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Date:         Thu, 8 Sep 2005 09:53:36 +0930
Reply-To:     kylie.lange@flinders.edu.au
Sender:       "SPSSX(r) Discussion" <SPSSX-L@LISTSERV.UGA.EDU>
From:         Kylie Lange <kylie.lange@flinders.edu.au>
Subject:      Re: About effect sizes
Comments: To: Karl Koch <TheRanger@gmx.net>
In-Reply-To:  <6803.1126102652@www6.gmx.net>
Content-Type: text/plain; charset=ISO-8859-1; format=flowed

Hi Karl,

Another reference you may want to check out:

Charles A. Pearce, Richard A. Block & Herman Aguinis, Cautionary Note on Reporting Eta-Squared Values from Multifactor ANOVA Designs. Educational and Psychological Measurement, Vol 64(6), pp 916-924, Dec 2004.

Kylie.

On 7/09/2005 11:47 PM, Karl Koch wrote:

>Hello Paul, > >thank you for the information. I will follow the links and have a look to >the tools as well. I will also see if I can hold of this book. I have >general problems with the libraries here in order to get fast and cheap >access to books of this magnitude. This is my mean reason, why I look for >papers and examples or documentens that are freely available (e.g. written >for tutorials at universities). > >My general problem here is really how to calculate effect size values. I >have already calculated partially eta squared (with SPSS), eta squared and >Cohen's f value for my design. However, I stuck at the point where I want to >know how much do factorial interactions affect the value of the main >effects. Also on the decision which effect size would be "best" (if this is >not a research question by itself)... > >I would like to make an example: > >I have three factors which are all significant. They all have non-trivial >(Cohen f / eta squared / partically eta squared) effect sizes (which is >good). However, I also have ongoing interactions between the effects. >Natuarally I wouild assume that I need to correct somehow those effects >sizes of the main effects in order to arrive at the real values that >represent the strengh of the factor. Those values would then allow to >estimate the impact of each of the three factors on the DV. I could then >say, for example, factor 1 was the strongest of my factors in terms of >increasing the score in my DV, factor 3 was second and factor 2 was third. >etc. > >What really could help here would be a paper that used a factorial design >(preferably with more than two levels and more than two factors, but not >necessary) that applied effect sizes. > >Kind Regards, > > > > > > > >>--- Ursprüngliche Nachricht --- >>Von: Paul Ginns <p.ginns@itl.usyd.edu.au> >>An: SPSSX-L@LISTSERV.UGA.EDU >>Betreff: Re: About effect sizes >>Datum: Tue, 6 Sep 2005 14:28:47 +1000 >> >>Hi Karl, >> >>a few suggestions for ANOVA effect sizes: >> >>Cohen's f is one effect size you can use, but there are others. The >>standardised mean difference (Cohen's d) can be scaled up to factorial >> >> >designs. > > >>Freeware which can be used to produce factorial d values, and confidence >>intervals around these d values, is available at >>http://www.psy.unsw.edu.au/research/PSY.htm and Kevin Bird has written a >> >> >book about this approach to > > >>ANOVA called >> >>Bird, K.D. (2004). Analysis of variance via confidence intervals. London: >>Sage Publications. >> >>Note that the above webpage also includes some SPSS syntax for calculating >>studentized maximum root product interaction contrasts. >> >>Another effect size is Epsilon squared. Jim Jaccard's Zumastat programme >>www.zumastat.com has a module that allows calcualtion of e-sq. and its CI >>using F values and degrees of freedom. (Note: I have no commercial >> >> >interest > > >>in Zumastat.) >> >>cheers, >> >>Paul >> >> >> >>Date: Mon, 5 Sep 2005 16:27:02 +0200 >>From: Karl Koch <TheRanger@gmx.net> >>Subject: About Effect Sizes... >> >>Hello group, >> >>I have done a 3x3x2 factorial experiement which has significant main >>effects >>and interactions. The DV was a score between 1 (not useful) and 6 (very >>useful) on 6 levels. To know more about the magnitude of the three >>factors, >>I was thinking of exploring the possibility to apply effect sizes. This >>would also spice up the experiment documentation. >> >>I found a, in my oppion good article in the Information Technology, >>Learning, and Performance Journal which is available online >>at: >> >> >> >> >http://education.nyu.edu/alt/beprogram/osrajournal/kotrlikwilliamsspring2003.pdf > > >>The article states that for Analysis of Variance (which applies to me) I >>should use Cohen's f effect size measure. Now my questions: >> >>1) Can somebody here confirm that or does somebody here know other books >>or >>articles (preferable also available online) which can help me to make a >>decent decision on that? I know that the entire topic is pretty strong >>discusses and some people disagree on what to choose. However, I am >>looking >>for a decent narrative what to use. Some examples (papers?) where people >>have used effect sizes in similar experiments would be helpful, too. >> >>2) I have not only significant main effects but also significant >>interactions. How does this influence the meaning of the effect size >>measures of the meain effects. >> >>3) If I want to find out the following: What is the most influcial factor >>(the one that influenced the DV most) ? Can I use the effect size measure >>to >>determine that e.g. by determining that by choosing the main effect with >>the >>greatest effect size? >> >>4) Is there any other way to rank the three factors according their >>importance (in terms of influencing the DV by increasing the score)? >> >>Any help would be greatly appreciated. >> >>Kind Regards, >>Karl >>


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