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 Anonymous posted on Monday, March 19, 2012 - 11:17 am
I've got a few questions about the MLR output:

The Mplus output for MLR includes, if I am correct,

Regression coefficient estimates using maximum likelihood estimation
Robust standard errors computed with Huber-White 'sandwich' estimator
Robust chi-square test of model fit using an extension of the Yuan-Bentler T2 test statistic
MLR uses Full Information Maximum Likelihood Estimation to handle missing data
Hypothesis is tested by computing the ratio of the estimate by its standard error ("Est./S.E."), and corresponding p-value. If I am correct, this would be my t-test/z-test?

If if this my t-test/z-test, would it be more appropriate to report as a t-test or a z-test (n = 326)?

Does this seem accurate, and is there anything I should probably know from this output?

Also, just to clarify, does this method still count as 'multiple linear regression'? I saw someone commented in the forum saying that it used logistic regression, so now I am doubting I should using this in the first place.

I would appreciate clarification/confirmation on this, please.

Thanks in advance,

JL
 Linda K. Muthen posted on Monday, March 19, 2012 - 1:29 pm
The ratio of the parameter estimate to its standard error is a z-test in large samples.

MLR estimates linear regression if the dependent variable is continuous and logistic regression if the dependent variable is categorical.
 Anonymous posted on Monday, March 19, 2012 - 4:43 pm
Ah I see, that's genius (how it knows which one to use). z-test it is then.

So it is safe to say I would still be able this regression equation, Y = i + aX + bM + cXM + E?
 Linda K. Muthen posted on Monday, March 19, 2012 - 5:00 pm
Yes.
 Michael posted on Tuesday, May 29, 2012 - 4:29 am
Dear Professor/s,

Im new to Mplus. I have use the MLR-estimator, because of the non-normality in the indicator-variables.
Now Im trying to find some guilines for the cutoff criterias for the fit indexes. Is it ok to use the suffestions from "Hu & Betnler (1999). Cutoff Criteria for Fit Indexes in Covariance Structure Analysis: Conventional Criteria Versus New Alternatives, SEM 6(1), 1-55". They appear to be only for the ML-Estimator.
In other words: Are the recommendations for the cutoff criterias for ML-estimator the same as for MLR?

Many Thanks in advance

Michael
 Linda K. Muthen posted on Tuesday, May 29, 2012 - 8:39 am
I don't believe there have been any studies specific to MLR. The Hu and Bentler cutoffs are probably the best you can do.
 Michael posted on Tuesday, May 29, 2012 - 10:46 am
Thank you very much for the quick response!
 Marketa Krenek  posted on Sunday, March 09, 2014 - 8:55 am
Hi there,

I'm running a series of Bivariate latent change score models and am wondering whether to use ml or mlr estimation. My n=200. My dependent variables are non normally distributed (skewness around 2-3, kurtosis between 2 and 13) and data are MAR (12 missing data patterns, lowest covariance coverage is .861 ). Is mlr recommended in this case? Or is ml adequate?

Thank you!
 Linda K. Muthen posted on Sunday, March 09, 2014 - 3:32 pm
I would use MLR. It is robust to non-normality. ML is not.
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