I'm analyzing data from a designed agricultural experiment using linear mixed models.  At one point, I collected data for what I thought would serve a covariate (weed rating) for explaining crop yield, but further analysis revealed overlap between this variable and one of my experimental treatments.  A major hint that these variables might be collinear was that, when both variables were included in the model as predictors for crop yield, the treatment variable was no longer significant (it previously had been consistently significant).  Note that both variables are categorical, and the treatment variable has two levels while the covariate (weed rating) is an ordered variable with 5 levels (1 to 5).    I would like to determine a standard way to demonstrate redundancy between these variables, so that I can justify not including them simultaneously in models.   A chi-squared test (code below) seems to be appropriate.  Is this approach adequate for establishing predictor redundancy?  I'm not using the term "collinearity" here, because I'm under the impression that collinearity applies more to regression and not ANOVA.  I don't think I need to determine VIF here. title "Chi-squared test for predictor redundancy"; proc freq data=df; tables treatment * other_variable / chisq; run; Ultimately, my goal is to establish a causal link between the response (crop yield), the treatment, and weed rating.  I've accomplished part of this by treating the covariate (weed rating) variable as the response variable and modeling that with my experimental treatments.  That confirmed a high degree of correlation (not sure if "correlation" is technically correct to use here, but you know what I mean) between the treatment in question and the weed rating.   Now, I believe that I just need to establish the link between weed rating and yield, and justify not including the redundant treatment in the model.  Thank you for reading. ... View more