Hi,
I just need some reassurance in regards to whether what I am thinking is right? For some reason , I don't think I am .
I am planning to assess the mean difference between groups. In particular, my aim is to determine what features present in users' posts ( total word count, words related to fruit, words related to colour, words related to cars) differentiate strongly between users' personality traits (openness, neuroticism, and extraversion). I am using a software that can automatically detect this words and outputs the frequencies that these words appear per post.
From my understanding ( please correct me if I am wrong):
Features is a continuous numeric variable
Personality Traits is a categorical variable
My dependent variable is Features, while my independent variable is Personality Traits.
Therefore, ANOVA is a potential statistical test to for example determine the difference in means of words related to fruit between personality traits? OR should I use Multinomial Logistic Regression?
Thank you in advance.
I just need some reassurance in regards to whether what I am thinking is right? For some reason , I don't think I am .
I am planning to assess the mean difference between groups. In particular, my aim is to determine what features present in users' posts ( total word count, words related to fruit, words related to colour, words related to cars) differentiate strongly between users' personality traits (openness, neuroticism, and extraversion). I am using a software that can automatically detect this words and outputs the frequencies that these words appear per post.
From my understanding ( please correct me if I am wrong):
Features is a continuous numeric variable
Personality Traits is a categorical variable
My dependent variable is Features, while my independent variable is Personality Traits.
Therefore, ANOVA is a potential statistical test to for example determine the difference in means of words related to fruit between personality traits? OR should I use Multinomial Logistic Regression?
Thank you in advance.