Hi Tormod-<div><br></div><div>To follow up, we do allow you to set two values of the treatment other than 0 or 1. Make sure you're using the latest version of the package (3.1), in R 2.13. As Kosuke mentions, it won't matter when everything is linear, but when things are non-linear, then the ACME from 0 to 1 might different from a 3 to 4 change. This is typical of any time you use probit/logit etc. due to the nature of the response function in those models. Also, the sensitivity analysis, is only worked out for the t=0 and t=1 case. Note that things like MPlus won't calculate sensitivity analyses...Keep in mind the strong assumptions you are making when conducting mediation analysis. For us, that means running a sensitivity analysis if possible, or thinking about alternative research designs.</div>
<div><br></div><div>best,</div><div>dustin</div><div><br></div><div><br></div><div><br clear="all">Dustin Tingley<br>Government Department<br>Harvard University<br><a href="http://scholar.harvard.edu/dtingley" target="_blank">http://scholar.harvard.edu/dtingley</a><br>
<br>
<br><br><div class="gmail_quote">On Wed, Sep 14, 2011 at 10:19 PM, Kosuke Imai <span dir="ltr"><<a href="mailto:kimai@princeton.edu">kimai@princeton.edu</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex;">
Hi Tormod,<br>
<br>
I'm ccing the listserv we have so that my collaborators and others can give additional insights. The mediation software doesn't have the functionality to fully accomodate the continuous treatment. However, when the software will calculate the average causal mediation effect of changing the treatment from 0 to 1. If you use a linear model for both mediator and outcome, then this should be sufficient because the ACME would not change regardless of the base value you choose for the treatment. If you have a nonlinear model, however, the ACME you get is still valid but only for the scenario where you change the treatment value from 0 to 1. If you are thinking about different scenario (e.g., changing the treatment from 3 to 4), then you will get a different answer.<br>
<br>
Best,<br>
Kosuke<br>
<br>
Department of Politics<br>
Princeton University<br>
<a href="http://imai.princeton.edu" target="_blank">http://imai.princeton.edu</a><br>
<br>
On Sep 14, 2011, at 8:24 AM, Tormod Bøe wrote:<br>
<br>
> Dear Associate Professor Imai,<br>
><br>
> I am currently doing a PhD at the University of Bergen. We have conducted a large-scale population based study of childhood mental health.<br>
><br>
> I am in the process of writing at paper where we investigate to which extent sleep problems mediate the association between parental socioeconomic status and children's mental health problems.<br>
><br>
> We have two sleep measures (one rated on a three point scale, and two that are binary[0=adequate sleep time, 1=long/short sleep time]), parental SES is continuous (years of education and family income rated on five point scales) and outcome is continuous (range 0-30).<br>
><br>
> I have made some attempts at running mediation analyses using your R package mediation. I have made one model m where I regress family SES on sleep problems, and model y where I regress family SES and sleep problems on mental health problems outcome. I use sleep problems as "mediator" and have used SES variables (e.g. family economy) as treatment variables. However, is this an appropriate use of your package?<br>
><br>
> I am especially concerned with regards to the use of a continuous "treatment" variable. The models run without any errors, and the pattern of results (with regards to significant interactions) resemble those I obtain when running path analyses in Mplus.<br>
><br>
> If you could please provide some advice in this manner it would be greatly appreciated.<br>
><br>
> Yours sincerely,<br>
> Tormod Bøe<br>
<br>
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