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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. 


1) Regarding scale setting, can you use the fixed factor method (set latent variances to 1) when using the WLSMV estimator? If so, is it appropriate to interpret either the raw or STDYX estimates (since they should be the same)? 2) I ran the same model (with ordinal indicators) using WLSMV to get fitstatistics and then with MLR to get BIC to compare nonnested models and used the fixed factor method. I'm not sure why I am getting such different parameter estimates when using the MLR estimator (they are even different in terms of significance and directionality!). For example, here is some WLSMV output: Estimate S.E. Est./S.E. PValue EFFICACY BY E01 0.623 0.054 11.497 0.000 ... SATISFAC BY S01 0.027 0.067 0.405 0.686 ... EFFICACY WITH SATISFACTI 0.854 0.066 12.931 0.000 And here is some MLR output: Estimate S.E. Est./S.E. PValue EFFICACY BY E01 1.507 0.347 4.340 0.000 ... SATISFAC BY S01 1.227 0.388 3.163 0.002 ... EFFICACY WITH SATISFACTI 0.002 0.253 0.009 0.993 


1) I assume you have a categorical DV and the default Delta parameterization of WLSMV in which case the DV variance is 1. If so, the answer to your question is yes. 2) First check that your 2 runs have the same number of parameters. Then not that WLSMV uses probit and ML uses logit by default (but can use link=probit). Note also that if all loadings of a factor are negative you can switch them all to positive. 

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