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<p>That makes perfect sense, thank you! How would I interpret the
same analysis for a continuous moderator?</p>
<pre tabindex="0" class="GNKRCKGCGSB" id="rstudio_console_output" style="font-family: 'Lucida Console'; font-size: 10pt !important; outline: none; border: none; word-break: break-all; margin: 0px; -webkit-user-select: text; white-space: pre-wrap !important; line-height: 9.615385055541992px; color: rgb(0, 0, 0); font-style: normal; font-variant: normal; font-weight: normal; letter-spacing: normal; orphans: auto; text-align: -webkit-left; text-indent: 0px; text-transform: none; widows: auto; word-spacing: 0px; -webkit-text-stroke-width: 0px; background-color: rgb(255, 255, 255);"><span><span class="GNKRCKGCGSB" style="outline: none; border: none; word-break: break-all; margin: 0px; -webkit-user-select: text; white-space: pre-wrap !important;"> estimate se zval pval ci.lb ci.ub
var1var2X.Y 0.6192 0.0670 9.2463 <.0001 0.4879 0.7504 ***
var1var2X.Z -0.0181 0.1013 -0.1789 0.8580 -0.2166 0.1803
var1var2Y.Z -0.3017 0.1149 -2.6258 0.0086 -0.5270 -0.0765 **
M 0.0001 0.0001 0.8208 0.4118 -0.0001 0.0003
var1var2X.Z:M -0.0004 0.0002 -1.6019 0.1092 -0.0008 0.0001
var1var2Y.Z:M -0.0001 0.0002 -0.4270 0.6694 -0.0006 0.0004 </span></span>
<span><span class="GNKRCKGCGSB" style="outline: none; border: none; word-break: break-all; margin: 0px; -webkit-user-select: text; white-space: pre-wrap !important;"></span></span></pre>
<span><span class="GNKRCKGCGSB" style="outline: none; border: none; word-break: break-all; margin: 0px; -webkit-user-select: text; white-space: pre-wrap !important;"></span></span><br>
<span><span class="GNKRCKGCGSB" style="outline: none; border: none; word-break: break-all; margin: 0px; -webkit-user-select: text; white-space: pre-wrap !important;"><font face="Helvetica, Arial, sans-serif">I guess this result implies that for each SD (guessing from multi-level analyses here) of the mediator, the association between X and Z get slightly more negative than the association between Y and Z. Is there a way I can test this three-way interaction for significance in the case of a continuous moderator?
I'm also wondering if I should include all of my relevant moderators in a single analyses or conduct seperate ones. Interpretation is easier if I look at them separately, but including all in one analyses might be clearer regarding confounding effects? I also don't have that many studies in the analyses (21), so I'm not really sure how many factors I could include in one analysis. What's best practice here?
Thanks,
Anna-Lena
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<div class="moz-cite-prefix">Am 05.10.2018 um 17:48 schrieb
Viechtbauer, Wolfgang (SP):<br>
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<blockquote type="cite"
cite="mid:617a8a92aacc42aaa04a7448bc22d1ef@UM-MAIL3214.unimaas.nl">
<pre wrap="">The first output is more difficult to interpret, so I'll go with the second.
You want to test:
H0: X.Z:M0 - Y.Z:M0 = X.Z:M1 - Y.Z:M1,
which is equivalent to:
H0: X.Z:M0 - Y.Z:M0 - X.Z:M1 + Y.Z:M1 = 0
So, you want to use:
anova(res(0,1,-1,0,-1,1))
And indeed, this is testing if the difference between cor(X,Y) and cor(X,Z) is moderated by M (i.e., is different for M0 vs M1).
Best,
Wolfgang
-----Original Message-----
From: Anna-Lena Schubert [<a class="moz-txt-link-freetext" href="mailto:anna-lena.schubert@psychologie.uni-heidelberg.de">mailto:anna-lena.schubert@psychologie.uni-heidelberg.de</a>]
Sent: Monday, 01 October, 2018 14:47
To: Viechtbauer, Wolfgang (SP); <a class="moz-txt-link-abbreviated" href="mailto:r-sig-meta-analysis@r-project.org">r-sig-meta-analysis@r-project.org</a>
Subject: Re: [R-meta] Comparing dependent, overlapping correlation coefficients
I have recently tried to finish all analyses and have realized that I still haven't fully understood how moderation works here. In the example below, I'm interested in whether the difference between cor(X,Y) and cor(X,Z) is moderated by some factor. From my reading of the metafor examples, var1var2X.Z:M should indicate whether the cor(X,Z) is moderated by M. However, Wolfgang says that term instead describes a moderation of the difference between correlations. I'm perfectly happy to believe him, but I would really like to understand why this is the case.
In my empirical example the results look as follows:
estimate se zval pval ci.lb ci.ub
var1var2X.Y 0.6580 0.0218 30.1818 <.0001 0.6152 0.7007 ***
var1var2X.Z -0.1282 0.0468 -2.7420 0.0061 -0.2199 -0.0366 **
var1var2Y.Z -0.2896 0.0480 -6.0315 <.0001 -0.3837 -0.1955 ***
M1 0.0736 0.0301 2.4474 0.0144 0.0147 0.1326 *
var1var2X.Z:M1 -0.1648 0.0786 -2.0969 0.0360 -0.3188 -0.0108 *
var1var2Y.Z:M1 -0.1266 0.0845 -1.4983 0.1340 -0.2922 0.0390
Here I'm interested in the difference between cor(x,z) and cor(y,z). Which of these interaction terms is critical to the moderation of this correlation, and why? Also, how can I interpret the direction of the effect?
If I look at another display of the same results, I get an understanding of the moderation, but I'm not sure about a clear test here. Would anova(res(0,1,-1,0,1,-1)) be a sensible test? What I'd really want to test would be if X.Z:M0 - Y.Z:M0 = X.Z:M1 - Y.Z:M1
estimate se zval pval ci.lb ci.ub
var1var2X.Y:M0 0.6580 0.0218 30.1818 <.0001 0.6152 0.7007 ***
var1var2X.Z:M0 -0.1282 0.0468 -2.7420 0.0061 -0.2199 -0.0366 **
var1var2Y.Z:M0 -0.2896 0.0480 -6.0315 <.0001 -0.3837 -0.1955 ***
var1var2X.Y:M1 0.7316 0.0207 35.3007 <.0001 0.6910 0.7722 ***
var1var2X.Z:M1 -0.2194 0.0483 -4.5437 <.0001 -0.3140 -0.1248 ***
var1var2Y.Z:M1 -0.3426 0.0491 -6.9767 <.0001 -0.4388 -0.2463 ***
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<h3 class="auto-style2">Dr. Anna-Lena Schubert</h3>
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<pre style="line-height: 1;">Hauptstraße 47-51
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<pre style="line-height: 1;">Phone: +49 6221 54 7746
Mail: <a class="moz-txt-link-abbreviated" href="mailto:anna-lena.schubert@psychologie.uni-heidelberg.de">anna-lena.schubert@psychologie.uni-heidelberg.de</a>
Web: <a class="moz-txt-link-freetext" href="http://www.psychologie.uni-heidelberg.de/ae/diff/diff/people-schubert.html">http://www.psychologie.uni-heidelberg.de/ae/diff/diff/people-schubert.html</a></pre>
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