[R-meta] Welcome to the "R-sig-meta-analysis" mailing list

[Anne-Wil Kruijt]

Dear all,

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! 

All best,

Anne-Wil

On 16/10/2017, 17:32, "Viechtbauer Wolfgang (SP)" <wolfgang.viechtbauer at maastrichtuniversity.nl> wrote:

    Dear Anne-Wil,
    
    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).
    
    Best,
    Wolfgang
    
    -----Original Message-----
    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:
    
        Dear all,
            
        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? 
        
        All best,
        
        Anne-Wil