PS: Political Science & Politics

PS: Political Science & Politics provides critical analyses of contemporary political phenomena and is the journal of record for the discipline of political science reporting on research, teaching, and professional development. PS, begun in 1968, is the only quarterly professional news and commentary journal in the field and is the prime source of information on political scientists' achievements and professional concerns.

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231 to 240 of 274 Results

Pooled WCPS Data 2013-2019 Oct 21, 2020 - Harvard Dataverse Lavariega Monforti, Jessica, 2020, "Pooled WCPS Data 2013-2019", https://doi.org/10.7910/DVN/SNLJB6, Harvard Dataverse, V1 Survey data from 2013, 2015, 2017, and 2019 Women of Color in Political Science Workshops.
Replication Data for: Reply to "Powerless Conservatives or Powerless Findings?" Oct 16, 2020 Zigerell, Lawrence, 2020, "Replication Data for: Reply to "Powerless Conservatives or Powerless Findings?"", https://doi.org/10.7910/DVN/CJQROH, Harvard Dataverse, V1 Reply to "Powerless Conservatives or Powerless Findings?"
Replication Data for: Research Career Paths Among Political Scientists in Research Institutions Oct 5, 2020 - Harvard Dataverse Hill, Kim, 2020, "Replication Data for: Research Career Paths Among Political Scientists in Research Institutions", https://doi.org/10.7910/DVN/G7KPHT, Harvard Dataverse, V1, UNF:6:nvHh0Ais+haR6CBEeSbZPA== [fileUNF] Considerable work assesses research success in political science. Yet the latter work has not taken account of widespread findings that scholars can follow a variety of career research paths that complicate how we envision scholarly success. Further, we have no systematic information on these career paths in any scientific discipline. I present an...
Replication Data for: “Learning through Peer Reviewing and Publishing in the Pi Sigma Alpha Undergraduate Journal of Politics: Twenty Years Later” Oct 5, 2020 Walker, Christina; Towner, Terri, 2020, "Replication Data for: “Learning through Peer Reviewing and Publishing in the Pi Sigma Alpha Undergraduate Journal of Politics: Twenty Years Later”", https://doi.org/10.7910/DVN/FVKAGM, Harvard Dataverse, V1 The Pi Sigma Alpha Undergraduate Journal of Politics, sponsored by the Pi Sigma Alpha National Honor Society, was founded in 2001 at Purdue University. After 20 years, much has changed in undergraduate research and publishing, but the benefits of producing a peer-reviewed journal remain the same. Undergraduate research has increased in prominence,...
Replication Data for: Primary Model Predicts Trump Re-election Sep 23, 2020 Norpoth, Helmut, 2020, "Replication Data for: Primary Model Predicts Trump Re-election", https://doi.org/10.7910/DVN/IBFHRE, Harvard Dataverse, V1, UNF:6:M0i5mP3JJBMs0h2CXFHL8Q== [fileUNF] Electoral votes and primary support
Replication Data for: STATE-LEVEL FORECASTS FOR THE 2020 U.S. PRESIDENTIAL ELECTION: TOUGH VICTORY AHEAD FOR BIDEN Sep 23, 2020 JEROME, Bruno, 2020, "Replication Data for: STATE-LEVEL FORECASTS FOR THE 2020 U.S. PRESIDENTIAL ELECTION: TOUGH VICTORY AHEAD FOR BIDEN", https://doi.org/10.7910/DVN/QUPBSR, Harvard Dataverse, V1, UNF:6:DsZQdCaOMpmtl7IoJnzKcg== [fileUNF] Most forecasting models for American presidential elections provide estimates of the national two-party vote. Since popular vote winners generally win a majority of Electoral College votes, these models can normally assume that their forecast offers a clear indication of who will end up in the Oval Office. Evidently, this assumption is not always w...
Replication Data for: It's the Pandemic Stupid: A Simplified Model for Forecasting the 2020 Presidential Election Sep 23, 2020 Abramowitz, Alan, 2020, "Replication Data for: It's the Pandemic Stupid: A Simplified Model for Forecasting the 2020 Presidential Election", https://doi.org/10.7910/DVN/NQWLFH, Harvard Dataverse, V1, UNF:6:IAgH6CY+tWauGk0xr5dKNA== [fileUNF] Data for incumbent forecasting model
Replication Data for: Social Presence as Best Practice: The Online Classroom Needs to Feel Real Sep 18, 2020 Daigle, Delton T; Stuvland, Aaron, 2020, "Replication Data for: Social Presence as Best Practice: The Online Classroom Needs to Feel Real", https://doi.org/10.7910/DVN/OGTHTO, Harvard Dataverse, V1, UNF:6:5mI49dRCYtwJNOIT61wq0A== [fileUNF] As universities around the world stopped delivering face to face classes, the nonintentional creation of many online digital learning spaces has led to much speculation on “best practices” for virtual course delivery. Our evidence shows that the highest educational value comes from optimizing the “social presence” of your classroom environment. Usi...
Replication Data for: Assessing the Renaissance of Individuals in International Relations Sep 18, 2020 Holmes, Marcus; Jordan, Richard; Parajon, Eric, 2020, "Replication Data for: Assessing the Renaissance of Individuals in International Relations", https://doi.org/10.7910/DVN/PGBDNS, Harvard Dataverse, V1, UNF:6:LMqsb0pap31zQyXb709g7w== [fileUNF] The study of microfoundations, especially individuals, is enjoying something of a renaissance in international relations (IR) scholarship. Yet, this rise is harder to find in publication data. Using the Teaching, Research, and International Policy (TRIP) journal article database, we show that only 13.7% of IR articles in twelve leading journals emp...
Replication Data for: A Long-Range State-Level Forecast of the 2020 Presidential Election Sep 18, 2020 DeSart, Jay, 2020, "Replication Data for: A Long-Range State-Level Forecast of the 2020 Presidential Election", https://doi.org/10.7910/DVN/6GWHQB, Harvard Dataverse, V1, UNF:6:1VK3Ubh0iyz5ppZbWK6orw== [fileUNF] Most U.S. presidential election forecast models produce forecasts three to five months in advance of the election, and often only generate a prediction of the national popular vote. This paper presents a model that generates predictions of state-level outcomes a year in advance of the election, which can then be extrapolated up to national popular...

