I am running a tapping experiment where I have to analyze whether meter and/or pattern affect the synchronization performance. The within subjects experiment is comprised of 6 conditions, so it’s a 2 x3 experiment (meter x pattern). As of now, I’ve been able to match the tapping data to the respective stimuli for each participant and aggregated the data so that each participant is contributing six data points to the group level analysis.

Now, I need to find a way to run a two-factor ANOVA on matlab. The thing is, I don’t have experience with running this kind of analysis on circular data and I know it requires a special kind of procedure. Do you have any suggestions as to how I can proceed?

I have tried to run a Harrison-Kanji test but my output always results in NaN values for the p-values, which is strange cause the data appears to be valid (no missing data, consistent amount of data points per group). So I am getting something like this:

Angles for PhaseLag (in degrees): 0.1872    -0.12609    -0.26187    -0.15538     0.13689       0.426   -0.034659     0.23428   -0.031413     0.23462
Angles for PLV (in degrees): 0.14884    0.062623     0.22645     0.14601     0.12632     0.32505    0.077925     0.13987    0.077551    0.087998
P-value for PhaseLag (Meter: 7/8, Pattern: reverse): NaN
P-value for PhaseLag (Meter: NaN, Pattern: NaN): P-value for PLV (Meter: 7/8, Pattern: reverse): NaN
P-value for PLV (Meter: NaN, Pattern: NaN): P-values for PhaseLag:
    {[NaN NaN NaN]}    {[NaN NaN NaN]}    {[NaN NaN NaN]}
    {[NaN NaN NaN]}    {[NaN NaN NaN]}    {[NaN NaN NaN]}

P-values for PLV:
    {[NaN NaN NaN]}    {[NaN NaN NaN]}    {[NaN NaN NaN]}
    {[NaN NaN NaN]}    {[NaN NaN NaN]}    {[NaN NaN NaN]}

So far this is what my code tries to do:

Load data

Convert to table

Compute Radial Fields

Assign group IDs: Assign unique group IDs based on the combination of ParticipantID, Meter, and Pattern

Specific Condition Analysis: Filter data based on specific conditions and compute angles and p-values.

I’m just so stuck on the ANOVA stage, if anyone could give me any suggestions I would be so grateful.