ML and WLSMV estimation with categori... PreviousNext
Mplus Discussion > Categorical Data Modeling >
 Keith Widaman posted on Tuesday, August 29, 2017 - 5:14 pm
A colleague used ML with a path model with 5 MVs, 2 of which were categorical. WLSMV is preferred for such models. Sample size was large (1198).

Under ML:

AGED 0.048 0.010 4.643
NP 0.812 0.086 9.488
TDP 0.388 0.084 4.618
HS 1.038 0.280 3.714

Under MLR:

AGED 0.048 0.010 4.773
NP 0.812 0.084 9.704
TDP 0.388 0.085 4.575
HS 1.038 0.282 3.685

Under WLSMV:

AGED 0.025 0.007 3.299
NP 0.518 0.057 9.166
TDP 0.037 0.075 0.489
HS 0.431 0.088 4.903

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?
 Bengt O. Muthen posted on Wednesday, August 30, 2017 - 4:07 pm
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.
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