I have tens of thousands of rows of data, where each row is an object and columns are predictors. My columns of interest are sensory measurements on a 10-point scale. From variable screening, I have reduced my predictors to 3 categorical variables. I am interested in identifying which levels are most influential (I have 33 total levels), and the direction of the influence. I do not need to make numerical prediction targets; an explainable qualitative model is sufficient.
I am thinking a mixed model would serve my purpose; the fixed effects parameter estimate p-values would allow me to identify those specific levels to focus on. Question: Would their fixed parameter estimate be a way to rank impact, and does the sign of the estimate indicate the direction of the effect (i.e., + means increasing the level increases the response, and decreasing the level decreases the response)?
My apologies but I have never used a mixed model before 
. Thanks in advance for any feedback.
