Multicolinearity: Why you should care and what to do about it Multicolinearity is a problem for statistical analyses. This large, unwieldy word essentially refers to situations where your predictor variables so highly correlated with one another, they become redundant. Generally speaking, this is a problem because it will increase your Type II error rate (i.e., false negatives). In the most severe cases, multicolinearity can produce really bizarre results that defy logic. For example, the direction of relationships can sometimes reverse (e.g., a positive relationship...

Pavithra Ratenom: Hi, I have 1 independent variable, 1 dependent variable an...ella bella: Hi! is it possible to have all three pathways negative? My r...Andrey Andoko: Dear Sean It is really helpful as my research model will us...Mehak: i want to use an independent variable as moderator after thi...sean: Yes, but this is older advice. Best practice now is to calcu...Najm: Hi Sean, according to the three steps model (Dudley, Benuzil...