[Mediation-information] Appropriate use of package "mediation"
dtingley at gov.harvard.edu
Thu Sep 15 14:12:48 CEST 2011
I was referencing a point about the difference between a 0 to 1 change vs. a
3 to 4 change. This point is a basic one that applies any time you are using
probit etc. due to the functional form. I was not talking about comparing a
0 to 1 vs. 0 to 4 change. Hope this helps.
Have you read our papers on the topic? I think that would be helpful for you
it sounds like.
Best of luck with your project.
On Thu, Sep 15, 2011 at 3:29 AM, Tormod Bøe <tormod.boe at uni.no> wrote:
> Dear Kosuke and Dustin,
> Thanks for your prompt and thoughtful replies. In my case there is evidence
> of a linear "social gradient" in that symptom scores of outcome increases
> with decreasing levels of SES. However, the association with outcome is
> stronger in the most extreme categories of "treatment" (i.e. for "very poor"
> economy and for "basic" parental education levels).
> If I am understanding your responses correctly, this suggest that the
> results of the mediation analysis would be different for different levels of
> treatment (e.g. for comparing "poor"[=0] with "average"[=1] vs. "poor"[=0]
> with "very good"[=4] economy).
> Yours sincerely,
> On 15.09.2011 04:27, dustin tingley wrote:
>> Hi Tormod-
>> 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.
>> Dustin Tingley
>> Government Department
>> Harvard University
>> On Wed, Sep 14, 2011 at 10:19 PM, Kosuke Imai <kimai at princeton.edu
>> <mailto:kimai at princeton.edu>> wrote:
>> Hi Tormod,
>> 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.
>> Department of Politics
>> Princeton University
>> On Sep 14, 2011, at 8:24 AM, Tormod Bře wrote:
>> > Dear Associate Professor Imai,
>> > I am currently doing a PhD at the University of Bergen. We have
>> conducted a large-scale population based study of childhood mental
>> > 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.
>> > 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
>> > 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?
>> > 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.
>> > If you could please provide some advice in this manner it would
>> be greatly appreciated.
>> > Yours sincerely,
>> > Tormod Bře
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