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

[Gregor Volberg]

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 
   
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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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