The most obvious reason for choosing one over the other would be the kind of fit indices you need. The WLSMV will give you CFI, TLI and RMSEA, which will help you evaluate the fit of a given model. If you need to compare non-nested models, you would need AIC and/or BIC, which aren't available with WLSMV and categorical data. The opposite is true of ML (again, only when dealing with categorical data).

I'm not sure why they recommend WLSMV on the Mplus website, but if you are comparing nested models, the WLSMV is probably the most convenient as it will allow you to both (1) evalute whether the models provide adequate fit to the data (e.g. CFI > .90 and RMSEA < .5), and (2) use a χ2 difference test to see which models provides the best fit out of a number of competing models.

The most obvious reason for choosing one over the other would be the kind of fit indices you need. The WLSMV will give you CFI, TLI and RMSEA, which will help you evaluate the fit of a given model. If you need to compare non-nested models, you would need AIC and/or BIC, which aren't available with WLSMV and categorical data. The opposite is true of ML (again, only when dealing with categorical data).

I'm not sure why they recommend WLSMV on the Mplus website, but if you are comparing nested models, the WLSMV is probably the most convenient as it will allow you to both (1) evalute whether the models provide adequate fit to the data (e.g. CFI > .90 and RMSEA < .5), and (2) use a chi2 difference test to see which models provides the best fit out of a number of competing models.

The most obvious reason for choosing one over the other would be the kind of fit indices you need. The WLSMV will give you CFI, TLI and RMSEA, which will help you evaluate the fit of a given model. If you need to compare non-nested models, you would need AIC and/or BIC, which aren't available with WLSMV and categorical data. The opposite is true of ML.

The most obvious reason for choosing one over the other would be the kind of fit indices you need. The WLSMV will give you CFI, TLI and RMSEA, which will help you evaluate the fit of a given model. If you need to compare non-nested models, you would need AIC and/or BIC, which aren't available with WLSMV and categorical data. The opposite is true of ML (again, only when dealing with categorical data).

I'm not sure why they recommend WLSMV on the Mplus website, but if you are comparing nested models, the WLSMV is probably the most convenient as it will allow you to both (1) evalute whether the models provide adequate fit to the data (e.g. CFI > .90 and RMSEA < .5), and (2) use a χ2 difference test to see which models provides the best fit out of a number of competing models.