David K Park
Biographic Data
| ID | 4371899 |
|---|---|
| NAME | David K Park |
| GIVEN NAMES | David K |
| FAMILY NAME | Park |
| SIGNATURE | PARK D K |
| AFFILIATIONS | Washington University in St. Louis |
| ORCID | 0000-0001-9725-0193 |
| VERIFIED | Yes |
| TOTAL WORKS | 5 |
| TOTAL CITATIONS | 324 |
| WORKS AS AUTHOR | 5 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2004 |
| LATEST PUBLICATION YEAR | 2016 |
| H-INDEX | 4 |
Data Science and Political Economy
Sharyn O'Halloran, Sameer Maskey, Geraldine McAllister, David K. Park, Kaiping Chen, Data Science and Political Economy: Application to Financial Regulatory Structure, RSF: The Russell Sage Foundation Journal of the Social Sciences, Vol. 2, No. 7, Big Data in Political Economy (November 2016), pp. 87-109
Polls and Elections Understanding Persuasion and Activation in Presidential Campaigns
Political campaigns are commonly understood as random walks, during which, at any point in time, the level of support for any party or candidate is equally likely to go up or down. Each shift in the polls is then interpreted as the result of some combination of news and campaign strategies. A completely different story of campaigns is the mean reversion model in which the elections are determined by fundamental factors of the economy and partisan…
Dialogue in American Political Campaigns? An Examination of Issue Convergence in Candidate Television Advertising
The theory of issue ownership holds that competing candidates should avoid discussing many of the same issues during a campaign. In contrast, theories of democracy suggest that competitive elections are the mechanism by which the public can hold politicians accountable. To determine the extent to which each theory depicts current campaigns, we develop a new measure of “issue convergence” and test whether or not issue convergence increases as the …
Practical Issues in Implementing and Understanding Bayesian Ideal Point Estimation
Logistic regression models have been used in political science for estimating ideal points of legislators and Supreme Court justices. These models present estimation and identifiability challenges, such as improper variance estimates, scale and translation invariance, reflection invariance, and issues with outliers. We address these issues using Bayesian hierarchical modeling, linear transformations, informative regression predictors, and explici…
Bayesian Multilevel Estimation with Poststratification
We fit a multilevel logistic regression model for the mean of a binary response variable conditional on poststratification cells. This approach combines the modeling approach often used in small-area estimation with the population information used in poststratification (see Gelman and Little 1997,Survey Methodology23:127–135). To validate the method, we apply it to U.S. preelection polls for 1988 and 1992, poststratified by state, region, and the…
Bayesian Multilevel Estimation with Poststratification
We fit a multilevel logistic regression model for the mean of a binary response variable conditional on poststratification cells. This approach combines the modeling approach often used in small-area estimation with the population information used in poststratification (see Gelman and Little 1997,Survey Methodology23:127–135). To validate the method, we apply it to U.S. preelection polls for 1988 and 1992, poststratified by state, region, and the…
Dialogue in American Political Campaigns? An Examination of Issue Convergence in Candidate Television Advertising
The theory of issue ownership holds that competing candidates should avoid discussing many of the same issues during a campaign. In contrast, theories of democracy suggest that competitive elections are the mechanism by which the public can hold politicians accountable. To determine the extent to which each theory depicts current campaigns, we develop a new measure of “issue convergence” and test whether or not issue convergence increases as the …
Practical Issues in Implementing and Understanding Bayesian Ideal Point Estimation
Logistic regression models have been used in political science for estimating ideal points of legislators and Supreme Court justices. These models present estimation and identifiability challenges, such as improper variance estimates, scale and translation invariance, reflection invariance, and issues with outliers. We address these issues using Bayesian hierarchical modeling, linear transformations, informative regression predictors, and explici…
Polls and Elections Understanding Persuasion and Activation in Presidential Campaigns
Political campaigns are commonly understood as random walks, during which, at any point in time, the level of support for any party or candidate is equally likely to go up or down. Each shift in the polls is then interpreted as the result of some combination of news and campaign strategies. A completely different story of campaigns is the mean reversion model in which the elections are determined by fundamental factors of the economy and partisan…
Data Science and Political Economy
Sharyn O'Halloran, Sameer Maskey, Geraldine McAllister, David K. Park, Kaiping Chen, Data Science and Political Economy: Application to Financial Regulatory Structure, RSF: The Russell Sage Foundation Journal of the Social Sciences, Vol. 2, No. 7, Big Data in Political Economy (November 2016), pp. 87-109
Bayesian Multilevel Estimation with Poststratification
We fit a multilevel logistic regression model for the mean of a binary response variable conditional on poststratification cells. This approach combines the modeling approach often used in small-area estimation with the population information used in poststratification (see Gelman and Little 1997,Survey Methodology23:127–135). To validate the method, we apply it to U.S. preelection polls for 1988 and 1992, poststratified by state, region, and the…
Practical Issues in Implementing and Understanding Bayesian Ideal Point Estimation
Logistic regression models have been used in political science for estimating ideal points of legislators and Supreme Court justices. These models present estimation and identifiability challenges, such as improper variance estimates, scale and translation invariance, reflection invariance, and issues with outliers. We address these issues using Bayesian hierarchical modeling, linear transformations, informative regression predictors, and explici…
Dialogue in American Political Campaigns? An Examination of Issue Convergence in Candidate Television Advertising
The theory of issue ownership holds that competing candidates should avoid discussing many of the same issues during a campaign. In contrast, theories of democracy suggest that competitive elections are the mechanism by which the public can hold politicians accountable. To determine the extent to which each theory depicts current campaigns, we develop a new measure of “issue convergence” and test whether or not issue convergence increases as the …
Polls and Elections Understanding Persuasion and Activation in Presidential Campaigns
Political campaigns are commonly understood as random walks, during which, at any point in time, the level of support for any party or candidate is equally likely to go up or down. Each shift in the polls is then interpreted as the result of some combination of news and campaign strategies. A completely different story of campaigns is the mean reversion model in which the elections are determined by fundamental factors of the economy and partisan…
Data Science and Political Economy
Sharyn O'Halloran, Sameer Maskey, Geraldine McAllister, David K. Park, Kaiping Chen, Data Science and Political Economy: Application to Financial Regulatory Structure, RSF: The Russell Sage Foundation Journal of the Social Sciences, Vol. 2, No. 7, Big Data in Political Economy (November 2016), pp. 87-109
Electoral Systems and Political Participation (4 works) · Political science (4 works) · Econometrics (3 works) · Economics (3 works) · Law (3 works) · Politics (3 works) · Social Media and Politics (3 works) · Business (2 works) · Computer Science (2 works) · Law (2 works)