22nd October 2013 at 8:13 am #1376Silke MaturaMember
Correlating neuropsychological test scores with brain imaging data, I have done two correlations with the same data set. Since one variable is not normally distributed, I have used Kendall’s tau correlation for that variable (Recognition scores) and for the normally distributed variable (cued recall) I have used Pearson Moment correlation. The reviewer has asked for Bonferroni correction, since I have done multiple (two) correlations with the same data set. I am unsure if Bonferroni correction is necessary since I have used two different statistical tests for correlation (Kendall’s tau and Pearson Moment correlation). I would be very grateful for your comments on this issue.
Silke28th October 2013 at 3:33 am #1379Dave CollingridgeParticipant
Generally speaking, running multiple tests on the same data requires adjustment to control for Type I errors, I think this would apply to different correlations on the same data. If the results of the same tests are significant after correction (i.e., p< .05/2 or 0.025), then give the reviewer what he/she wants. If the results were not previously significant then a bonferroni will not change anything either. If one of your tests becomes non-significant and this bothers you then consider changing to a one-tail test if you have not already done so, assuming that a one-tailed test is justified in that you expected the difference to occur in a certain direction. If that does not work then ask yourself whether your study is exploratory or causal in nature. If a study is exploratory then I think that one is justified in not running a correction for fear of missing potentially meaningful findings that should be explored in a follow up study with more controls. If that does not work run a power calculation. If your power is low due to low sample size that can be a reason to not use a correction like bonferroni. If that does not help then consider reporting both corrected and uncorrected results. If you must report non-significant results due to a correction, you are always free to inject your own views which may be that the results are significant, but you were unable to show it statistically in your study.29th October 2013 at 6:09 pm #1378Dave CollingridgeParticipant
Silke, mir sagen wenn einer meiner Vorschläge für Sie geeignet ist.
Dave30th October 2013 at 9:11 am #1377Silke MaturaMember
sorry that I haven’t replied to your suggestions yet. Your suggestions were very helpful. I think I will go for the power analysis – the sample size was not very big (n = 63) and that might have resulted in low power. I read some comments by Andy Field and he stated that the correlation coefficient (Kendall’s Tau) can be interpreted as the effect size and that p-values for correlations are usually not as meaningful as the correlation coefficient. Since the analysis produced a pretty decent tau auf 0.39, I am quite confident that there really is a meaningful correlation, although the p-value is slightly larger (0.035) than it should be (0.025). Anyhow, I am very grateful for your help and impressed by your German.
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