Notice: On March 31, it was **announced** that Statalist is moving from an email list to a **forum**. The old list will shut down at the end of May, and its replacement, **statalist.org** is already up and running.

[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

From |
Maarten Buis <maartenlbuis@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: high correlation interaction & main effect |

Date |
Tue, 5 Mar 2013 10:32:57 +0100 |

On Tue, Mar 5, 2013 at 6:12 AM, Caroline Wilson wrote: > I'm using Stata to run a multilevel model. I have a few interaction variables in my model, which are the product of a continuous variable (time) and a categorical variable. My variable for time was defined as years from the start of the study, to the nearest day, and was defined this way because it makes sense in the context of my study. The interaction variables are in some cases highly correlated (0.8) with the main effects. > A couple of questions: > 1. How much should I worry about the high correlation? > 2. What, practically speaking, can I do about the high correlation? I'd like to keep the interaction terms in the model since they represent my key research question. You should know about it, and know about the consequences: low power, i.e. you are less likely to find a effect when you should. This is not nice but it is an accurate representation of the amount of information available in the data. One way you can see this is by looking at the minumum number of observations necessary to estimate an effect. The larger that minimum, the less information is present in each individual observation. Consider a main effect. The absolute minimum number of observations necesary for estimating it is 2: one observation in group 1, the other in group 2 and the difference in response is your effect. For an interaction effect the absolute minimum number of observation necessary is 4: You need 2 observations in group a, one each in group 1 and 2, in order to compute the effect of group 1 versus 2 in group a. You need another 2 observations in group b, one each in group 1 and 2, in order to compute the effect of group 1 versus 2 in group b. The difference in these effects is your interaction effect. So striclty speaking there is nothing you can and should do about this high correlation. -- Maarten --------------------------------- Maarten L. Buis WZB Reichpietschufer 50 10785 Berlin Germany http://www.maartenbuis.nl --------------------------------- * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**RE: st: high correlation interaction & main effect***From:*Caroline Wilson <wilson_cj@hotmail.com>

**References**:**st: high correlation interaction & main effect***From:*Caroline Wilson <wilson_cj@hotmail.com>

- Prev by Date:
**Re: st: test difference in marginal effects for the same dummy variable, computed first as difference and then as derivative** - Next by Date:
**Re: st: Question about interactions** - Previous by thread:
**st: high correlation interaction & main effect** - Next by thread:
**RE: st: high correlation interaction & main effect** - Index(es):