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A Change in Attitudes Toward Muslims? A Bayesian Investigation of Pre and Post 9/11 Public Opinion
Unformatted Document Text:  Aleks, and Su 2008a). In addition, we assign noninformative priors Dirichlet(1) on response categories κ’s so that each κ is augmented by an additional count. We pooled the four datasets together and include year indicators of 2001, 2002, and 2005. This way, we try to see if any of these years, compared to the base year of 2000, has an effect on the prediction of American’s attitudes toward Muslim. We find similar results in the analysis of the ethnocentric structure of Muslim affect. Under the Bayesian pooled ordered logistic model with year fixed effects, Figure 2 1 tells us that the best predictor of attitudes toward Muslims is the favorability of Jews. Individuals who like (dislike) Jews also like (dislike) Muslims when we control the model for demographic characteristic 2 . The variables of our main interest are the year effects, or lack thereof. Neither years (2001, 2002, and 2005) have any effect on Muslim affect. In other words, the ethnocentric structure of Muslim affect persists during both the pre and post 9/11 periods. Next, we fit separate ordered logistic regressions to the datasets of four different years (see Figure 3) to allow within variations. We want to see the structure of cutpoints separately for each year. Overall, the estimates are similar to those of the pooled model. To compare the estimates of four different years, I set the predictors X at the mean values. In other words, we are comparing non-white, and male Americans in their mid-age who have mid-education, mid-income, live in the North, and hold neutral feeling toward Jews. Under this condition of Xβ = 0, we can compare four sets of cutpoints across four different year. 1 Figure 2 displays the estimates of the ordered logistic regression using the function bayespolrof R package arm (Gelman et al. 2008b) 2 The descriptive analysis of these demographic characteristics is in Figure A, Figure B, andFigure C. To facilitate interpretation and to ensure modeling stability, we rescale all the predic-tors, except of the binary ones, by subtracting the means and divided by 2 standard deviations(Gelman Forthcoming). Thus, zero becomes the mean values of the predictors that are notbinary. 12

Authors: Kalkan, Kerem. and Su, Yu-Sung.
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Aleks, and Su 2008a). In addition, we assign noninformative priors Dirichlet(1) on
response categories κ’s so that each κ is augmented by an additional count. We pooled
the four datasets together and include year indicators of 2001, 2002, and 2005. This
way, we try to see if any of these years, compared to the base year of 2000, has an
effect on the prediction of American’s attitudes toward Muslim.
We find similar results in the analysis of the ethnocentric structure of Muslim
affect. Under the Bayesian pooled ordered logistic model with year fixed effects,
Figure 2
1
tells us that the best predictor of attitudes toward Muslims is the favorability
of Jews. Individuals who like (dislike) Jews also like (dislike) Muslims when we control
the model for demographic characteristic
2
. The variables of our main interest are the
year effects, or lack thereof. Neither years (2001, 2002, and 2005) have any effect on
Muslim affect. In other words, the ethnocentric structure of Muslim affect persists
during both the pre and post 9/11 periods.
Next, we fit separate ordered logistic regressions to the datasets of four different
years (see Figure 3) to allow within variations. We want to see the structure of
cutpoints separately for each year. Overall, the estimates are similar to those of the
pooled model. To compare the estimates of four different years, I set the predictors X
at the mean values. In other words, we are comparing non-white, and male Americans
in their mid-age who have mid-education, mid-income, live in the North, and hold
neutral feeling toward Jews. Under this condition of Xβ = 0, we can compare four
sets of cutpoints across four different year.
1
Figure 2 displays the estimates of the ordered logistic regression using the function bayespolr
of R package arm (Gelman et al. 2008b)
2
The descriptive analysis of these demographic characteristics is in Figure A, Figure B, and
Figure C. To facilitate interpretation and to ensure modeling stability, we rescale all the predic-
tors, except of the binary ones, by subtracting the means and divided by 2 standard deviations
(Gelman Forthcoming). Thus, zero becomes the mean values of the predictors that are not
binary.
12


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