# Talk:Interaction between two continuous variables

(Difference between revisions)
 Revision as of 06:47, 18 April 2007 (view source)Widowil (Talk | contribs)← Older edit Revision as of 17:59, 24 April 2007 (view source)Ravi (Talk | contribs) Newer edit → Line 2: Line 2: So now we do not have multicollinearity, but we have an uninterpretable score...??? So now we do not have multicollinearity, but we have an uninterpretable score...??? Can anyone ease my mind on this one :-)? Can anyone ease my mind on this one :-)? + + + Here is the answer from one of our statistically minded grad students: + The magnitude and value of the interaction term is not important. One + examines the term for significance and then conducts the relative post hocs. + Just as in ANOVA a significant interaction term doesn't tell you anything. + You still need to do the post hocs to figure out the nature of the + interaction. Regression is identical. After all, ANOVA is a special case of + regression.

## Revision as of 17:59, 24 April 2007

I have a question. Whenever you center your two continuous variables and calculate the interaction term, would it be hard to correctly interpret the effect...? Suppose you have -2 as a centered score on x1 and -2 as a score on x2. Then the interactionterm would be 4. At the same time, another respondent with centered scores of x1=2 and x2=2 will end up with the same score on the interactionterm (4)...somehow it doesn't feel right.... So now we do not have multicollinearity, but we have an uninterpretable score...??? Can anyone ease my mind on this one :-)?

Here is the answer from one of our statistically minded grad students: The magnitude and value of the interaction term is not important. One examines the term for significance and then conducts the relative post hocs. Just as in ANOVA a significant interaction term doesn't tell you anything. You still need to do the post hocs to figure out the nature of the interaction. Regression is identical. After all, ANOVA is a special case of regression.