Angela Harlem said:
do you mean where the whatsit is 0.05 or under, meaning no correlation? honestly, i just finished a unit on research methods (in justice, but it's not unique to the field, obviously), and i have momentarily blanked everything out. which is a shit because i need to remember it all for another unit coming up next.
Yes, it does have to do with correlations, or in my case, lack thereof.
If your sample is skewed (ie. not a normal distribution), there is a good chance that when performing correlations, you will get a false negative - the two variables will appear to be not correlated, when indeed they are. To fix this, you transform the variable to make the distribution normal. I'm working with two variables that should be correlating, but they're not.
Ideal skewness is zero. As I'm sure you know, rarely in research is anything ever ideal. So, I need to know what level of skewness is acceptable, before you do a transformation.
What I've been told is this - you eyeball the distribution, and if it's not normal, do a transformation. I've done histograms with a normal distribution laid over top of them, and while mine aren't perfectly normal, they're not nearly as bad as some of the examples of positive and negative skewness I've seen. The two distributions I'm concerned with right now, the skewness of them are -.233 and .222. I want to know if that's within the bounds of being "normal enough."
U2Man, what do you mean by the model and the parameters?
Does anyone know how to interpret the numbers of either the Kolmogorov-Smirnov or the Shapiro-Wilks tests of normality?