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Explaining Information Effects in Collective Preferences
Unformatted Document Text:  position of Clinton and Dole on government services (69.5%), job assurance (64.5%), health insurance (65.5%), environmental regulation (51.9%), affirmative action (60.8%), and abortion (58.4%). Respondents in the lowest knowledge quartile averaged 4.7 correct answers out of 16 possible, while respondents in the highest knowledge quartile averaged 14.2 correct answers. Simulation Method The method I use for simulating “fully informed” preferences extends the simulation approach developed by Bartels (1996) and by Delli Carpini and Keeter (1996). I use logistic regression to avoid the restrictive assumption that political information must have a linear relationship with preferences. Delli Carpini and Keeter use ordinary least squares to estimate parameters in their simulation; Bartels uses a probit model to estimate parameters, but later transforms those parameters in a way that assumes linearity of information effects. I also estimate “fully informed” preferences for people who give “don’t know” and “no opinion” responses in the survey data, following the assumption that as information levels rise, the proportion of people who give opinions or turn out to vote should also rise. The logit model I use for simulating “fully informed” opinions in dichotomous questions is structured as follows: prob Y i = 1 ( ) = α + β 1 I i + ∑ β k D ik + ∑ δ k I i ∗ D ik ( ) + e i , where Yi is respondent i’s policy preference (e.g., 1 for “favor”, 0 for “oppose”), Ii is respondent i’s score on a scale of political information, Dik is respondent i’s score on the kth demographic characteristic, Ii *Dik is the product of respondent i’s information score multiplied by respondent i’s score on the kth demographic characteristic, and ei is the error term for the ith observation. In this equation, β 1 is the coefficient for the information variable, β k is the coefficient for the kth demographic characteristic, and δ k is the coefficient for the kth interaction term. An ordered logit model is used when estimating “fully informed” opinions in trichotomous questions. After completing the four-step simulation procedure described in Althaus 1998 (and elaborated in Althaus 2003), the mean of the Yi probabilities from hypothetically “fully

Authors: Althaus, Scott.
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position of Clinton and Dole on government services (69.5%), job assurance (64.5%), health
insurance (65.5%), environmental regulation (51.9%), affirmative action (60.8%), and abortion
(58.4%). Respondents in the lowest knowledge quartile averaged 4.7 correct answers out of 16
possible, while respondents in the highest knowledge quartile averaged 14.2 correct answers.
Simulation Method
The method I use for simulating “fully informed” preferences extends the simulation
approach developed by Bartels (1996) and by Delli Carpini and Keeter (1996). I use logistic
regression to avoid the restrictive assumption that political information must have a linear
relationship with preferences. Delli Carpini and Keeter use ordinary least squares to estimate
parameters in their simulation; Bartels uses a probit model to estimate parameters, but later
transforms those parameters in a way that assumes linearity of information effects. I also estimate
“fully informed” preferences for people who give “don’t know” and “no opinion” responses in
the survey data, following the assumption that as information levels rise, the proportion of people
who give opinions or turn out to vote should also rise. The logit model I use for simulating “fully
informed” opinions in dichotomous questions is structured as follows:
prob Y
i
=
1
(
)
=
α + β
1
I
i
+ ∑
β
k
D
ik
+ ∑
δ
k
I
i
D
ik
(
)
+
e
i
,
where Yi is respondent i’s policy preference (e.g., 1 for “favor”, 0 for “oppose”), Ii is respondent
i’s score on a scale of political information, Dik is respondent i’s score on the kth demographic
characteristic, Ii *Dik is the product of respondent i’s information score multiplied by respondent
i’s score on the kth demographic characteristic, and ei is the error term for the ith observation. In
this equation,
β
1 is the coefficient for the information variable,
β
k is the coefficient for the kth
demographic characteristic, and
δ
k is the coefficient for the kth interaction term. An ordered logit
model is used when estimating “fully informed” opinions in trichotomous questions.
After completing the four-step simulation procedure described in Althaus 1998 (and
elaborated in Althaus 2003), the mean of the Yi probabilities from hypothetically “fully


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