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Hello, I'm reporting SEM results that were calculated with the MLR estimator. While this may seem trivial, I haven't been able to find what the MLR acronym stands for. I'd guess that it stands for "Maximum Likelihood Robust", but I want to ensure that I cite it properly. If you could please let me know, I'd appreciate it. Thanks! 


I don't think MLR is an acronym. It is an Mplus option for maximum likelihood estimation with robust standard errors. 


I am running a two level MLSEM. I have slightly nonnormal continuous data and from what I understand, using a SatorraBentler x2 with robust standard errors should be used. Mplus has this under the MLM estimator. However, in a two level analysis, MLM is not available, but MLR is. In the manual, MLR also provides robust standard errors. My question is: how is MLR related to MLM (in short how do I write this up aside from saying that I used a maximum likelihood estimator with robust standard errors)? 


MLM maximum likelihood parameter estimates with standard errors and a meanadjusted chisquare test statistic that are robust to nonnormality. The MLM chisquare test statistic is also referred to as the SatorraBentler chisquare. MLR maximum likelihood parameter estimates with standard errors and a chisquare test statistic (when applicable) that are robust to nonnormality and nonindependence of observations when used with TYPE=COMPLEX. The MLR standard errors are computed using a sandwich estimator. The MLR chisquare test statistic is asymptotically equivalent to the YuanBentler T2* test statistic. See the Yuan and Bentler paper referenced in the user's guide. MLR is an extension of MLM that can include missing data. 


Thanks for the info. I have a follow up question. I am using MPLUS 5.2 and it displays the twotailed p value how is it possible that in the unstandardized output it is nonsignificant (p>.05) and then in the standardized results, it is significant (p<.05)? I am modeling achievement (ACHW and ACHB) defined by reading and math at two levels (student and school level) and I am using the presence of basic facilities at the school level as a predictor (i.e., presence of electricity, 1=yes, 0=no). Unstd ACHB ON ELECTRIC 1.438 0.864 1.664 0.096 STDY Standardization ACHB ON ELECTRIC 1.185 0.585 2.028 0.043 StdYX ACHB ON ELECTRIC 0.488 0.241 2.027 0.043 Thank you. 


The unstandardized and standardized values have different sampling distributions and can give somewhat different z values. 


If that is the case, which one should be 'trusted' and interpreted? 


I would go with the tests for the unstandardized coefficients, but I haven't seen this studied. It could be a good methods research project, simulating data to see for which type of coefficient the z tests behave best at different sample sizes. 


I was just wondering, if you use mlr as the estimator method on a regression or path analysis is it still helpful to center explanatory variables? 


I don't see that the MLR choice and centering choice are related. 


Thanks for your quick reply. 


Hello, If you use the estimator MLR without using the Type=Complex option, can you still get standard errors that are robust to nonnormality and nonindepenence of observations? Thanks Wayne 


No, without TYPE=COMPLEX MLR is robust only to nonnormality. 


hello, is the type=complex option required in the case of missing data (mcar or mar) or not. Thanks alex 


All missing data estimation using maximum likelihood assumes MAR. 

Till posted on Tuesday, September 13, 2011  11:47 am



Dear Mrs. or Mr. Muthιn, I'm running a latent growth curve analysis. This is the Input: Variable: names= g1 e1 n1 g2 e2 n2 g3 e3 n3 l01 l02 l03 l04 l05 l06 l07 l08 l09; usevariable=all; missing=all(99); model: i s  l01@0 l02 l03 l04 l05@1 l06 l07 l08 l09; F1 by n1 n2 n3; F2 by e1 e2 e3; F3 by g1 g2 g3; i s on F1 F2 F3; Analysis: Estimator=MLR; output: samp standardized tech4; I would like to use the MLR estimator because the mardia coefficient shows me that I can't assume multivariate normal distribution for my data. Is the use of the MLR Estimator appropriate here or do I have to use the normal ML? Thank you in advance Till 


Dear Dr. Bengt and Dr. Linda In my model, I have 41 variables. 4 of them have kurtosis values > 3 (3.6, 3.6, 5.6 and 6.8). Do I need to run my model using MLM or MLV estimators? What is the rule of thumb to use the MLM/MLV instead of ML? What is the difference between MLM and MLV? Thanks 


There are three estimators that are robust to nonnormality. Following are brief descriptions. Only MLR is available with missing data. This is what I would recommend. MLM maximum likelihood parameter estimates with standard errors and a meanadjusted chisquare test statistic that are robust to nonnormality. The MLM chisquare test statistic is also referred to as the SatorraBentler chisquare. MLMV maximum likelihood parameter estimates with standard errors and a mean and varianceadjusted chisquare test statistic that are robust to nonnormality MLR maximum likelihood parameter estimates with standard errors and a chisquare test statistic (when applicable) that are robust to nonnormality and nonindependence of observations when used with TYPE=COMPLEX. The MLR standard errors are computed using a sandwich estimator. The MLR chisquare test statistic is asymptotically equivalent to the YuanBentler T2* test statistic. 

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