[FieldTrip] Antw: effect sizes in M/EEG data

[Stanley Klein]

Dear Nina,  Gregor is correct. A simple way to think of it is that effect
size (d) is the distance between two means divided by the standard deviation
of the data (like IQ). For determining significance one uses t, which is the
same difference of the means, but now divided by the standard error (the
standard deviation of the means).  SD doesn't depend on the number of
trials, but SE does. I often advocate reporting d plus its standard error.
That way one can avoid reporting an embarrassing naked statistic (a number
without its error bar).
Stan

On Tue, Sep 6, 2011 at 1:21 AM, Gregor Volberg <
Gregor.Volberg at psychologie.uni-regensburg.de> wrote:

>  Dear Nina,
>
> dependend samples t values can easily be transformed into Cohen's d as d =
> t / sqrt(df) [taken from Rosnow & Rosenthal, Effect sizes for Experimenting
> Psychologists, Canadian Journal of Experimental Psychology, 2003, 57:3,
> 221-237; you should find a pdf copy with a search on Google scholar]. For
> example, a t value of 5.15 with 19 degrees of freedom corresponds to a d of
> 5.15/sqrt(19) = 1.18. So just compute depsamplesT and divide the resulting
> vector or matrix by the number of dfs - no further FT functions are needed.
>
> My two cents are that effect sizes are especially useful for integrating
> results across studies where the very same design is applied to different
> samples, e.g., in clinical trials on pharmaceutical products. In EEG/MEG
> studies, even when they are on the same topic, there will be very divergent
> experimenting protocols (timing, task, etc), and also the dependent variable
> will differ from study to study,  depending on the chosen time points /
> frequencies / channels of interest. A common effect size metric like Cohen's
> d does not improve the comparibility much in this case. I would therefore
> simply report the t
>
> statistic along with the corresponding  df and p, and leave it for the
> interested reader to compute the corresponding d if there should be a need
> for that.
>
> Best regards,
>
> Gregor
>
>
>
>
> --
> Dr. rer. nat. Gregor Volberg <gregor.volberg at psychologie.uni-regensburg.de>
> ( mailto:gregor.volberg at psychologie.uni-regensburg.de )
> University of Regensburg
> Institute for Experimental Psychology
> 93040 Regensburg, Germany
> Tel: +49 941 943 3862
> Fax: +49 941 943 3233
> http://www.psychologie.uni-regensburg.de/Greenlee/team/volberg/volberg.html
>
>
>
> >>> Nina Kahlbrock <Nina.Kahlbrock at uni-duesseldorf.de> 9/5/2011 2:45 PM
> >>>
>
> Dear statistic experts,****
>
> ** **
>
> I have a rather general question concerning effect sizes. I understand that
> t-values give me an approximation of the effect size, as they state how big
> the difference between two conditions is by taking into account the variance
> and the number of observations. However, as I recall, effect sizes (like
> Cohen’s d) are computed in order to find out how important the effect is,
> independent of the number of observations. However, if I understand the code
> correctly, dependent samples t-values are computed in a very similar way as
> Cohen’s d (which is “the” value for effect sizes), both including the
> variance and thus the number of observations in their functions.****
>
> In order to draw conclusions about the importance of the observed effect,
> is it necessary to compute effect sizes like Cohen’s d or is it sufficient
> to compute t-values as effect sizes for M/EEG data? If important to compute
> effect sizes in a way different from t-values, does anyone know of a
> function that computes effect sizes like that in FT?****
>
> ** **
>
> Thank you in advance for your answer.****
>
> ** **
>
> Best Regards,****
>
> ** **
>
> Nina****
>
> ** **
>
> - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
> - - - - - - - - - - - - - - - - - - - -****
>
> ** **
>
> Nina Kahlbrock****
>
> Institute of Clinical Neuroscience and Medical Psychology ****
>
> Heinrich Heine University Duesseldorf****
>
> Universitaetsstr.  1****
>
> 40225  Düsseldorf****
>
> ** **
>
> Tel.:      +49 211 81 18075****
>
> Fax. .:   +49 211 81 19916****
>
> ** **
>
> Mail:     Nina.Kahlbrock at med.uni-duesseldorf.de****
>
> http://www.uniklinik-duesseldorf.de/medpsychologie****
>
> ** **
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