# Taking correlation before or after log-transformation of variables

Is there a general principle on whether one should compute pearson correlation for two random variables X and Y before taking their log transform or after? Is there a procedure to test which is more appropriate? They yield similar but different values, since log transform is non-linear. Does it depend on whether X or Y are closer to normality after log? If so, why does that matter? And does that mean that one should do a normality test on X and Y versus log(X) and log(Y) and based on that decide whether pearson(x,y) is more appropriate than pearson(log(x),log(y))?

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@vinux has a nice answer, and provides an informative link for understanding the role of normality in correlation. I just wanted to point to this question: stats.stackexchange.com/questions/298 which is very good for understanding what logs do in regression. –  gung Feb 13 '12 at 17:46

Correlation(pearson) measures a linear relationship between two continuous variables. There is no such choice for (X,Y) or (log X, log Y). Scatter plot of the variables can be used for understanding of the relationship.

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Because $\log(X)$ and $\log(Y)$ are monotonic transformations of the data $X$ and $Y$, you might also choose to use Spearman's rank correlation ($\rho_S$) and not worry about transforming your data, as you would get $\rho_S(X,Y) = \rho_S(\log(X),\log(Y))$