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  • Negative coefficient interpretation

    Dear Researchers,
    What I know about the interpretation of the negative coefficient of the regression analysis is that as the independent variable increases by one unit, it is expected that the dependent variable will decrease by the value of the coefficient of the independent variable. But, can we say a decrease by one unit in the independent variable causes an increase in the dependent variable by the value of the coefficient of the independent variable.

    Thank you in advance.

  • #2
    Let's see . . .



    . display in smcl as text -1 * -1
    1

    .


    I don't know about causes, but, other than that, yeah, I guess you can!

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    • #3
      That is what the model implies. But in this blog post and article, Paul Allison argues that symmetry assumptions may be questionable.

      https://statisticalhorizons.com/asym...for-panel-data

      https://journals.sagepub.com/doi/10....78023119826441
      -------------------------------------------
      Richard Williams, Notre Dame Dept of Sociology
      StataNow Version: 19.5 MP (2 processor)

      EMAIL: [email protected]
      WWW: https://www3.nd.edu/~rwilliam

      Comment


      • #4
        Thank you Richard,

        Now, I have understood the case, so I will decompose the difference score for each predictor variable into positive and negative values and I will run the regression. Thank you very much,

        Comment


        • #5
          Dear Richard,
          I have a quick question for you, please.

          In the above article that is available in the second link that you have mentioned, particularly in the abstract, Allison (2019) mentioned in the abstract that “The effect of increasing a variable is the same as the effect of decreasing that variable but in the opposite direction. This is implausible for many social phenomena. York and Light showed how to estimate asymmetric models by estimating first-difference regressions in which the difference scores for the predictors are decomposed into positive and negative changes”.

          So, my question is that we should decompose the predictor into positive and negative scores to precisely specify the direction for positive and negative values WHEN the main predictor before the decomposing has a negative coefficient BUT, when it has a positive coefficient, there is no need to decompose it because the direction is clear?
          Do you think I am correct? or what do you think?

          Many thanks in advance.

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