[R-meta] Welcome to the "R-sig-meta-analysis" mailing list
mail at awkruijt.nl
Mon Oct 16 21:26:48 CEST 2017
Thank you for clarifying. I’ll stick with sdi^2/ni for the metafor analysis. Part my confusion has to do with what I *really* am trying to figure out, namely how to best calculate/estimate the population sd, for use in a follow-up analysis, which I fear is beyond the scope of this list: Bayesfactor’s meta.ttest function allows specification of a null-interval defined in ‘standardized values’. Yet, I’ve managed to get myself utterly confused here and seemingly unable to figure out the/a calculation for which test-simulations give me full confidence that I am indeed specifying the exact boundary values that I wish to specify. If anyone has any good ideas, they are very welcome to me. I’ll take this issue back to the BayesFactor people too – thank you for helping out so quickly on the metafor-related question!
On 16/10/2017, 17:32, "Viechtbauer Wolfgang (SP)" <wolfgang.viechtbauer at maastrichtuniversity.nl> wrote:
You are mixing up two things:
1) The unbiased estimate of the variance is
var(x) = sum((x - mean(x))^2) / (n-1).
2) The variance of a mean is
var(x) / n
So, the correct computation is
vi <- sdi^2/ni
(assuming that sdi is the square-root of the unbiased estimate of the variance, but this is pretty much what is always reported).
From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces at r-project.org] On Behalf Of Anne-Wil Kruijt
Sent: Monday, 16 October, 2017 17:20
To: r-sig-meta-analysis at r-project.org
Subject: Re: [R-meta] Welcome to the "R-sig-meta-analysis" mailing list
Apologies, where I wrote ‘method = “MN”’, it should have been ‘measure = “MN”’.
On 16/10/2017, 17:18, "Anne-Wil Kruijt" <mail at awkruijt.nl> wrote:
Thank you, professor Viechtbauer, for referring me to this list.
Delving into metafor’s mechanics I noticed that when ‘method = “MN” ‘ is specified in escalc(), its calculation of sampling variance (vi) is “vi <- sdi^2/ni”. I wondered why there is no option to use the(/a?) unbiased estimator “vi <- sdi^2/ni-1”. I’m considered to bypass the escalc step and ‘manually’ compute vi as sdi^2/ni-1 – but I’m hesitant because it isn’t an option in escalc() (when method = “MN”, i.e. when obtaining the ‘raw mean difference’ for input as yi). Does anyone have any insights to share on why it is or is not a good idea to use the ‘unbiased estimator’ in the context of a REML MA on raw mean difference values?
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