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Date:         Thu, 8 Sep 2005 09:10:05 -0600
Reply-To:     Matthew Pirritano <>
Sender:       "SPSSX(r) Discussion" <SPSSX-L@LISTSERV.UGA.EDU>
From:         Matthew Pirritano <>
Subject:      comparing ranks
In-Reply-To:  <>
Content-Type: text/plain; charset=ISO-8859-1; format=flowed

Hello all,

A colleague has data in which she has asked participants to rank order 10 items at two different points in time. She wants to look at the correlation between the two sets of ranks.

I'm thinking that the best way to do this is to have one column for subject id number. Each subject has 10 rows, 1 for each rank. There are two more columns. One column for each time point. With this data a Spearman correlation could be calcuated between the two columns of ranks..

Is this correct?

Also, is it possible to do this same analysis if the data are structured such that each rank has a column and subjects are only on one row? I'm guessing the data need to be restructured in the above manner in order to conduct this analysis. But just want to make sure before I suggest it to her.

thanks for the help,


Matthew Pirritano, Ph.D. National Science Foundation Post-Doctoral Fellow College of Education Department of Individual, Family & Community Education MSC05 3040 1 University of New Mexico Albuquerque, NM 87131-0001 Telephone (505)277-7115 FAX (505)277-8361

Kylie Lange wrote:

> 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 <> >>> 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 >>> 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 >>> 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 <> >>> 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: >>> >>> >>> >>> >> >> >> >> >>> 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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