Dear all,
I have two models: one is to run the model
with x1, x2, and x3 predictors, and the other is to take x3
out and run the model with the x1 and x2 only at the third level. In
the first model, only x2 is statistically significant, but in the
second model both x1 and x2 are significant after x3 is taken away. The
second model's results make more sense than the results in the first
model. I did a correlation test, and found that x3 highly correlated
with x1 (r coefficient >0.5 and p<0.01). But the VIF test of the
first model (linear one-level model) does not show multicolinearity
problem of the x3 variable (VIF value <2).
My question is: should I use VIF test or
the correlation test to identify the possible multicollinearity
problem, if the two tests results are not consistent, as indicated
above?
Would appreciate any advice! Thanks,
Rosie
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