Pooled WCPS Data 2013-2019

Oct 21, 2020 - Harvard Dataverse

Lavariega Monforti, Jessica, 2020, "Pooled WCPS Data 2013-2019", https://doi.org/10.7910/DVN/SNLJB6, Harvard Dataverse, V1

Survey data from 2013, 2015, 2017, and 2019 Women of Color in Political Science Workshops.

Replication Data for: Reply to "Powerless Conservatives or Powerless Findings?"

Oct 16, 2020

Zigerell, Lawrence, 2020, "Replication Data for: Reply to "Powerless Conservatives or Powerless Findings?"", https://doi.org/10.7910/DVN/CJQROH, Harvard Dataverse, V1

Reply to "Powerless Conservatives or Powerless Findings?"

Replication Data for: Research Career Paths Among Political Scientists in Research Institutions

Oct 5, 2020 - Harvard Dataverse

Hill, Kim, 2020, "Replication Data for: Research Career Paths Among Political Scientists in Research Institutions", https://doi.org/10.7910/DVN/G7KPHT, Harvard Dataverse, V1, UNF:6:nvHh0Ais+haR6CBEeSbZPA== [fileUNF]

Considerable work assesses research success in political science. Yet the latter work has not taken account of widespread findings that scholars can follow a variety of career research paths that complicate how we envision scholarly success. Further, we have no systematic information on these career paths in any scientific discipline. I present an...

Replication Data for: “Learning through Peer Reviewing and Publishing in the Pi Sigma Alpha Undergraduate Journal of Politics: Twenty Years Later”

Oct 5, 2020

Walker, Christina; Towner, Terri, 2020, "Replication Data for: “Learning through Peer Reviewing and Publishing in the Pi Sigma Alpha Undergraduate Journal of Politics: Twenty Years Later”", https://doi.org/10.7910/DVN/FVKAGM, Harvard Dataverse, V1

The Pi Sigma Alpha Undergraduate Journal of Politics, sponsored by the Pi Sigma Alpha National Honor Society, was founded in 2001 at Purdue University. After 20 years, much has changed in undergraduate research and publishing, but the benefits of producing a peer-reviewed journal remain the same. Undergraduate research has increased in prominence,...

