The strength of a correlation is given by its value; the closer the absolute value is to 1 the stronger it is.
You may also wish to test the reliability of the coefficients obtained. Benjamin's test will help you decide whether there is a significant difference between two correlation coefficients.
Vahid pointed you to a document on Karl Wuench's nice website. Karl and I actually wrote an article describing the common methods for comparing correlations and OLS regression coefficients. Those with institutional access to "Behavior Research Methods" can download it here: http://link.springer.com/article/10.3758/s13428-012-0289-7#close. (Note that there is also an Errata document: http://link.springer.com/article/10.3758/s13428-013-0344-z). The supplementary material includes SPSS syntax and SAS programs, which can be downloaded from the following pages:
Regression analysis gives you indication about the contribution of each factor on the formation of the formula for each variable. I believe this is what you want.
For those who down voted my answer, please remember that only by recognizing our weakness and our long-held but erroneous belief that we shall improve. Let us not continue and multiply this blunder to our students and among our peers. Please review your statistics please.
Alex is right, what I wanted was to figure out whether two correlations are SIGNIFICANTLY different from each other. One was .62 and the other .68. Given my limited number of participants, the outcome of the formula that was given first by Benjamin and commented by the others is that my two correlations do not differ from each other and that, consequently and unfortunately for me, the second is not stronger than the first one.
Thank you all for your help and thank you Ed for your comments.
I'm aware that I cannot draw definite conclusions from a null result. My analysis is just telling me : Given your results and the number of participants in your study, I cannot tell you that one of your correlation is stronger than the other.
And still I've got to say "Thank you dear analysis"
The easiest approach is to estimate confidence intervals for the two estimated sample correlation coefficients. if the two CI do not overlap, then the correlations are different. Otherwise, you do not have evidence to say that they are different.
Hi Jaime. The method you propose can mislead, because there can be a statistically significant difference (at the .05 level) between two point estimates despite overlap in the two 95% CIs. For example, see this short note from CMAJ:
http://www.cmaj.ca/content/166/1/65.long
That note talks about means, but the same point applies to correlations or any other statistic you might compare (e.g., odds ratios, etc).
If you want to use a CI method, you need a CI for the DIFFERENCE between the two correlations. If the CI for the difference does not include a value of 0, then the difference is statistically significant at the .05 level. The SPSS and SAS programs I mentioned earlier in this thread do compute the usual tests for comparing two correlations, but also compute CIs for the differences. The SPSS syntax and corresponding SAS programs can be downloaded at the links shown below.
Use the Fisher's Z-transformation when you are comparing correlation coefficients from 2 independent groups (e.g. between control and experimental groups). However, use Hotelling's t for comparing correlations within a sample.
A handy calculator can be found at http://psych.unl.edu/psycrs/statpage/ (click on computators link and download FZT application)
I am not sue If it is a master level project or a doctoral level dissertation. Depending on the research question analysis needs to be done. You could use mixed method.
You could also do triangulation to draw conclusion.