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Re: st: RE: xi3: mlogit effect coding

From   Maarten buis <>
Subject   Re: st: RE: xi3: mlogit effect coding
Date   Fri, 2 Feb 2007 08:42:17 +0000 (GMT)

--- Diego Bassani <> wrote:
> but that would apply if we were using dummy variables.
> When I use effect coding I am expressing the change in the beta
> compared to a grand mean of the betas in the population (all
> categories combined). This is what is making me think that it is very
> unlikely to be an odds, relative odds or odds ratio since the
> information from the numerator is used to calculate the reference
> value in the denominator.

I still think that you can think of these as odds ratios. Lets take a
concrete example: the example below. The parameter in the top panel are
the odds ratios of dropping out of highschool versus going to college.
So I would interpret _Irace_2 (black) as: the odds that someone drops
out of highschool versus going to college is twice as large for a black
women then for a women of average race. (The dataset is about women)

All the different codings possible with -xi3- are there to make
interpretation easier. They all lead to exactly the same model, the
results are just displayed in a different way. So if you find effect
coding difficult in this context, then just go back to dummy coding.

*------------- begin example -------------
set more off
sysuse nlsw88, clear
gen ed = grade
recode ed (0/11 = 1) (12 = 2) (13/18 = 3)
label define ed 1 "dropout" 2 "highschool" 3 "college"
label values ed ed
xi3: mlogit ed e.race south age, rrr

nlcom (dropout: exp(-[dropout]_b[_Irace_2]-[dropout]_b[_Irace_3])) /*
*/ (highschool:
*--------------- end example ---------------

Hope this helps,

Maarten L. Buis
Department of Social Research Methodology
Vrije Universiteit Amsterdam
Boelelaan 1081
1081 HV Amsterdam
The Netherlands

visiting address:
Buitenveldertselaan 3 (Metropolitan), room Z434

+31 20 5986715

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