See also:Kendall's Tau-SPSS, Pearson Correlation-SPSS, Commentary: The Spearman Rank correlation is the non-parametric equivalent of the Pearson correlation. What does that mean? It means that the Spearman correlation has fewer assumptions. Perhaps the biggest advantage is that the Spearman correlation can be applied to non-normal data. In a sense, all the Spearman correlation does is transform the data into ranked data, if it has not been transformed already. It's really just a Pearson correlation applied to ranked or ordinal data.

What I really like about the Spearman correlation is that I can quickly compare it to the Pearson correlation. That is, when dealing with interval/ratio data that might be non-normally distributed, I can apply Spearman and Pearson to the same data. Those correlations that differ "meaningfully" tell me that my data are too non-normally distributed to use the Pearson correlation.

If the Spearman has less assumptions than Pearson, why not use it every time? Well, that's a good question. One thing to keep in mind is that, across all statistical analyses, as you deal with statistics with fewer assumptions, they also tend to be less informative. In the case of the Spearman correlation, some consider it inappropriate to square the correlation to derive a coefficient of determination. In my opinion, so long as one restricts interpretations to the percentage of variance accounted for in ranks, it should be okay to square the Spearman correlation coefficient. Overall, though, if you're data are not naturally ranked, and the data are sufficiently normally distributed, then stick with Pearson's r.