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Thread: Rare suppressor effect?

  1. #1
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    Rare suppressor effect?




    Hey, I recently discovered a somewhat surprising result in a hierarchical regression:


    I have three predictors (C M N) and one outcome (D).

    Model 1 Regression (also with backgroun variables):
    C: B = -.06, p <.05

    Model 2 Regression:
    C: B = .05, p<.01
    M: B = -26, p<.01
    N: B = .56, p<.01

    If i run the same regression but including only one of the two other predictors in model 2, the same pattern emerges, but C is not significant.

    Scatter C and D:
    http://tinypic.com/r/a2br6o/8

    Correlations:

    http://tinypic.com/r/1gofmv/8

    My thoughts is that C has variance both negatively and positively related to the outcome, although more negatively related (hence, the correlation coefficient). The same negative variance is also shared partly by N and partly by M, which "removes" this variance in the calcuation of the coefficient.

    Any thoughts, interpretations?

    PS: Maybe I've been too nice with the scatter plot when i was eye-balling linearity?

  2. #2
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    Re: Rare suppressor effect?

    Anyone? I would very much appreciate some help to interpret, or just a comment

  3. #3
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    Re: Rare suppressor effect?


    Bumping this for still living unknowingly of what this effect means.

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