Replication Data for: Primary Model Predicts Trump Re-election

Sep 23, 2020

Norpoth, Helmut, 2020, "Replication Data for: Primary Model Predicts Trump Re-election", https://doi.org/10.7910/DVN/IBFHRE, Harvard Dataverse, V1, UNF:6:M0i5mP3JJBMs0h2CXFHL8Q== [fileUNF]

Electoral votes and primary support

Replication Data for: STATE-LEVEL FORECASTS FOR THE 2020 U.S. PRESIDENTIAL ELECTION: TOUGH VICTORY AHEAD FOR BIDEN

Sep 23, 2020

JEROME, Bruno, 2020, "Replication Data for: STATE-LEVEL FORECASTS FOR THE 2020 U.S. PRESIDENTIAL ELECTION: TOUGH VICTORY AHEAD FOR BIDEN", https://doi.org/10.7910/DVN/QUPBSR, Harvard Dataverse, V1, UNF:6:DsZQdCaOMpmtl7IoJnzKcg== [fileUNF]

Most forecasting models for American presidential elections provide estimates of the national two-party vote. Since popular vote winners generally win a majority of Electoral College votes, these models can normally assume that their forecast offers a clear indication of who will end up in the Oval Office. Evidently, this assumption is not always w...

Replication Data for: It's the Pandemic Stupid: A Simplified Model for Forecasting the 2020 Presidential Election

Sep 23, 2020

Abramowitz, Alan, 2020, "Replication Data for: It's the Pandemic Stupid: A Simplified Model for Forecasting the 2020 Presidential Election", https://doi.org/10.7910/DVN/NQWLFH, Harvard Dataverse, V1, UNF:6:IAgH6CY+tWauGk0xr5dKNA== [fileUNF]

Data for incumbent forecasting model

Replication Data for: Social Presence as Best Practice: The Online Classroom Needs to Feel Real

Sep 18, 2020

Daigle, Delton T; Stuvland, Aaron, 2020, "Replication Data for: Social Presence as Best Practice: The Online Classroom Needs to Feel Real", https://doi.org/10.7910/DVN/OGTHTO, Harvard Dataverse, V1, UNF:6:5mI49dRCYtwJNOIT61wq0A== [fileUNF]

As universities around the world stopped delivering face to face classes, the nonintentional creation of many online digital learning spaces has led to much speculation on “best practices” for virtual course delivery. Our evidence shows that the highest educational value comes from optimizing the “social presence” of your classroom environment. Usi...

Replication Data for: Assessing the Renaissance of Individuals in International Relations

Sep 18, 2020

Holmes, Marcus; Jordan, Richard; Parajon, Eric, 2020, "Replication Data for: Assessing the Renaissance of Individuals in International Relations", https://doi.org/10.7910/DVN/PGBDNS, Harvard Dataverse, V1, UNF:6:LMqsb0pap31zQyXb709g7w== [fileUNF]

The study of microfoundations, especially individuals, is enjoying something of a renaissance in international relations (IR) scholarship. Yet, this rise is harder to find in publication data. Using the Teaching, Research, and International Policy (TRIP) journal article database, we show that only 13.7% of IR articles in twelve leading journals emp...

Replication Data for: A Long-Range State-Level Forecast of the 2020 Presidential Election

Sep 18, 2020

DeSart, Jay, 2020, "Replication Data for: A Long-Range State-Level Forecast of the 2020 Presidential Election", https://doi.org/10.7910/DVN/6GWHQB, Harvard Dataverse, V1, UNF:6:1VK3Ubh0iyz5ppZbWK6orw== [fileUNF]

Most U.S. presidential election forecast models produce forecasts three to five months in advance of the election, and often only generate a prediction of the national popular vote. This paper presents a model that generates predictions of state-level outcomes a year in advance of the election, which can then be extrapolated up to national popular...

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