How many X's do you know'' surveys - Columbia University
How many X's do you know'' surveys - Columbia University
How many X's do you know'' surveys - Columbia University
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Overview<br />
Social and political polarization<br />
Background: how <strong>many</strong> people <strong>do</strong> <strong>you</strong> know?<br />
Learning from “<strong>How</strong> <strong>many</strong> X’s <strong>do</strong> <strong>you</strong> know” <strong>surveys</strong><br />
Next<br />
Evaluation of inferences using fake data<br />
3 models<br />
Fitting our model<br />
Results: how <strong>many</strong> people <strong>do</strong> <strong>you</strong> know?<br />
Results: group sizes and overdispersions<br />
Confidence building and model extensions<br />
Data: censored at 1<br />
Data: censored at 3<br />
Data: censored at 5<br />
Fake: censored at 1<br />
Fake: censored at 3<br />
Fake: censored at 5<br />
est. gregariousness, exp(αi)<br />
150 600 2400<br />
est. gregariousness, exp(αi)<br />
150 600 2400<br />
est. gregariousness, exp(αi)<br />
150 600 2400<br />
150 600 2400<br />
150 600 2400<br />
150 600 2400<br />
150 600 2400<br />
gregariousness, exp(αi)<br />
estimated w/o censoring<br />
gregariousness, exp(αi)<br />
estimated w/o censoring<br />
gregariousness, exp(αi)<br />
estimated w/o censoring<br />
Data: censored at 1<br />
Data: censored at 3<br />
Data: censored at 5<br />
est. % of network, exp(βk)<br />
0.01% 0.1% 1%<br />
est. % of network, exp(βk)<br />
0.01% 0.1% 1%<br />
est. % of network, exp(βk)<br />
0.01% 0.1% 1%<br />
0.01% 0.1% 1%<br />
0.01% 0.1% 1%<br />
0.01% 0.1% 1%<br />
% of network, exp(βk)<br />
estimated w/o censoring<br />
% of network, exp(βk)<br />
estimated w/o censoring<br />
% of network, exp(βk)<br />
estimated w/o censoring<br />
Data: censored at 1<br />
Data: censored at 3<br />
Data: censored at 5<br />
est. overdispersion, ωk<br />
1 3 5 7 9<br />
est. overdispersion, ωk<br />
1 3 5 7 9<br />
est. overdispersion, ωk<br />
1 3 5 7 9<br />
est. gregariousness, exp(αi)<br />
150 600 2400<br />
est. gregariousness, exp(αi)<br />
150 600 2400<br />
est. gregariousness, exp(αi)<br />
150 600 2400<br />
150 600 2400<br />
150 600 2400<br />
gregariousness, exp(αi)<br />
estimated w/o censoring<br />
gregariousness, exp(αi)<br />
estimated w/o censoring<br />
gregariousness, exp(αi)<br />
estimated w/o censoring<br />
Fake: censored at 1<br />
Fake: censored at 3<br />
Fake: censored at 5<br />
est. % of network, exp(βk)<br />
0.01% 0.1% 1%<br />
est. % of network, exp(βk)<br />
0.01% 0.1% 1%<br />
est. % of network, exp(βk)<br />
0.01% 0.1% 1%<br />
0.01% 0.1% 1%<br />
0.01% 0.1% 1%<br />
0.01% 0.1% 1%<br />
% of network, exp(βk)<br />
estimated w/o censoring<br />
% of network, exp(βk)<br />
estimated w/o censoring<br />
% of network, exp(βk)<br />
estimated w/o censoring<br />
Fake: censored at 1<br />
Fake: censored at 3<br />
Fake: censored at 5<br />
est. overdispersion, ωk<br />
1 3 5 7 9<br />
est. overdispersion, ωk<br />
1 3 5 7 9<br />
est. overdispersion, ωk<br />
1 3 5 7 9<br />
1 3 5 7 9<br />
1 3 5 7 9<br />
1 3 5 7 9<br />
1 3 5 7 9<br />
1 3 5 7 9<br />
1 3 5 7 9<br />
overdispersion, ωk<br />
estimated w/o censoring<br />
overdispersion, ωk<br />
estimated w/o censoring<br />
overdispersion, ωk<br />
estimated w/o censoring<br />
overdispersion, ωk<br />
estimated w/o censoring<br />
overdispersion, ωk<br />
estimated w/o censoring<br />
overdispersion, ωk<br />
estimated w/o censoring<br />
Gelman, DiPrete, Salganik, Teitler, Zheng<br />
Studying polarization using <strong>surveys</strong>