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Re: Re: st: binary mediation command


From   "Ariel Linden. DrPH" <ariel.linden@gmail.com>
To   <statalist@hsphsun2.harvard.edu>
Subject   Re: Re: st: binary mediation command
Date   Mon, 4 Jul 2011 10:04:16 -0700

See the following website for a better understanding of how you use and
interpret binary_mediation.

http://www.ats.ucla.edu/stat/stata/faq/binary_mediation.htm

Ariel

Date: Sun, 03 Jul 2011 15:44:09 -0400
From: Pina Valle <gv08d@fsu.edu>
Subject: Re: st: binary mediation command

Even when using percentile confidence intervals, is this output relevant
since I am trying to determine the proportion mediated by the indirect
effect?

Indirect effects with binary response variable evercoh
        indir_1 = .00601173        (adrel, binary)
total indirect  = .00601173
 direct effect  = .10223182
  total effect  = .10824356
       c_path   = .10680445
proportion of total effect mediated = .05553895 ratio of indirect to direct
effect  = .05880491 Binary models use logit regression

Thanks
Pina

- ----- Original Message -----
From: Philip Ender <ender97@gmail.com>
Date: Wednesday, June 15, 2011 4:21 pm
Subject: Re: st: binary mediation command
To: statalist@hsphsun2.harvard.edu

> Pina Valle wrote:
> 
> >I am trying to test mediation with a dichotomous outcome, and I
> have looked around and found a command in >STATA called 
> binary_mediation. However, there isn't really any indication in the 
> notes I found on whether the >mediation is significant. Here is an 
> example of my output along with the commands that I have used:
> >...
> 
> The documentation for -binary_mediation states:  This program does not 
> provide standard errors or statistical tests for coefficients.
> Bootstrap standard errors and confidence intervals are recommended for 
> this purpose.
> 
> Using either the percentile or bias corrected confidence intervals, 
> whichever you prefer, intervals that contain zero are not significant 
> at the stated level while those that do not contain zero are 
> significant.  Traditional statistical tests are likely to lead to 
> biased p-values.  I think the confidence interval approach is safer.
> 
> Phil
> --
> Phil Ender
> UCLA Statistical Consulting Group

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