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Thread: Interaction effect in a logistic regression between cont and cat variables.

  1. #16
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    Re: Interaction effect in a logistic regression between cont and cat variables.




    Quote Originally Posted by noetsi View Post
    Is centering strongly recommended when adding interaction terms?
    Yes yes.

    I had no heard that.
    Me too, unfortunately! But I will never add an interaction to a model again before I center at least the involved continuous or ordinal variables. It is remotely possible that non-centered variables do not correlate strongly with their interactions, but most of the time some major multicollinearity happens when one does not center the variables and models the variables together with their interaction(s).

    As I understand it centering changes the definition of the intercept which is obviously important if you work with categorical variables (as I normally do).
    It can also cause other disturbances (like the one I am struggling with) and this is one of the many reasons I am a 'multicollinearity hater' , but it is a very good way to make sure the added interactions are not sources of multicollinearity.

    --------------------------------------

    Quote Originally Posted by noetsi View Post
    It sounds to me that it would be simpler to run a VIF or tolerance test and if they don't show MC not to worry about centering.
    That sounds quite fine to me, but the problem is most of the time, when you add some interactions to the model, the VIF indicates that there is multicollinearity here. So you need to either remove some variables (from main variables or interactions) or center your main variables.


    Here is a different question on interaction. Say you have the following
    Y=B0 +B1X1 + B2X2 +B3X3 + B4X1X2...

    And this was the correct model, that is variable 3 was not involved in an interaction. When you analyze the effects of B1 at specific levels of B2 (simple effects) do you still have to analyze it at specific levels of B3 -as with a three way interaction or not?

    I don't think so, but I ran across a reference that made me think you might.
    You mean would I prefer entering three- or four-way interactions into the model as well?

    Well I would if 1) the LRT showed me the model has become more accurately predictive 2) It did not cause my currently significant variables go non-significant (once the number of variables and interactions get too high, the model itself becomes excellent but many predictors become non-significant, so I prefer a less accurate model with a couple of significant predictors) and 3) if I could easily understand or interpret them. The two-way interactions seem quite disturbing most of the time, let alone three- or four-way interactions. In my field even a multivariate analysis (without or with a few two-sided interactions) can be disturbing for clinicians and reviewers and editors (I had this experience last week), so I would avoid higher-level interactions even if they favored my model, for practical limitations.

    ====================

    Quote Originally Posted by noetsi View Post
    No I mean if you are doing a two way interaction simple effect (you don't believe there are three or more level interactions) do you still need to analyze this at a specific level of the other IV as you would if there is three way interaction. That makes no sense to me to do, but I ran into an article that said you did.

    I don't worry about three or more level interactions. For one thing others have found them to be rare and even if they exist I doubt I could explain them. Its difficult enough to explain two way interaction. Three way interaction makes my head hurt

    Aha I think you mean like this article. Actually I would never do it because it is way beyond me and my audience! (not because it is not a good thing)
    "victor is the reviewer from hell" -Jake
    "victor is a machine! a publication machine!" -Vinux

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  3. #17
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    Re: Interaction effect in a logistic regression between cont and cat variables.

    No I mean if you are doing a two way interaction simple effect (you don't believe there are three or more level interactions) do you still need to analyze this at a specific level of the other IV as you would if there is three way interaction. That makes no sense to me to do, but I ran into an article that said you did.

    I don't worry about three or more level interactions. For one thing others have found them to be rare and even if they exist I doubt I could explain them. Its difficult enough to explain two way interaction. Three way interaction makes my head hurt
    "Very few theories have been abandoned because they were found to be invalid on the basis of empirical evidence...." Spanos, 1995

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    Re: Interaction effect in a logistic regression between cont and cat variables.

    Quote Originally Posted by Marvin85 View Post
    So Number of sexual partners plays a suppressor/moderating role in the interaction between SW (sexual worker) and HIV? I just would like to understand the logic if this regression to be able to explain it to my supervisor and to a not statistical experience audience.

    1. So when I regress HIV (DV), SW(IV) controlling for Number of sexual Partners (IV), the effect of SW means that I am compering Sex workers vs not sex workers with the same number of sexual partners right? For example, I am comparing a sexual workers vs a non-sexual workers with 20 sexual partners each so the effect of sexual partners is canceled/ controlled; is this statement correct?
    2. Seeing this from another perspective, number of sexual partners among this community does not influence participants likelihood to have HIV when controlling for sex workers status. How can I interpret this? Perhaps, This means the numbers of sexual partners a participant has not predict HIV status if he/she is a sex worker? This will be the same for participants who are not sex workers?

    Thank you in advance. I would like to make a case with this information and perhaps write a manuscript.

    Best,
    Marvin
    Wow .. I am very thankful with all you guys. Tarek your response was outstanding, as well as Victor and Noetsi. I will follow your advice and will get back to you guys. My ultimate goal with this post is to be able to explain a non statistical experience audience this finding (assuming that it is valid) and perhaps write a paper for publication. What would be a nice takeaways or conclusions of this finding (again let suppose it is correct).

    Thank you all!

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    Re: Interaction effect in a logistic regression between cont and cat variables.

    If the model is correct then multiocolinearity will not bias your coefficients, it will just make your estimates (for those coefficients) inefficient (i.e. the standard errors will be bigger than they should be).

    So if he likes the model then I see no reason not to go ahead and interpret the coefficients.

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    Re: Interaction effect in a logistic regression between cont and cat variables.


    I am trying to figure out why victor deleted his own post

    I agree with threestars although because the SE are inflated its possible that a variable might be statistically signficant but not show up that way in the test. Particularly with p values near signficance this should be pointed out. Journals are likely to object if you have high MC and don't address it. But the coefficients will be (as threestar notes) correct despite MC.

    MC is one of the hardest problems to deal with (although I hear now that centering will sometimes do this). You might consult the Fox Sage monograph on Regression Diagnostics although the long and short of it is that he says there is no easy way to deal with it Well he also notes that MC has to be really estreme to matter.
    "Very few theories have been abandoned because they were found to be invalid on the basis of empirical evidence...." Spanos, 1995

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