[R-meta] Effect coding in metafor
Yogev Kivity
yogev_k at yahoo.com
Thu Mar 1 03:56:00 CET 2018
Well, I see your point, but the main effect may still be informative, especially if the interaction is not a cross-over interaction. I guess I am thinking of it from an ANOVA-like perspective - I would first like to examine the main effects and interaction, and then proceed with follow-up analyses (i.e., simple effects and contrasts).
For example (a 2*2 design), in our meta-analysis we are examining whether the capacity to mentalize (to think about the mental states that underlie one's and others' behaviors) predicts various outcomes (usually in a Pearson's r metric). More specifically, we want to examine implicit and explicit measures of mentalizing as well as implicit and explicit measures of outcome. We are interested in both the main effects (whether implicit measures of mentalizing are better predictors of outcome than explicit measures; whether explicit measures of outcome are better predicted by mentlizing then implicit measures of outcome) as well as the interaction (if the advantage of implicit measurement of mentlizing is larger for implicit measures of outcome than for explicit measures of outcome). Thus, I was thinking that effect coding might me more appropriate than dummy coding for the first step of our analyses.
Best,Yogev
--
Yogev Kivity, Ph.D.
Postdoctoral Fellow
Department of Psychology
The Pennsylvania State University
Bruce V. Moore Building
University Park, PA 16802
Office Phone: (814) 867-2330
On Wednesday, February 28, 2018, 4:33:18 PM EST, Viechtbauer Wolfgang (SP) <wolfgang.viechtbauer at maastrichtuniversity.nl> wrote:
How is the main effect of relevance when there is an interaction?
But ok, leaving this aside, yes, the coefficient for 'testeraware' in model 'res.i1' is indeed the simple effect of the 'tester' factor for level 'none' of the 'weeks' factor.
So you want something different. Can you describe in words what this main effect you are looking for is supposed to reflect?
Best,
Wolfgang
>-----Original Message-----
>From: Yogev Kivity [mailto:yogev_k at yahoo.com]
>Sent: Wednesday, 28 February, 2018 22:14
>To: r-sig-meta-analysis at r-project.org; Viechtbauer Wolfgang (SP)
>Subject: Re: RE: RE: [R-meta] Effect coding in metafor
>
>Thanks!
>
>Sorry - I guess I should have given an example from a model with an
>interaction.
>
>And what if there is an interaction (model 'res.i1') and I am interested
>in the main effect? would I then need to change the coding?
>
>Best,
>Yogev
>
>--
>Yogev Kivity, Ph.D.
>Postdoctoral Fellow
>Department of Psychology
>The Pennsylvania State University
>Bruce V. Moore Building
>University Park, PA 16802
>Office Phone: (814) 867-2330
>
>On Wednesday, February 28, 2018, 3:21:39 PM EST, Viechtbauer Wolfgang
>(SP) <wolfgang.viechtbauer at maastrichtuniversity.nl> wrote:
>
>Yes, b3 = -.0511 is the main effect of the 'tester' factor. Since there
>is no interaction, the effect is the same for all three levels of the
>'weeks' factor, so it is not just the simple effect for the 'none' level,
>but also for the 'some' and 'high' levels. Look at the line plot again --
>it does not matter at which level of 'weeks' you look, the b3 = -.0511
>effect is always the same.
>
>Best,
>Wolfgang
>
>>-----Original Message-----
>>From: Yogev Kivity [mailto:yogev_k at yahoo.com]
>>Sent: Wednesday, 28 February, 2018 18:08
>>To: r-sig-meta-analysis at r-project.org; Viechtbauer Wolfgang (SP)
>>Subject: Re: RE: [R-meta] Effect coding in metafor
>>
>>Dear Wolfgang,
>>
>>Thanks for the link, this is very helpful in general!
>>However, I am still a bit confused about how to obtain estimates of a
>>main effect. In model ‘res.a1’ in the example in the link, would you
>>interpret the estimate of the ‘testeraware’ coefficient (b3 = -.0511) as
>>a test of the main effect of the ‘tester’ factor? Given that the ‘weeks’
>>factor is dummy coded, I thought that ‘testeraware’ represents the
>simple
>>effect of ‘tester’ within the ‘none’ level. If the latter is correct,
>>than I am looking for a way to test for a main effect (in this example,
>>the effect of ‘tester’ across the levels of ‘weeks’).
>>
>>Best,
>>Yogev
>>
>>--
>>Yogev Kivity, Ph.D.
>>Postdoctoral Fellow
>>Department of Psychology
>>The Pennsylvania State University
>>Bruce V. Moore Building
>>University Park, PA 16802
>>Office Phone: (814) 867-2330
>>
>>On Monday, February 26, 2018, 9:08:23 AM EST, Viechtbauer Wolfgang (SP)
>><wolfgang.viechtbauer at maastrichtuniversity.nl> wrote:
>>
>>Dear Yogev,
>>
>>Here is an extensive discussion of an example with 2 categorical
>>moderators and it happens to be the case that it also involves one
>>dichotomous and one 3 level factor:
>>
>>http://www.metafor-
>>project.org/doku.php/tips:multiple_factors_interactions
>>
>>I don't see why you need to change the coding.
>>
>>Best,
>>Wolfgang
>>
>>>-----Original Message-----
>>>From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces at r-
>>>project.org] On Behalf Of Yogev Kivity
>>>Sent: Monday, 26 February, 2018 4:38
>>>To: r-sig-meta-analysis at r-project.org
>>>Subject: [R-meta] Effect coding in metafor
>>
>>>
>>>Hi all,
>>>I am running a meta-analysis with 2 categorical moderators, one is
>>>dichotomous and the other has 3 levels (e.g. x1 and x2). For example:
>>>rma.mv(yi, V, mods=~factor(x1)*factor(x2), random = ~ 1 |
>>>StudyID/GroupID/EffectSizeID, data=DataF)
>>>(the specifics of the design of the meta-analysis seemed irrelevant to
>>>me, but if I am mistaken, I am happy to provide further details)
>>>As a first step, I am interested in the main effects of both moderators
>>>as well as the interaction effect. Therefore, to my understanding, I
>>need
>>>both moderators to be set as effect coding factors, rather then dummy
>>>coding factors. However, I could not figure out a way to do it in
>>>metafor. For example, setting the following contrasts did not work, and
>>>essentially the output remained identical to that of the dummy
>variables
>>>(i.e., each estimate expresses the simple effect of the moderator
>within
>>>the reference category of the other moderator):
>>>contrasts(Data$x1)<-c(-.5,.5)
>>>
>>>Obviously, I can get around the problem by creating multiple numeric
>>>variables with weights that correspond to the desired effect coding,
>but
>>>I was wondering whether I am missing something and whether there is a
>>>more elegant way of doing it.
>>>Best,Yogev
>>>--
>>>
>>>Yogev Kivity, Ph.D.
>>>Postdoctoral Fellow
>>>Department of Psychology
>>>The Pennsylvania State University
>>>Bruce V. Moore Building
>>>University Park, PA 16802
>>>Office Phone: (814) 867-2330
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