Robin Hayman 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
There are probably better ways to compare two sets of correlations. ANOVA or meta-analysis (depending on the source of the original correlated data) might bet better. > distribution of the data and it is NOT normally distributed (the range > 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?? It will never be _exactly_ normally distributed. The range is irrelevant. What are the shapes of the distributions? Are the variances similar? If N is low a Mann-Whitney U tests might be preferable. > 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? Unless the distribution of scores is very odd (not unimodal and roughly symmetrical) or has potential outliers the t test is likely OK. I'd worry more about where the correlations are coming from - it is probable that a technique using the all the raw data might be superior. Thom . . ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at: . http://jse.stat.ncsu.edu/ . =================================================================
