A, B, C are categorical variables. A is a dependent variable against which I would like to find if B and C have any relationship.

Suppose P-value from a Chi-square test between categorical attribute A and B comes out to be 4%, and that between A and C comes out to be 1%.

  • Is it correct to say C has higher impact on A than B?
  • Should P-value be treated as binary (either significant as per predefined significance level or not significant)?
  • Do we know the "direction" of impact from P-value (e.g. the way we can interpret the correlation coefficient, ~1 -> higher positive correlation &
    ~-1 higher negative correlation)?
  • Consequently, can we rank a list of categorical variables based on the P- values with the dependent variables?

Kindly also suggest me if there are alternative ways of achieving the above objectives.

Please let me know if I need to be more clear in the question.


The answer to the questions in bullet points is no. And there is another approach, you need probably to fit some model. You didn't give much detail or context, so only: look into logistic or loglinear models, maybe.

Then your bulleted points:

  • No, p-value does not measure strengt of effect, only strength of
    evidence. You need some measure of effect size.
  • No, do not treat as binary. The numerical value keeps more
  • No, p-value do not indicate direction of effect, in any way. You
    need some measure of effect size.
  • No.

If you want more concrete proposals, we need much more context.


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