On Tue, 23 Apr 2002 09:17:08 +0100, "Robin Hayman" <[EMAIL PROTECTED]> wrote:
> Hi all, > > I have two columns of numbers that I wish to compare to see how > similar/ dis-similar they are. The columns of numbers are a series of > Pearson Product Moment Correlation Coefficients (PPMCC). In order to > compare the two columns of numbers in the past I have employed a > standard independant samples t-test. Recently I have plotted the > distribution of the data and it is NOT normally distributed (the range Several considerations do come to mind. There are columns: are the numbers 'paired' in a meaningful way? When it comes to data collection and tests in general, a correlation is a lousy thing to compare, since it *assumes* equality of variances. That is, a whole lot of people do ask to compare two correlations when they ought to be asking to compare two regression coefficients or regression lines. The worst problem with non-normality, when it comes to doing ANOVA (including t-tests) is that there are far outliers that thoroughly distort the means. If you are comfortable with comparing the sets of numbers as *means*, then you should be comfortable with the t-tests. > in actual fact is more like -0.1 to +0.8). Therefore the assumptions > underlying the t-test are violated and I shouldn't be conducting a > t-test on the data. Right?? - with correlations over .5, you do get into the range where Fisher's z is ordinarily used to stretch out the larger values. That is usually considered a stabilizing transformation for correlations, so it is acceptable without much argument. Of course, any time you are taking the average of correlations, someone might argue that you ought to consider the N's that underlie the r's -- and provide a test to show that the r's are basically from a homogeneous set. That does assume that the r's are intended to represent a central tendency instead of being a composite value. > > I have thought about using a non-parametric equivalent of the t-test > and also about using some kind of normalisation on the data set so > that I can use a t-test (determined to use a t-test!!!). What is the > correct way to go forward? Hope this helps. -- Rich Ulrich, [EMAIL PROTECTED] http://www.pitt.edu/~wpilib/index.html . . ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at: . http://jse.stat.ncsu.edu/ . =================================================================
