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I have a very simple regression with a categorical outcome. ML and WLSMV give very different estimates. How should I interpret them? ML estimates of the predictor Estimates 0.513 S.E. 0.249 StdYX 0.272 Thresholds Outcome$1 2.049 Why is StdYX differ from the Estimates? WLSMV estimates (same model) Estimates 0.283 S.E. 0.143 StdYX 0.274 Why is the WLSMV estimate (0.283) so different from the ML estimate (0.513)? Thanks in advance. 


In Mplus, the default for maximum likelihood is logistic regression and the default for weighted least square is probit regression. These differ by a scale of approximately 1.7. 


Hi, Linda, thanks for the response. Now I remember reading about WLSMV and probit model somewhere. Could you also explain why stdYX is different from the estimate? I read that they are different only when there are multiple dependent variables. In this simple model, there is only one dependent variable. Thanks 


As long as the variances of y and x are not one, STDYX will be different that the raw parameter estimate for both univariate and multivariate regression. 

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