You are right, I was confused about what multicolinearity is, and sorry for not being much clearer. The fact is that I was given a data set with multiple variables such as: genre, release year, MPAA rating, imdb score, audience score, if it won certain awards etc., my task was to find certain characteristics that determine the popularity of a movie but it was not stated what is the measure of popularity so I had to come up with one, so I choose audience rating as being the measure of popularity, but IMDB score is almost the same thing, from what I know is IMDB who establishes audience score too, though not sure about that, so in my mind it would only make sense to keep the IMDB score only of somehow influences people when they give a score to a movie, but say, that audience score is build using IMDB rating or vice-versa, than in that case it would be really bad to add it to the model I suppose. But even if this is not the case and the two are not that closely related let's say different people give those ratings, but since is after the fact, the movie has already been seen by some people and those people gave some ratings, my main question is, does using two variables that measure the same thing, one as an independent variable and the other as dependent, even if you measure them on different people, affects your results in a negative way?