So, under ML, MLR, and WLSMV, AGED, NP, and HS had effects with similar z ratios.
But, the effect of TDP on DEMENTIA differed tremendously. Under ML and MLR, TDP had a robust direct effect with z around 4.60 and p < .0001. Under WLSMV estimation, TDP had a direct effect with z of 0.49 and p = .625, so not close to significance.
Any ideas about why results for the direct effect of TDP varied so much across ML, MLR versus WLSMV?
Hard to say without seeing the full output (send to Support). Perhaps TDP was binary and perhaps it was declared as categorical in which case ML(R) uses the observed TDP version as the predictor whereas WLSMV uses the latent continuous response variable behind the observed.
I have a similar question. I have a binary outcome, which is specified as categorical in the inputfile. However, using MLR or WLSMV produces very different results. I am using multiply imputed data so the difference could not be lying in the fact that MLR and WLSMV handle missing data differently.
How can I decide which estimator I should be using? I am running a twolevel regession with only two predictors.