I am running stats on 5 IVs (5 personality traits, extroversion, agreeableness, conscientiousness, neuroticism, openness) against 3 DVs Attitude to PCT, Attitude to CBT, Attitude to PCT vs CBT. I also added in age and gender to see what other effects there are.
I am testing to see whether personality traits can predict attitudes of the DVs.
I initially used Pearson's correlation for all variables (45 tests).
The main finding was that extroversion was correlated to attitude of PCT at p=0.05. But as I was running 45 tests I did a Bonferroni correction of alpha = 0.05/45 = 0.001, therefore making this finding insignificant.
I then ran a simple linear regression on all variables, again extroversion was significant with attitude to PCT. If I do the Bonferroni correction this it comes out insignificant again.
- Do I need to Bonferroni correct at Pearson's correlation?
- If I do, and therefore making extroversion with attitude to PCT insignificant, is there still a point in doing linear regression?
- If I do a linear regression, do I need to do the Bonferroni correction for this also?
- Do I only report corrected valued or both uncorrected and corrected values?