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Hi, Is there a way to get standardized coefficients and fit indices (RMSEA, CLI, etc.) with MLR? I am comparing models and selecting the best with BIC but because I have an interaction model using the xwith command I don't seem to have these available. Thanks. 


These are not available for TYPE=RANDOM. 


Thanks Linda. Do I have any other kind of options? I can't do a multi group because of a small sample size. 


With TYPE=RANDOM; the variance of y varies with the values of x. This precludes the calculation of standardized coefficients and chisquare and related fit statistics. In this case, nested models are tested using 2 times the loglikelihood difference which is distributed as chisquare. 

Syd posted on Thursday, July 22, 2010  1:30 pm



Hi, I am testing the direct and interaction effects of two secondorder continuous latent variables (f1 and f2) on another continous latent variable (f3), and the mediation effect of this variable on a binary (f4) dependent variable with a single indicator. My model is roughly as follows (I have left out BY commands for f5 through f10 for simplicity): f4 BY y1; f1 BY f5 f6 f7; f2 BY f8 f9 f10; f3 BY x1x6; f3 ON f1 f2; f1xf2  f1 xwith f2; f3 ON f1xf2; f4 ON f3 f1 f2 f1xf2 I have two questions: 1. I understand that I need to use either MLR or WLSMV estimators in a random effects model with numerical integration. Is this correct? 2. I also understand that no fit indices are provided when testing continuous latent variable interactions. If my understanding is correct, then how can I understand whether my model is good or ill fitting for the full model? I will greatly appreciate any suggestions and guidance. 

Syd posted on Thursday, July 22, 2010  1:37 pm



Just to clarify my second question, above, I am wondering if there is a way to assess absolute fit of my full model. Thank you. 


1. Only MLR is available with XWITH. 2. No absolute fit statistic is available for this model. I would make sure the fit is good for the model without the interaction and then check that the interaction is significant. See a recent paper in Psychometrika on fit for interaction models by Moojart and Satorra. 

Syd posted on Thursday, July 22, 2010  7:50 pm



Thank you for the very prompt explanation and the reference. It was very helpful. 

Syd posted on Saturday, July 24, 2010  4:25 am



Hi Linda, While testing a model with a singleindicator, binary dependent variable, I have run into an issue that I can't figure out. My model is as follows: VARIABLE: CATEGORICAL ARE y1; ANALYSIS: ESTIMATOR=MLR; MODEL: f1 BY x1x3; f2 BY x4x6; f3 BY x7x9; f4 BY x10x12; f5 BY x13x15; f6 BY x16x18; f7 BY x19x25; f8 BY f1f3; f9 BY f4f6; f10 BY y1; f7 ON f5 f6; f10 ON f7; The model runs fine with a single indicator continuous dependent variable, I get the following message when I use the binary DV: THE MODEL ESTIMATION DID NOT TERMINATE NORMALLY DUE TO A NONZERO DERIVATIVE OF THE OBSERVEDDATA LOGLIKELIHOOD. THE MCONVERGENCE CRITERION OF THE EM ALGORITHM IS NOT FULFILLED. CHECK YOUR STARTING VALUES OR INCREASE THE NUMBER OF MITERATIONS. ESTIMATES CANNOT BE TRUSTED. THE LOGLIKELIHOOD DERIVATIVE FOR PARAMETER 72 IS 0.16653809D+00. I have tried increasing the MITERATIONS up to 1000, but the result remained the same. I would greatly appreciate some insight regarding what I might be doing wrong. Second, for such a model, is there a way to get the indirect effect of f8 (via f7) on f10? Thank you, 


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

Syd posted on Monday, July 26, 2010  4:21 am



Hi, Is there any way to calculate standardized coefficients when using MLR and TYPE=RANDOM? Thank you, 


No, we don't give them automatically. You can use MODEL CONSTRAINT to do this. 

yao lu posted on Tuesday, March 13, 2012  7:37 pm



Hi Linda, Following up your post above (July 26, 20107:40am),how can Model Constraint calculate standardized coefficients when using Type = Random? I read the Model Constraint section in the manual, but still not quite get it. Could you please provide me the coding for calculating standardized coefficients? Thank you very much. 


There is no single y variance with TYPE=RANDOM. Because of this, you cannot standardize with respect to y. See Example 5.20 to see how MODEL CONSTRAINT can be used for standardization. 


Dear Dr. Muthen, I am currently testing a crosslevel interaction. According to the discussions absolute statistic fits for those sorts of models (Type = Random;) are not available. You proposed to make sure the fit is good for the model without the interaction. Which part of the model do I need to change to test the same model without the interaction. This is my model: Variable: usevar = code S1 cs bo MG; cluster = code; within = cs; ! between = S1 MG; analysis: type = twolevel random; model: %within% beta1j  bo on cs; %between% bo Beta1j ON S1 MG; bo with beta1j; output: sampstat; Would it look like this... (1) (...) model: %within% bo on cs; %between% bo ON S1 MG; Or like this... (2)(...) model: %within% beta1j  bo on cs; %between% bo ON S1 MG; Thanks a lot for your help! 


You would not have a random slope specification, that is, not betaj ... You can create an interaction variable for the Within vbl times the Between vble and use that in the model. 


Dear Dr. Muthen, Thanks a lot for that hint! In this case the type of model would change from "twolevel random" into "twolevel", is that right? I not sure where to use the created interaction variable in the model. On within or between level? Thank you very much. 


Q1 Right Q2 in Define And it will vary on both levels. 

Sabrina Krys posted on Wednesday, November 01, 2017  6:35 am



Hi! I know that for Type=Random there is no standardized output available. However, I ran a moderated mediation analysis with several mediators and I want to build standardized main, direct, indirect, and interaction effects. I read Example 5.20, but it did not help to me. Is there any possibility to standardize the coefficients? Thanks! 


It is available in Version 8 using twolevel Bayes. See description in Hamaker, E.L., Asparouhov, T., Brose, A., Schmiedek, F. & Muthén, B. (2017). At the frontiers of modeling intensive longitudinal data: Dynamic structural equation models for the affective measurements from the COGITO study. Submitted to Multivariate Behavioral Research. 

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