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

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Multicollinearity test |

Date |
Fri, 5 Feb 2010 11:41:24 -0800 (PST) |

> There are two other situations: (3) X1 and X2 are inter-related with > each other, but there is no clear direction of the relationship. This > means there is no clear theory to identify which factor causes which. > (4) X1 and X2 are not related theoretically, although statistically > correlated. > > My questions are: (1) How would we deal with the third situation? You have to determine whether a variable is an intervening variable or a confounding variable in order to decide whether or not to include a variable. So you really need to get that clear. A possibly very complicated problem is when x1 influences x2 _and_ x2 influences x1. In that case you can either a priori decide which direction is the more important / dominant one, or you'll have to use additional information to try to disentangle those (e.g. instrumental variables or panel data) > (2) The fourth situation should be a multicollinearity problem, and > what shall we do if findings from correlation and VIF tests are not > consistent? You should only control for variables you think are confounding variables, so if you believe that the two should not be related, then you believe that x2 is not a confounding variable, so it should not be in your model. Multicollinearity it is just the phenonemenon that you loose power when two explanatory variables are correlated. This is not a problem, this is exactly as it should be. When we enter two (or more) variables in a model, then we have to be able to distinguish between the two variables. If two variables are highly correlated, then we have a lot of trouble distinguishing between the two, so we become more uncertain about their individual effects, so we should get less power. In that sense VIF and correlation are useful to understand your model, but they do not help you in deciding which model to choose. Hope this helps, Maarten -------------------------- 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/

**References**:**Re: st: Multicollinearity test***From:*Rosie Chen <jiarongchen2002@yahoo.com>

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