It sounds like you're on the right track with using a repeated measures ANOVA for your data. Since the measurements come from the same participants, this method will account for the within-subject variability.
- Organize Your Data: Ensure your data is in a suitable format. You can use a matrix where each row represents a participant and each column represents a condition.
- Create a Table: Convert your data matrix into a table, which is required for the fitrm function.
- Define the Model: Use the fitrm function to fit a repeated measures model.
- Run the ANOVA: Use the ranova function to perform the repeated measures ANOVA.
Here's a sample code to guide you through the process:
% Sample data: 500 participants, 8 conditions
data = rand(500, 8); % Replace this with your actual data
% Convert data to table
T = array2table(data, 'VariableNames', {'Cond1', 'Cond2', 'Cond3', 'Cond4', 'Cond5', 'Cond6', 'Cond7', 'Cond8'});
% Define the repeated measures model
rm = fitrm(T, 'Cond1-Cond8 ~ 1', 'WithinDesign', table([1 2 3 4 5 6 7 8]', 'VariableNames', {'Condition'}));
% Perform repeated measures ANOVA
ranovatbl = ranova(rm);
% Display the results
disp(ranovatbl);
% Perform post-hoc tests
multcompare(rm, 'Condition');
I hope this helps you.
