In my data, some individuals have missing data on the central predictor (father missed the intake assessment). Comparing the DVs' means for those with a missing/non-missing predictor yielded some sizeable effects.
Now I want to find out whether the systematic missings may have led me to underestimate the size of the OLS regression coefficients. What's a good way to do this?
Simply comparing the variances of the DVs in the group without missings to the group with missings is easy to do?
But conceptually I want to know whether the whole sample has significantly less variability when I leave the group with missings (and significantly-lower-than-average-scores) out, not whether the two groups (with missings and without missings) have different variances.
All that I found so far was about independent samples, not about subsets.
Also, just to bring me up to speed: heteroscedasticity is usually used in the context of residual variance, right? What's a good term for constricted variance that would give me better luck with google?