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| anonymous posted on Friday, January 17, 2003 - 2:00 pm
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| In Mplus, only complete cases may be used in order to utilize robust standard errors. Is it appropriate to estimate robust standard errors across multiply imputed datasets? |
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| There are three estimators available for TYPE=MISSING in Mplus: ML, MLR, and MLF. The MLR estimator gives robust standard errors and the chi-square test statistic referred to as the Yuan-Bentler T2* test statistic. This is documented in the Addendum to the Mplus User's Guide which is available at www.statmodel.com under Product Support. |
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Hi Dr. Muthen
I am using 5 imputed data sets to do a 2class mixture model with an interaction term. i have a fairly small n = 48. is this trustworthy at any possibility or simply just not adequate.
2nd question belonging to the same model(and to the topic): the standard error in the model output for one parameter eg. 64 don't fit to the acov matr. in the output for any of the 5 imputations. here the var of the param = 20. is there one true or both not reliable.
thanks
Alex |
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n=48 is rather small, unless your 2 classes are very well separated, such as means at least 2 SDs apart. With your missing data, this problem is compounded.
SEs are computed using a "within-between" imputation formula given in books like Schafer's, so not only using the SE for a particular imputation run. |
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