Hi Maya,
This is a response to your clarification for calculation of first differences.
When we use setx ( ) and sim ( ) for calculating first differences, we can explicitly set
"contact" to a preferred value. Since you mentioned that zelig uses mean (for
numerical vars) and median (for categorical vars) as default values on other covariates, I
got a bit confused. I guess we do not need to go beyond using setx for anything else but
contact. Can you confirm that?
Sim ( ) will give us a slightly different value each time we run it, but that's due
to the fact that the function calculates simulated first differences. Does that make
sense? Do we need to do something more for calculating first differences in 1b. Also, in
1d, you wrote "Simulate first differences in levels of attention to the campaign when
going from the lowest to the highest educational category". Do you imply calculating
one first difference for two observations where educ is min in one and max in other, or do
you in fact imply calculating first differences between all educational levels, moving
from the lowest to the highest level in the increment of one level?
Your help is appreciated.
Nino Malekovic
MPA Candidate, Class 2011
Harvard Kennedy School
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Today's Topics:
1. Regression variables (Gavinlertvatana, Poj)
2. clarification for first differences (Maya Sen)
3. Re: Regression variables (Iain Osgood)
4. how many leaders, really? (Mark Brewster)
5. Re: how many leaders, really? (Mark Brewster)
6. Re: how many leaders, really? (Maya Sen)
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Message: 1
Date: Wed, 7 Apr 2010 15:36:14 -0400
From: "Gavinlertvatana, Poj" <pgavinlertvatana at hbs.edu>
Subject: [gov2001] Regression variables
To: Class List for Gov 2001/E-2001 <gov2001-l at lists.fas.harvard.edu>
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Hi all,
I'm test two models in linear regression, and I get this situation:
* When I add variable X as a covariate, it is significant.
* When I add variable X*Y interaction, X*Y is significant but X becomes
insignificant
The fit (R-squared) is the equal for both models (c. 0.9).
How would I choose one over another? The former is more parsimonious, but the second is
just as valid, isn't it? I want to find arguments to choose model 2 over model 1,
but can't really find a justification.
Best regards,
Joseph
Joseph Poj Gavinlertvatana
Doctoral student, Marketing
Harvard Business School
Wyss Hall, Soldiers Field, Boston, MA 02163
Ph 617.230.5907
Fx 617.496.4397
Txt/Vm 617.910.0563
Em pgavinlertvatana at
hbs.edu