MLR acronym? PreviousNext
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 Alyson Zalta posted on Friday, April 13, 2007 - 5:29 pm
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!
 Linda K. Muthen posted on Saturday, April 14, 2007 - 7:52 am
I don't think MLR is an acronym. It is an Mplus option for maximum likelihood estimation with robust standard errors.
 Francis Huang posted on Monday, March 23, 2009 - 4:05 pm
I am running a two level MLSEM. I have slightly nonnormal continuous data and from what I understand, using a Satorra-Bentler 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)?
 Linda K. Muthen posted on Monday, March 23, 2009 - 4:22 pm
MLM – maximum likelihood parameter estimates with standard errors and a mean-adjusted chi-square test statistic that are robust to non-normality. The MLM chi-square test statistic is also referred to as the Satorra-Bentler chi-square.

MLR – maximum likelihood parameter estimates with standard errors and a chi-square test statistic (when applicable) that are robust to non-normality and non-independence of observations when used with TYPE=COMPLEX. The MLR standard errors are computed using a sandwich estimator. The MLR chi-square test statistic is asymptotically equivalent to the Yuan-Bentler 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.
 Francis Huang posted on Tuesday, March 24, 2009 - 3:10 pm
Thanks for the info.

I have a follow up question. I am using MPLUS 5.2 and it displays the two-tailed 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.
 Bengt O. Muthen posted on Tuesday, March 24, 2009 - 7:19 pm
The unstandardized and standardized values have different sampling distributions and can give somewhat different z values.
 Francis Huang posted on Tuesday, March 24, 2009 - 8:01 pm
If that is the case, which one should be 'trusted' and interpreted?
 Bengt O. Muthen posted on Wednesday, March 25, 2009 - 8:36 am
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.
 Paul A.Tiffin posted on Thursday, April 08, 2010 - 12:35 pm
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?
 Bengt O. Muthen posted on Thursday, April 08, 2010 - 2:11 pm
I don't see that the MLR choice and centering choice are related.
 Paul A.Tiffin posted on Friday, April 09, 2010 - 12:22 am
Thanks for your quick reply.
 Wayne deRuiter posted on Thursday, July 22, 2010 - 7:31 pm
Hello,

If you use the estimator MLR without using the Type=Complex option, can you still get standard errors that are robust to non-normality and non-indepenence of observations?

Thanks
Wayne
 Linda K. Muthen posted on Friday, July 23, 2010 - 8:36 am
No, without TYPE=COMPLEX MLR is robust only to non-normality.
 Alexander Kapeller posted on Sunday, April 10, 2011 - 9:20 am
hello,

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

Thanks
alex
 Linda K. Muthen posted on Sunday, April 10, 2011 - 10:10 am
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
 Munajat Munajat posted on Monday, September 26, 2011 - 5:30 pm
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
 Linda K. Muthen posted on Monday, September 26, 2011 - 6:20 pm
There are three estimators that are robust to non-normality. 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 mean-adjusted chi-square test statistic that are robust to non-normality. The MLM chi-square test statistic is also referred to as the Satorra-Bentler chi-square.
• MLMV – maximum likelihood parameter estimates with standard errors and a mean- and variance-adjusted chi-square test statistic that are robust to non-normality
• MLR – maximum likelihood parameter estimates with standard errors and a chi-square test statistic (when applicable) that are robust to non-normality and non-independence of observations when used with TYPE=COMPLEX. The MLR standard errors are computed using a sandwich estimator. The MLR chi-square test statistic is asymptotically equivalent to the Yuan-Bentler T2* test statistic.
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