[Mediation-information] Appropriate use of package "mediation"

[dustin tingley]

Tormod-
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.

best,
Dustin

Dustin Tingley
Government Department
Harvard University
http://scholar.harvard.edu/dtingley



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,
> Tormod
>
>
> 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.
>>
>> best,
>> dustin
>>
>>
>>
>> Dustin Tingley
>> Government Department
>> Harvard University
>> http://scholar.harvard.edu/**dtingley<http://scholar.harvard.edu/dtingley>
>>
>>
>>
>> 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.
>>
>>    Best,
>>    Kosuke
>>
>>    Department of Politics
>>    Princeton University
>>    http://imai.princeton.edu
>>
>>    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
>>    health.
>>     >
>>     > 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
>>    0-30).
>>     >
>>     > 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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