Re: st: Understanding Factor variables - is order significant ?

[Richard Williams <richardwilliams.ndu@gmail.com>]

At 08:32 PM 5/25/2010, Michael N. Mitchell wrote:
>   Extend that idea to your interaction... Suppose you flip the 
> coding of your "ra" and "dm" variables. Note that the test of the 
> interaction, the p value, will remain the same (assuming both are 
> dummy variables). The coefficients of "ra" and "dm" will change as 
> well, due to the change in coding. The details get more 
> complicated, but are explained in section 3.5 of 
> http://www.ats.ucla.edu/stat/stata/webbooks/reg/chapter3/statareg3.htm 
> . It is explained using the old "xi" terminology, but the issues 
> still are the same.

He is not changing the coding though.  He is just flipping the 
placement of the terms, i.e. b1.ra#b0.dm in one model and 
b0.dm#b1.ra.  Like using female * race versus using race * female.

I'd be curious to know if the two models did produce identical 
fits.  That would indicate whether the parameterizations are 
equivalent.  If not, then something is getting screwed up.

I suspect using ## instead of # might solve the problem -- and that 
would be my preference anyway.

The following code also produces inconsistent results, with the 3rd 
model being wrong.  It isn't clear to me why that is the case.

use "http://www.indiana.edu/~jslsoc/stata/spex_data/ordwarm2.dta";, clear
ologit  warm yr89#male, nolog
ologit  warm b0.male#b1.yr89, nolog
ologit  warm b1.yr89#b0.male, nolog

I hate to accuse Stata of having a bug, but I am starting to wonder...

-------------------------------------------
Richard Williams, Notre Dame Dept of Sociology
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