When plotting within-subject data for condition A vs. condition B (significance tested through a t-test), should the error bars reflect standard error of the mean for each condition independently? Or should they be SE of the mean difference between A and B?
When there are more conditions for an ANOVA — A vs. B for condition 1 and A vs. B for condition 2 — should the error bars correspond to the SE of mean difference of A-B at 1 and then the SE of mean difference at 2?
Edit:
This paper gives a way to calculate SE/CI for within-subject designs. The gist is that the subjects are normalized to reduce the between-subject contribution to the error bars and better reflect the results of a repeated measures ANOVA.
Cousineau, D. (2005). Confidence intervals in within-subject designs: A simpler solution to Loftus and Masson’s method. Tutorial in Quantitative Methods for Psychology, 1(1), 42–45. PDF
Best Answer
Regarding the first question, I think this depends on how you plot the data. If you are using a bar graph with the individual means side by side then I would add the error bars for the individual means. If the height of the bar chart represents the difference of the two means then use the standard error for the mean difference. As to the second question if I understand you correctly you are looking at mean differences on subsets of the data where condition 1 applies in one case and condition 2 in the other. Since this is what you want to show I would use the corresponding standard error (i.e. for condition 1 provide the standard error for the mean difference for the data where condition 1 applies and do it the same way for the mean difference when condition 2 applies.