This is a continuation of the original Cog_Interference analysis, now considering the the relationship that mood has with category learning. First, we will need to import our clean dataframe from the original analysis and then combine it with a .csv file that was used to save the mood information. Mood information is saved as a level of positive affect and negative affect, one measure for each participant. The higher the value a mood measure has, the greater that participant is represented by that mood.

This data analysis attempts to analyse a full data set from over 303 independent csv files and wrangle it into long format. This newest document allows us to better conduct our analysis directly from csv files. One-Way ANOVAs and Bonferonni pairwise are performed on RD and II category sets.

This data analysis attempts to analyse a full data set from over 140 independent csv files and wrangle it into long format. This newest document allows us to better conduct our analysis directly from csv files. One-Way ANOVAs and Bonferonni pairwise are performed on RD and II category sets.

Old Version - October 1st 2019