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Date:   Wed, 24 Aug 2005 21:24:46 +0100
Reply-To:   starborn@statisticsdoc.com
Sender:   "SPSSX(r) Discussion" <SPSSX-L@LISTSERV.UGA.EDU>
From:   Keith Starborn <starborn@statisticsdoc.com>
Subject:   Re: Nonparametric vs. Parametric
Comments:   To: Nate Wojcik <nate.wojcik@gmail.com>
In-Reply-To:   <1b708a6e0508241222547df926@mail.gmail.com>
Content-Type:   text/plain; charset=ISO-8859-1; format="flowed"

Keith Starborn www.statisticsdoc.com

Nate -

You ask a series of very interesting questions. Just to start with one - the log transformation of the dependendent variable - what is the nature of the DV? Does it have a non-arbitrary zero point? If so, it would be appropriate to treat the DV as a ratio measure and a log transformation of the DV might well be very informative.

You also mention that you are working with pre and post burn treatments. Does this mean that the DV is measured on the same subjects at two time-points? Do you have the raw data organized so you can link measures on the same subjects at two time points? Are the intervals between pre- and post measures similar across subjects? If you can meet these conditions, a repeated measures ANOVA would be informative.

Best,

KS

Quoting Nate Wojcik <nate.wojcik@gmail.com>:

> i am currently analyzing data and i have come across a few ideas > that i need clarification on. my design consists of a single DV and > multiple IV's (with one IV containing greater than two levels). my > hypotheses have been structured such that a multi-way ANOVA > (three-way) should be sufficient. however, i am unable to satisfy the > assumption of a normally distributed population. first of all, when > determining the distribution and variances for a multi-way ANOVA, > should the sample distribution be analyzed by grouping factors? > second, since there are not any nonparametric equivalents for designs > with more than two IV's (to the best of my knowledge, unless a SRH > extension works, but how?), how important is it to meet all of the > assumptions of a three-way ANOVA. furthermore, if i can transform my > data (log and log+constant) to allow both the skewness and the > kurtosis to approach zero, will this suffice a normally distributed > population. are there any helpful cutoffs or limits? finally, if i > am working with pre- and post-burn treatments, can these be viewed as > repeated measures DVs? also, are there any statistical techniques > that allow you to compare the variance explained with treatment > response ratios (standardized data)? > > thanks > > nate >

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