If you are using WLSMV, you are not getting a logistic regression coefficient. You are getting a probit regression coefficient which cannot be exponentiated. Ask for a maximum likelihood estimator to obtain logistic regression.
gibbon lab posted on Tuesday, December 11, 2012 - 10:23 am
Thanks for the prompt response. I tried to specify "ESTIMATOR=MLR;" as in the following
Analysis: TYPE =complex; REPSE=JACKKNIFE1; ESTIMATOR=MLR;
But the output still gave me WLSMV estimator. I also tried "ESTIMATOR=ML;", it gave me WLSMV too. Is there any other option to force the program generating MLE? Or only WLSMV is available for replicated weights?
By the way, can you give guidance on how to interpret the probit regression coefficient? Is there a way to convert it to an odds ratio?
Maximum likelihood is available with replicate weights only with continuous outcomes.
See the Topic 2 course handout and video on the website where probit regression is discussed. Probit regressions coefficients cannot be converted to an odds ratio.
gibbon lab posted on Tuesday, December 11, 2012 - 2:03 pm
I tried to use TAYLOR method this time for estimating the variance. My code is
DATA: FILE IS D:\lzg\Adsdata\alcohol\spss_alc.dat;
VARIABLE: NAMES ARE finedwt0 ... gender10;
categorical are moresip_any;
USEVARIABLES ARE moresip_any scalealctrim2;
MISSING ARE ALL (9999);
Analysis: TYPE =complex; ESTIMATOR=MLR;
MODEL: moresip_any on scalealctrim2;
This time I got an Odds Ratio (2.29). But it is still not the same as what I got in SAS (1.20) using the same model. Was something wrong with my coding? The output did not give any error message. Thanks.
Rescaling the probit coefficient does not change its properties. It is the properties of the logistic coefficient that make it able to be exponentiated in a meaningful way. I think you would have a difficult time getting this past a reviewer. In the past, this rescaling was used to put the probit in a logit metric. To see that it is approximate, do a logistic and probit regression. Scale the probit coefficient and exponentiate the logistic and rescaled probit coefficients and see how close the values are.