[color=#000000]Hi Amy,[/color]
CONN will use T-stats for ANOVAs with two levels for each factor and F-stats for ANOVAs with three or more levels (for details about GLM stats see https://web.conn-toolbox.org/fmri-methods/general-linear-model). In reality that is somewhat arbitrary since two-sided T-stats and F- stats in the two-level case are exactly equivalent (and the only reason to use T- instead of F- values in this case is just to also allow directional tests). If you want to transform T-stats to F-stats simply use the equivalence:
T(df) ^2 = F(1,df)
So, for example, if CONN reports in voxel-based analyses |T(15)|>3, you may simply report instead F(1,15)>9; or if in an ROI-to-ROI analysis you are obtaining "T(15) = 2, p = 0.0639 (two-tailed)", you may equivalently report that as "F(1,15) = 4, p =0.0639"
Best
Alfonso
[color=#000000]ps. you may check this equivalence with the syntax:[/color]
[color=#000000] T=randn; % T-stat value[/color]
[color=#000000] df=randi([1 10]); % degrees of freedom[/color]
[color=#000000] p1=1-spm_Tcdf(T,df); % one-sided p-value (T-stat)[/color]
[color=#000000] p1=2*min(p1,1-p1); % two-sided p-value (T-stat)[/color]
[color=#000000] p2=1-spm_Fcdf(T^2,1,df); % p-value (F-stat)[/color]
[color=#000000] disp([p1 p2])[/color]
[i]Originally posted by Amy Bouchard:[/i][quote]Hello Alfonso,
Could you please tell me how would one obtain F values for the contrasts 1) main effect of group, 2) main effect of condition, 3) interaction between group and condition?
I have the same design (2 x 2 ANOVA), however, in the 2nd level results, it shows a t-statistic instead of an F-statistic.
Thanks,
Amy[/quote]
CONN will use T-stats for ANOVAs with two levels for each factor and F-stats for ANOVAs with three or more levels (for details about GLM stats see https://web.conn-toolbox.org/fmri-methods/general-linear-model). In reality that is somewhat arbitrary since two-sided T-stats and F- stats in the two-level case are exactly equivalent (and the only reason to use T- instead of F- values in this case is just to also allow directional tests). If you want to transform T-stats to F-stats simply use the equivalence:
T(df) ^2 = F(1,df)
So, for example, if CONN reports in voxel-based analyses |T(15)|>3, you may simply report instead F(1,15)>9; or if in an ROI-to-ROI analysis you are obtaining "T(15) = 2, p = 0.0639 (two-tailed)", you may equivalently report that as "F(1,15) = 4, p =0.0639"
Best
Alfonso
[color=#000000]ps. you may check this equivalence with the syntax:[/color]
[color=#000000] T=randn; % T-stat value[/color]
[color=#000000] df=randi([1 10]); % degrees of freedom[/color]
[color=#000000] p1=1-spm_Tcdf(T,df); % one-sided p-value (T-stat)[/color]
[color=#000000] p1=2*min(p1,1-p1); % two-sided p-value (T-stat)[/color]
[color=#000000] p2=1-spm_Fcdf(T^2,1,df); % p-value (F-stat)[/color]
[color=#000000] disp([p1 p2])[/color]
[i]Originally posted by Amy Bouchard:[/i][quote]Hello Alfonso,
Could you please tell me how would one obtain F values for the contrasts 1) main effect of group, 2) main effect of condition, 3) interaction between group and condition?
I have the same design (2 x 2 ANOVA), however, in the 2nd level results, it shows a t-statistic instead of an F-statistic.
Thanks,
Amy[/quote]