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Re: st: 3-way continuous interaction - comparing simple slopes


From   David Hoaglin <dchoaglin@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: 3-way continuous interaction - comparing simple slopes
Date   Tue, 21 May 2013 17:59:45 -0400

Dear Roman,

Is the nature of your y such that you could transform it (e.g., by
taking the logarithm or the square root) and have an analysis that has
fewer or even no interactions?

David Hoaglin

On Tue, May 21, 2013 at 2:58 PM, Roman Wörner <h0953997@wu.ac.at> wrote:
> Dear Statalister,
>
> I am a student and in my current research project I have to deal with a
> three-way continuous interaction. I found some material on the website of
> UCLA (http://www.ats.ucla.edu/stat/stata/faq/con3wayb.htm) where they
> explain how to use the -lincom- command to calculate simple slopes.
> Furthermore, they show how to use -lincom- to compare two simple slopes (I
> attached the code at the end of the message). This material was extremely
> helpful to me (how to put Aiken/West 1991 and Cohen et al. 2003 into
> practice).
>
>
> My question is as follows: to my understanding
>
> *lincom ($HzHw)-($HzLw)*
>
> tests whether the two slopes are different. What I actually want to test is
> not whether the two slopes are different but whether one slope is "steeper"
> than the other (my hypothesis is that two variables complement each other;
> with an increase in the third variable the complementarity effect becomes
> weaker). I would be very grateful for any advice on how to adapt the
> procedure as described down below or any other technique which does the
> trick.
>
> I am fairly new to STATA and statistics in general - so my appologies if
> this question sounds very basic to most of you.
>
> Thanks in advance and kind regards,
>
> Roman
>
> --
>
> use http://www.ats.ucla.edu/stat/stata/notes/hsb2, clear
>
> rename write y
> rename read x
> rename math w
> rename science z
> rename socst v1
> rename ses v2
>
> generate xz  = x*z
> generate xw  = x*w
> generate zw  = z*w
> generate xzw = x*z*w
>
> regress y x z w xz xw zw xzw v1 v2
>
> quietly sum x
> global hi=r(max)
> global lo=r(min)
> quietly sum w
> global Hw=r(mean)+r(sd)
> global Lw=r(mean)-r(sd)
> quietly sum z
> global Hz=r(mean)+r(sd)
> global Lz=r(mean)-r(sd)
> quietly sum v1
> global m1=r(mean)
> quietly sum v2
> global m2=r(mean)
>
> global HzHw "x + ($Hz)*xz + ($Hw)*xw + ($Hz)*($Hw)*xzw"
> global HzLw "x + ($Hz)*xz + ($Lw)*xw + ($Hz)*($Lw)*xzw"
> global LzHw "x + ($Lz)*xz + ($Hw)*xw + ($Lz)*($Hw)*xzw"
> global LzLw "x + ($Lz)*xz + ($Lw)*xw + ($Lz)*($Lw)*xzw"
>
> /* simple slopes */
> lincom $HzHw /* slope 1 */
> global b1 = r(estimate)
> lincom $HzLw /* slope 2 */
> global b2 = r(estimate)
> lincom $LzHw /* slope 3 */
> global b3 = r(estimate)
> lincom $LzLw /* slope 4 */
> global b4 = r(estimate)
>
> /* differences in simple slopes */
> lincom ($HzHw)-($HzLw)  /* a) HzHw vs HzLw */
> lincom ($HzHw)-($LzHw)  /* b) HzHw vs LzHw */
> lincom ($HzLw)-($LzLw)  /* c) HzLw vs LzLw */
> lincom ($LzHw)-($LzLw)  /* d) LzHw vs LzLw */
> lincom ($HzHw)-($LzLw)  /* e) HzHw vs LzLw */
> lincom ($HzLw)-($LzHw)  /* f) HzLw vs LzHw */*

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