Hi! This is the first time for me approaching Bonferroni correction for multiple comparisons and I have a doubt about that. I am going to run bivariate correlation to see association between 5 scales of a questionnaire and 6 scales of an interview (each scale of the interview vs each scale of the questionnaire). I would like to apply Bonferroni correction for multiple comparison but I am not sure how to calculate the number of comparison to correct p. Hope I was clear and thank you in advance to those who will help me!
Bonferroni correction
- Thread starter Franci_
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I need to correlate data of an interview made of 5 scores (communication, motor abilities, daily life, social abilities and total score) with scores from a questionnaire made of 6 domains (anxiety, depression and so on). So, correlations will be run between communication and anxiety, communication and depression [...], motor abilities and anxiety, motor abilities and depression and so on. Lot of comparisons and I need to correct for multiplicity. I am not sure my calculation of the number of comparison (6x5) is right. I know it is a stupid doubt, but I need to be sure the calculation is correct. Thank you in advance.
Personally, I perform Bonferroni corrections very rarely. If there are only few comparisons to be made,
then usually in my work there is one primary comparison, and some secondary comparisons, and I do
not mind making the secondary comparisons without Bonferroni (except a reviewer would demand it).
If there are many comparisons, Bonferroni destroys power and would therefore lead to a meaningless
analysis. But there certainly are circumstances, e.g. with a very large sample and/or the absolute priority
to prevent false-positive results, where Bonferroni (or preferably Bonferroni-Holm) can be justified.
I do not know whether you'd be better off with multiple/multivariate approaches such as multiple regression.
If you need to make 30 distinct comparisons, and all null hypotheses are true, then the probability of at least
one false-positive finding is about 80%. Instead of Bonferroni (0.05/30=0.0017) I would be inclined to choose
a conservative, but not so crazy significance level, such as 0.01. If all 30 null hypotheses were true, the chance
of at least one false-positve result would be 26% then. But if some null hypotheses were not true, you would
retain enough power with an alpha of 0.01.
Just my 2pence
Karabiner
then usually in my work there is one primary comparison, and some secondary comparisons, and I do
not mind making the secondary comparisons without Bonferroni (except a reviewer would demand it).
If there are many comparisons, Bonferroni destroys power and would therefore lead to a meaningless
analysis. But there certainly are circumstances, e.g. with a very large sample and/or the absolute priority
to prevent false-positive results, where Bonferroni (or preferably Bonferroni-Holm) can be justified.
I do not know whether you'd be better off with multiple/multivariate approaches such as multiple regression.
If you need to make 30 distinct comparisons, and all null hypotheses are true, then the probability of at least
one false-positive finding is about 80%. Instead of Bonferroni (0.05/30=0.0017) I would be inclined to choose
a conservative, but not so crazy significance level, such as 0.01. If all 30 null hypotheses were true, the chance
of at least one false-positve result would be 26% then. But if some null hypotheses were not true, you would
retain enough power with an alpha of 0.01.
Just my 2pence
Karabiner
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