I have a data file (available for download at http://www.bodowinter.com/tutorial/politeness_data.csv and based upon the research of Winder Grawunder, 2012) that gives conversation voice frequency (Hz) as a function of gender (M/F), attitude (formal/informal register), conversation scenario, e.g. asking a question vs. making a demand, etc., (broken into 7 scenario categories). The data was collected from six different individuals. For each individual, I can store the results in a table, T, having column headers attitude, gender, scenario, and frequency.
If I treat frequency as the numeric response, attitude and gender as fixed categorical effects, and scenario as a categorical random effect, a mixed linear mixed effect model corresponding to this data for each individual is given by
LME = fitlme(T,'frequency~attitude+gender+(1|scenario)');
The command anova(LME), gives me a p-value for each fixed effect, indicating its significance in determining the frequency. However, this p-value varies among the six individuals.
How do I combine all of the data across the six individuals to determine whether a fixed effect, such as gender, is significant in determining the pitch overall?
I realize that this is a multiple comparison problem. Can this be implemented using the multcompare command? If so, what steps are involved, starting from a single file that also accounts for the individuals? (It's stored in a table with column headers, gender, scenario, attitude, frequency, and individual.)