I am running a two-way repeated measures ANOVA (exercise intensity x time), in which I have 3 intensities and 5 time points.
I have a signifcant intensity x time interaction, and so ran the post-hoc using Tukey corrections.
However, for the corrections, there are many comparisons which I not interested in. For example, let's say the intesities are 100, 80 and 60% of peak power output, and the time-points are baseline, 25, 50, 75 and 100% task completion. I am only interested in whether within each intensity, there is a change over time relative to baseline (e.g. does my dependent variable change over time for the 100% peak power output trial), and whether at a specific time points (e.g. 50% task completion), there is a difference between the 3 intensities.
Yet, the post-hoc corrects for a number of unplanned comparisons (e.g. it will compare 25% task completion at 100% peak power output vs. 75% task completion from 80% peak power output).
Is there any way that I can specify only the planned comparisons which I am interested in, so that the p value is not adjusted for all these unplanned comparisons?
Post-hoc planned and unplanned comparisons
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Re: Post-hoc planned and unplanned comparisons
hi,
no this isn't available in the RM ANOVA. probably your best bet is to use no-correction, and then pluck out the p-values you are interested in, and correct these.
the p.adjust() method you could run from the rj editor.
cheers
jonathon
no this isn't available in the RM ANOVA. probably your best bet is to use no-correction, and then pluck out the p-values you are interested in, and correct these.
the p.adjust() method you could run from the rj editor.
cheers
jonathon
Re: Post-hoc planned and unplanned comparisons
Hello,
I am wondering if the same solution could be applied to post-hoc done in GLM models?
In my specific case, I have a significant 3-way interaction that I would like to further explore by doing a post-hoc comparison. However, I am not interested in all the comparisons, but only the ones that are relevant to the research question. Still, when doing a Bonferroni correction in Jamovi, it would correct for all the possible comparisons, making the end result too strict and risk not detecting true significance?
If I understood this previous answer right, would the solution then be to look at the non-corrected p-values and then to correct them manually? So for example, instead of taking the automatic Bonferroni correction for the total of 24 comparisons, I would take the usual alpha=0.05 and divide it by the number of comparisons I am interested in (e.g. 0.05/6) and then check the non-corrected p-values to see which ones fall below that cut off point? Would that be a recommended solution to this problem or is there another preferred way?
Thank you, any advice or input would be much appreciated!
I am wondering if the same solution could be applied to post-hoc done in GLM models?
In my specific case, I have a significant 3-way interaction that I would like to further explore by doing a post-hoc comparison. However, I am not interested in all the comparisons, but only the ones that are relevant to the research question. Still, when doing a Bonferroni correction in Jamovi, it would correct for all the possible comparisons, making the end result too strict and risk not detecting true significance?
If I understood this previous answer right, would the solution then be to look at the non-corrected p-values and then to correct them manually? So for example, instead of taking the automatic Bonferroni correction for the total of 24 comparisons, I would take the usual alpha=0.05 and divide it by the number of comparisons I am interested in (e.g. 0.05/6) and then check the non-corrected p-values to see which ones fall below that cut off point? Would that be a recommended solution to this problem or is there another preferred way?
Thank you, any advice or input would be much appreciated!