda for your quick reply. I am still a little unsure. The output did not print StdY. What numbers (estimates and pvalue) should I report? 


You can turn StdYX into StdY by dividing it by the standard deviation of x. There are no pvalues to report for the standardized estimates. 


When reporting the standardized effects of a set of predictors/covariates, some continuous and some dichotomuous, on a latent variable with continuous indicators, should I report the SDTYX for the continuous and the SDTY for the dichotomuous, or should I report the SDTY for all the predictors (the whole covariate model) in order to be consistent, since some of them are dichotomuous? Thanks Frodi 


StdYX for continuous. StdY for binary. 

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