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1. What must be the nature of a covariate when doing ESEM (on ordinal outcomes): can it be continuous, ordinal, binary? 2. As covariate cannot be specified in CATEGORICAL, how does Mplus 'know' the distribution of the covariate? Could you please give some references where this topic is explained? 3. You wrote in another thread: "You can include the covariates in the analysis by mentioning their variances in the MODEL command. They are then treated as dependent variables and distributional assumptions are made about them". Could you please give references where such a case is shown? Thank you very much in advance. 


12. As in regular regression, covariates can be binary or continuous and in both cases are treated as binary. See any regression textbook to read about this. 3. I know of no reference to this. It is what you do in Mplus to change the status of an observed exogenous variable from independent to dependent. 


Thanks Linda for your answers. I am realizing that I am still on a steep learning curve, here... 3. Sorry to insist, but could you please specify where in the Mplus literature I could find explanation on the matter (I looked in the user guide but couldn't find any examples)? I'm not sure I really understand the reasons and implications of changing an observed exogenous variable from independent to dependent, hence I'm not sure it is what I want to do. Thank you for your time. 


You would only bring the covariates into the model to avoid the observations being deleted due to missing data on the covariates. You should not concern yourself with this. 


Hi Linda, Bringing the covariates (e.g. X1, X2) into the model would be done simply like this? MODEL: X1; X2; 


Yes. 


Hello Drs. Muthen  I am hoping that you can assist with some confusion that I have regarding covariate modelling. Briefly, I am conducting a multimediation analysis. I have incorporated several standard observed demographic covariates (age, gender, race) as well as a latent socioeconomic status variable in a model that uses both latent and observed IVs and an ordered categorical DV (using WLSMV estimation). In the model statement, if I use the statement, SES on AGE RACE MALE, I receive a warning message indicating that two dichotomous variables (RACE and Male) were declared as continuous. It appears that means and variances are estimated for these variables. However, if I attempt to correct this by switching to covariances (SES with AGE RACE MALE) I receive a message indicating the following: WARNING: THE RESIDUAL COVARIANCE MATRIX (THETA) IS NOT POSITIVE DEFINITE. THIS COULD INDICATE A NEGATIVE VARIANCE/RESIDUAL VARIANCE FOR AN OBSERVED VARIABLE..... Removing either of these statements entirely is not an option as model fit is bad and modification indices suggest otherwise. Might you have any suggestions as to which is correct? Particularly when both options provide warning messages Thank you. 


Please send the outputs and your license number to support@statmodel.com. 


Hallo Drs. Muthen, I also have two question on how to put continous covariates into a SEM. I have two binary IVs, three continous DVs and 1 continous Mediator. The two covariates are continous too (CO1 and CO2). 1) Would the model command look like this?: DV1 DV2 DV3 ON IV1 IV2 CO1 CO2; DV1 DV2 DV3 ON Med1; As I also want to test for mediated moderation, my next question therefore is: 2) Do I also have to integrate the covariates in the Model Indirect?: DV1 VIA Med1 IV1IV2 (Interaction of IVs) CO1 CO2; DV2 VIA Med1 IV1IV2 CO1 CO2; DV3 VIA Med1 IV1IV2 CO1 CO2; Thanks in advance!! 


No, you should say DVj IND IV1; DVj IND IV2; repeated 3 times for j=1,2, 3. 

A. Ardèvol posted on Monday, January 13, 2020  11:27 am



Dear Drs. Muthén: If I mention the variance of my exogenous variables in the MODEL command, the output shows estimates for the relationship between these exogenous variables (WITH's), without using SAMPSTAT. For example: X1 WITH X2 0.051 0.015 3.359 0.001 Can I understand these estimates as estimates of the correlation among my exogenous variables? Is it a correlation or a covariance instead? Thanks a lot for your time and expertise 


It's a covariance unless the variances are both 1. 

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