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From |
Maarten buis <maartenbuis@yahoo.co.uk> |

To |
"statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu> |

Subject |
Re: st: Interaction terms |

Date |
Tue, 3 May 2011 14:59:28 +0100 (BST) |

On Tue, May 3, 2011 at 3:05 PM, lreine ycenna wrote: > (1) I'm running a regression, wanting to see the effect of overseas living > experience (OV) on one's income (y) which is also determined by eudcation > level (edu) and wealth. > > regress y ov edu wealth ovxedu ovxwealth. > > I also want to see the effect of overseas experience on y with a mixed > variable: edu and wealth. > > If I run regress y ov edu wealth eduxwealth ovxedu ovxwealth ovxeduxwealth, > I might run into collinearity problems when I include more variables in the > future. There is no such thing as a (multi-)collinearity problem(*). Strong correlations between explanatory variables will increase the standard error, but that is exactly what should happen: strong correlation means it is hard to distinguish one variable from the other which is necessary in order to separate the effects of the two variables. So the increase in standard error is an accurate representation of the amount of information available. If you think you need the threeway interactions, than you should include them regardless of what it does to your standard errors. In that case you will just have to live with the fact that your test will have little statistical power, i.e. your are very unlikely to find a significant result. Remember that a non- significant result implies an "absence of evidence" not "evidence of absence", in other words a non-significant results means "we do not know" rather than "the effect is 0". This is especially important with tests with little statistical power. > or can I run a separate regression such as regress y ov wealthxedu > ovxwealthxedu? Which one is correct? The only one who can answer that question is you: you are the researcher so you decide what model is a correct representation of your theory. > (2) when I run regress y ov edu wealth eduxwealth ovxedu ovxwealth > ovxeduxwealth, OV has a negative coefficient, > > but it changes to positive when I run regress y ov wealthxedu > ovxwealthxedu. What does this suppose to mean? With interaction the main effect of ov means the effect of ov when wealth and edu are both equal to zero. If you did not center wealth and edu before creating the interaction terms, that result is probably pretty meaningless. > (3) I have very large standard errors (ranged from 0-14) when running > both equations. Does this mean that the results are very 'imprecise'? To determine whether a standard error is large or small we also need to know the size of the effects, but large standard errors/imprecise results are to be expected when you include interactions. Hope this helps, Maarten (*) For that reason Gujarati (1995, chapter 10) called the multi-colinearity problem the "micro-numerosity" problem: correlation among explanatory variables costs statistical power to the point that we just don't have enough observations to find what we set out to find. We cannot change the correlation among explanatory variables, so the only thing we can do is to gather more data, i.e. the problem is not the correlations but the too small dataset, hence micro-numerosity (Sorry, a joke tend to loose its edge when you explain it). Damodar N. Gujarati (1995) Basic Econometrics, third edition. McGraw-Hill. -------------------------- Maarten L. Buis Institut fuer Soziologie Universitaet Tuebingen Wilhelmstrasse 36 72074 Tuebingen Germany http://www.maartenbuis.nl -------------------------- * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**Re: st: Interaction terms***From:*Nick Winter <nwinter@virginia.edu>

**References**:**st: Interaction terms***From:*lreine ycenna <lreine.ycenna@gmail.com>

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