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? ª
ª 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.
I´m new to Mplus. I have use the MLR-estimator, because of the non-normality in the indicator-variables. Now I´m 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?
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?