These 36 variables are actually part of a 3x3x2x2 repeated measures design.
time x light x screen x eye
These variables represent "contrast sensitivity" values taken at baseline,
and then 12 months and 24 months post refractive surgery. The issue has
been raised as to wether or not we should analyze the "contrast sensitivity"
values or the "log contrast sensitivity" values. These are continuous data.
The data was analyzed as a repeated measures design described above using
the "raw" contrast sensitivity data.
Thanks for your help and I hope this further explains the data.
From: Peter Flom [mailto:flom@NDRI.ORG]
Sent: Tuesday, November 04, 2003 1:56 PM
Subject: Re: Transforming data
The first question is what you are going to do with these 36 variables.
they going to be independent variables in a regression? Are they of
interest in themselves?
Are they going to be a part of a factor analysis? Or what?
The second question is what these variables ARE. Are they counts?
something? Continuous? Discrete? or what?
Without knowing this, it's hard to recommend anything.
Peter L. Flom, PhD
Assistant Director, Statistics and Data Analysis Core
Center for Drug Use and HIV Research
National Development and Research Institutes
71 W. 23rd St
New York, NY 10010
(212) 845-4485 (voice)
(917) 438-0894 (fax)
>>> Thompson Bill T Contr USAFSAM/FEC <Bill.Thompson@BROOKS.AF.MIL>
11/4/2003 2:24:08 PM >>>
I have 36 variables of which I perfromed Shapio-Wilks normality test
those 36 variables there were 5 that were "not" normally distributed
when log transformed they became normally distributed. There were 3
remained "not" normally distributed, however, there were alos 2
that "were" normally distributed that when transformed became "not"
My question is as follows: What considerations should be addressed
this situation happens. Do I transform all the data even if a couple
variables become non-normal or do I just leave the data as is and
This is my first encounter with this situation so forgive my
||11/4/2003 8:09:59 PM