<div>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).</div>
<div>Stan <br><br></div>
<div class="gmail_quote">On Tue, Sep 6, 2011 at 1:21 AM, Gregor Volberg <span dir="ltr"><<a href="mailto:Gregor.Volberg@psychologie.uni-regensburg.de">Gregor.Volberg@psychologie.uni-regensburg.de</a>></span> wrote:<br>
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<p style="MARGIN-TOP: 0px; MARGIN-BOTTOM: 0px"><font size="2" face="Dialog">Dear Nina,</font> </p><br>
<p style="MARGIN-TOP: 0px; MARGIN-BOTTOM: 0px"><font size="2" face="Dialog">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.</font> </p>
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<p style="MARGIN-TOP: 0px; MARGIN-BOTTOM: 0px"><font size="2" face="Dialog">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 </font></p>
<p style="MARGIN-TOP: 0px; MARGIN-BOTTOM: 0px"><font size="2" face="Dialog">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. </font></p>
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<p style="MARGIN-TOP: 0px; MARGIN-BOTTOM: 0px"><font size="2" face="Dialog">Best regards,</font> </p>
<p style="MARGIN-TOP: 0px; MARGIN-BOTTOM: 0px"><font size="2" face="Dialog">Gregor</font> </p>
<p style="MARGIN-TOP: 0px; MARGIN-BOTTOM: 0px"><br><br><font style="FONT-SIZE: 10pt" face="Dialog"><br>-- <br>Dr. rer. nat. Gregor Volberg <<a href="mailto:gregor.volberg@psychologie.uni-regensburg.de" target="_blank">gregor.volberg@psychologie.uni-regensburg.de</a>> ( mailto:<a href="mailto:gregor.volberg@psychologie.uni-regensburg.de" target="_blank">gregor.volberg@psychologie.uni-regensburg.de</a> ) <br>
University of Regensburg <br>Institute for Experimental Psychology <br>93040 Regensburg, Germany <br>Tel: <a href="tel:%2B49%20941%20943%203862" target="_blank" value="+499419433862">+49 941 943 3862</a> <br>Fax: <a href="tel:%2B49%20941%20943%203233" target="_blank" value="+499419433233">+49 941 943 3233</a> <br>
<a href="http://www.psychologie.uni-regensburg.de/Greenlee/team/volberg/volberg.html" target="_blank">http://www.psychologie.uni-regensburg.de/Greenlee/team/volberg/volberg.html </a><br><br><br></font>>>> Nina Kahlbrock <<a href="mailto:Nina.Kahlbrock@uni-duesseldorf.de" target="_blank">Nina.Kahlbrock@uni-duesseldorf.de</a>> 9/5/2011 2:45 PM >>><br>
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<p style="MARGIN-TOP: 0px; MARGIN-BOTTOM: 0px" class="MsoNormal"><span style="FONT-FAMILY: Arial; FONT-SIZE: 10pt" lang="EN-US"><font size="2" face="Arial">Dear statistic experts,<u></u><u></u></font></span> </p>
<p style="MARGIN-TOP: 0px; MARGIN-BOTTOM: 0px" class="MsoNormal"><span style="FONT-FAMILY: Arial; FONT-SIZE: 10pt" lang="EN-US"><font size="2" face="Arial"><u></u> <u></u></font></span> </p>
<p style="MARGIN-TOP: 0px; MARGIN-BOTTOM: 0px" class="MsoNormal"><span style="FONT-FAMILY: Arial; FONT-SIZE: 10pt" lang="EN-US"><font size="2" face="Arial">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.<u></u><u></u></font></span> </p>
<p style="MARGIN-TOP: 0px; MARGIN-BOTTOM: 0px" class="MsoNormal"><span style="FONT-FAMILY: Arial; FONT-SIZE: 10pt" lang="EN-US"><font size="2" face="Arial">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?<u></u><u></u></font></span> </p>
<p style="MARGIN-TOP: 0px; MARGIN-BOTTOM: 0px" class="MsoNormal"><span style="FONT-FAMILY: Arial; FONT-SIZE: 10pt" lang="EN-US"><font size="2" face="Arial"><u></u> <u></u></font></span> </p>
<p style="MARGIN-TOP: 0px; MARGIN-BOTTOM: 0px" class="MsoNormal"><span style="FONT-FAMILY: Arial; FONT-SIZE: 10pt" lang="EN-US"><font size="2" face="Arial">Thank you in advance for your answer.<u></u><u></u></font></span> </p>
<p style="MARGIN-TOP: 0px; MARGIN-BOTTOM: 0px" class="MsoNormal"><span style="FONT-FAMILY: Arial; FONT-SIZE: 10pt" lang="EN-US"><font size="2" face="Arial"><u></u> <u></u></font></span> </p>
<p style="MARGIN-TOP: 0px; MARGIN-BOTTOM: 0px" class="MsoNormal"><span style="FONT-FAMILY: Arial; FONT-SIZE: 10pt" lang="EN-US"><font size="2" face="Arial">Best Regards,<u></u><u></u></font></span> </p>
<p style="MARGIN-TOP: 0px; MARGIN-BOTTOM: 0px" class="MsoNormal"><span style="FONT-FAMILY: Arial; FONT-SIZE: 10pt" lang="EN-US"><font size="2" face="Arial"><u></u> <u></u></font></span> </p>
<p style="MARGIN-TOP: 0px; MARGIN-BOTTOM: 0px" class="MsoNormal"><span style="FONT-FAMILY: Arial; FONT-SIZE: 10pt" lang="EN-US"><font size="2" face="Arial">Nina<u></u><u></u></font></span> </p>
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<p style="MARGIN-TOP: 0px; MARGIN-BOTTOM: 0px"><span style="FONT-SIZE: 10pt" lang="EN-GB"><font size="2" face="Arial">- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -<u></u><u></u></font></span> </p>
<p style="MARGIN-TOP: 0px; MARGIN-BOTTOM: 0px"><span style="FONT-SIZE: 10pt" lang="EN-GB"><font size="2" face="Arial"><u></u> <u></u></font></span> </p>
<p style="MARGIN-TOP: 0px; MARGIN-BOTTOM: 0px"><span style="FONT-SIZE: 10pt" lang="EN-GB"><font size="2" face="Arial">Nina Kahlbrock<u></u><u></u></font></span> </p>
<p style="MARGIN-TOP: 0px; MARGIN-BOTTOM: 0px"><span style="FONT-SIZE: 10pt" lang="EN-GB"><font size="2" face="Arial">Institute of Clinical Neuroscience and Medical Psychology <u></u><u></u></font></span></p>
<p style="MARGIN-TOP: 0px; MARGIN-BOTTOM: 0px"><span style="FONT-SIZE: 10pt"><font size="2" face="Arial">Heinrich Heine University Duesseldorf<u></u><u></u></font></span> </p>
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