MLR to handle both missing and non-no... PreviousNext
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 Shu Fai CHEUNG posted on Saturday, November 03, 2012 - 10:58 am
I read the user's guide and it seems that MLR in Mplus 7 can handle both missing data and non-normal data. Am I correct? My understanding is that ML solution to missing data usually assumes a multivariate normal distribution. If MLR in Mplus can handle both missing data and non-normal data, does it mean that MLR, though still assumes multivariate normal distribution, makes some adjustments such that even though the distribution is not so normal, it's results are robust to the deviation? If I want to know how robust MLR as implemented in Mplus is, are there simulation studies on its performance when both missing data and non-normal data are present in a dataset, for various degrees of deviation from multivariate normal distribution? I would like to know to what extent I can trust the MLR results for my dataset. Thanks.
 Bengt O. Muthen posted on Sunday, November 04, 2012 - 11:40 am
Missing data modeling does indeed assume normality. MLR robustness does not necessarily hold for the combination of missing data and non-normality. There have been simulation studies of this combination - see articles by Victoria Savalei in the SEM journal for instance (e.g. her 2010 article). See also Yuan et al (2012) in a recent issue of Sociological Methods & Research.
 Shu Fai CHEUNG posted on Sunday, November 04, 2012 - 7:14 pm
Thanks for your prompt reply and the references. I am studying them. I am also interested in the technical details. May I know whether the section on MLR in the following web note in 2002 on MLR is still relevant to the Mplus 7?

Muthén, B. and Asparouhov, T. (2002). Using Mplus Monte Carlo simulations in practice: A note on non-normal missing data in latent variable models. Version 2, March 22, 2002.
 Bengt O. Muthen posted on Monday, November 05, 2012 - 1:09 pm
I forgot about that one. Section 5 is certainly relevant, showing that MLR can work quite well. So no theoretical guarantee, but it can do well in practice. So yes, still relevant for Version 7.
 Barbara O. posted on Monday, March 18, 2013 - 11:32 am
I am working on a paper examining longitudinal pathways predicting perpetration of dating violence in young adulthood. I'm using path analyses in mplus, accounting for the fact that dating violence is censored. There are three outliers in my dating violence variable. If I get rid of outliers (either by transforming or trimming), I get the same pattern of results regardless of whether I:
(a) use MLR (even after transformation the variable has high kurtosis),
(b) use ML without robust estimation,
(c) don't account for censoring at all and just use ML.
Thus, my results seem consistent. However, if I don't remove the outliers, and just use MLR to account for the non-normal data, my pattern of results changes.

Does MLR not take care of outlier problems?
 Linda K. Muthen posted on Monday, March 18, 2013 - 1:35 pm
MLR does not take care of outliers.
 John Plake posted on Thursday, September 19, 2013 - 10:06 am
I am trying to track down some documentation of the limits of MLM or MLR estimators in handling extremely non-normal survey data, with and without MAR or MCAR data.

In a recent pilot study with N = 65, item-level skew was common, with one extreme example showing skew = -8.307 and kurtosis = 69.000. Scale-level non-normality was -1.926 (skew) and 4.86 (kurtosis) in the worst case.

Yuan and Bentler (2000) demonstrated that their approach (is it MLM in Mplus?) handled skew of 4.18 and kurtosis of 62.55 fairly well in MCAR.

Can you help me know if the limits have been tested?
 Bengt O. Muthen posted on Thursday, September 19, 2013 - 10:27 am
Vika Savalei had a 2101 SEM article on that. Use MLR in Mplus.
 John Plake posted on Thursday, September 19, 2013 - 11:04 am
Great. Thanks!
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