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21 to 30 of 80 Results

Presidential Campaign Ads Metadata: 1952-2012 Mar 26, 2025 - Campaign Television Advertisement Project Dietrich, Bryce Jensen; Breuer, Adam; Crespin, Michael H.; Pryse, J. A.; Butler, Matthew; Imai, Kosuke, 2025, "Presidential Campaign Ads Metadata: 1952-2012", https://doi.org/10.7910/DVN/MCKTUA, Harvard Dataverse, V1, UNF:6:bpDu+MGJRDum/k8kUaTdPQ== [fileUNF] This file contains all of the metadata for the 9,707 presidential ads posted within this dataverse. Please refer to the "Notes" section for the codebook.
Campaign Television Advertisement Project(Purdue University) Mar 26, 2025Harvard Dataverse This Dataverse contain 9,707 campaign ads from United States Presidential Elections (1952-2012).
Replication Data for: Estimating Heterogeneous Causal Effects of High-Dimensional Treatments: Application to Conjoint Analysis Jan 16, 2025 - Max Goplerud Dataverse Goplerud, Max; Imai, Kosuke; Pashley, Nicole E., 2025, "Replication Data for: Estimating Heterogeneous Causal Effects of High-Dimensional Treatments: Application to Conjoint Analysis", https://doi.org/10.7910/DVN/YAHPEH, Harvard Dataverse, V1 Replication data for the results in Goplerud, Imai, and Pashley (2025): "Estimating Heterogeneous Causal Effects of High-Dimensional Treatments: Application to Conjoint Analysis".
Replication Data for: Priming bias versus post-treatment bias in experimental designs Dec 10, 2024 - Political Analysis Dataverse Brown, Jacob; Blackwell, Matthew; Hill, Sophie; Imai, Kosuke; Yamamoto, Teppei, 2024, "Replication Data for: Priming bias versus post-treatment bias in experimental designs", https://doi.org/10.7910/DVN/JZ55TF, Harvard Dataverse, V1, UNF:6:hxomurJ816d2j2kWHylbZQ== [fileUNF] The repository contains replication code and data for the article: "Priming bias versus post-treatment bias in experimental designs"
Replication Data and Code for: A Summer Bridge Program for First-Generation Low-income Students Stretches Academic Ambitions with no Adverse Impacts on First-year GPA Nov 5, 2024 - Harvard Dataverse Johnson, Rebecca; Simko, Tyler; Imai, Kosuke, 2024, "Replication Data and Code for: A Summer Bridge Program for First-Generation Low-income Students Stretches Academic Ambitions with no Adverse Impacts on First-year GPA", https://doi.org/10.7910/DVN/DLBFNS, Harvard Dataverse, V1, UNF:6:u90ojMYsmKVl/5y3lZMKpg== [fileUNF] This repository contains data and replication code for the following article: "A Summer Bridge Program for First-Generation Low-Income Students Stretches Academic Ambitions with No Adverse Impacts on First-year GPA" The data is deidentified in a way that protects student privacy.
Replication data for "Evaluating Bias and Noise Induced by the U.S. Census Bureau's Privacy Protection Methods" Nov 5, 2024 - Algorithm-Assisted Redistricting Methodology (ALARM) Project Kenny, Christopher; McCartan, Cory; Kuriwaki, Shiro; Simko, Tyler; Imai, Kosuke, 2024, "Replication data for "Evaluating Bias and Noise Induced by the U.S. Census Bureau's Privacy Protection Methods"", https://doi.org/10.7910/DVN/TMIN3H, Harvard Dataverse, V3 The United States Census Bureau faces a difficult trade-off between the accuracy of Census statistics and the protection of individual information. We conduct the first independent evaluation of bias and noise induced by the Bureau's two main disclosure avoidance systems: the TopDown algorithm employed for the 2020 Census and the swapping algorithm...
47-Prefecture Redistricting Simulations Sep 3, 2024 - Algorithm-Assisted Redistricting Methodology (ALARM) Project Miyazaki, Sho; Yamada, Kento; Yatsuhashi, Rei; Imai, Kosuke, 2022, "47-Prefecture Redistricting Simulations", https://doi.org/10.7910/DVN/Z9UKSH, Harvard Dataverse, V3, UNF:6:p19IftGnCWCsP62d33rkSQ== [fileUNF] The goal of the 47-Prefecture Simulation Project is to generate and analyze redistricting plans for the single-member districts of the House of Representatives of Japan using a redistricting simulation algorithm. In this project, we analyzed the partisan bias of the 2022 redistricting for 25 prefectures subject to redistricting. Our simulations are...
Replication Data for: Measuring and Modeling Neighborhoods Feb 2, 2024 - American Political Science Review Dataverse McCartan, Cory; Brown, Jacob R; Imai, Kosuke, 2024, "Replication Data for: Measuring and Modeling Neighborhoods", https://doi.org/10.7910/DVN/SDSUQG, Harvard Dataverse, V1, UNF:6:Sjio5x6DrAyaH10J0S0liw== [fileUNF] Granular geographic data present new opportunities to understand how neighbor- hoods are formed, and how they influence politics. At the same time, the inherent subjectivity of neighborhoods creates methodological challenges in measuring and mod- eling them. We develop an open-source survey instrument that allows respondents to draw their neighborh...
Replication Data for: Using Machine Learning to Test Causal Hypotheses in Conjoint Analysis Nov 29, 2023 - Political Analysis Dataverse Ham, Dae Woong; Kosuke Imai; Lucas Janson, 2023, "Replication Data for: Using Machine Learning to Test Causal Hypotheses in Conjoint Analysis", https://doi.org/10.7910/DVN/ENI8GF, Harvard Dataverse, V1 Conjoint analysis is a popular experimental design used to measure multidimensional preferences. Many researchers focus on estimating the average marginal effects of each factor while averaging over the other factors. Although this allows for straightforward design-based estimation, the results critically depend on the ways in which factors interac...
50-State Redistricting Simulations May 2, 2023 - Algorithm-Assisted Redistricting Methodology (ALARM) Project McCartan, Cory; Kenny, Christopher T.; Simko, Tyler; Kuriwaki, Shiro; Garcia, George, III; Wang, Kevin; Wu, Melissa; Imai, Kosuke, 2021, "50-State Redistricting Simulations", https://doi.org/10.7910/DVN/SLCD3E, Harvard Dataverse, V14, UNF:6:T2SKZdDrEKt7cKDAK9/IFQ== [fileUNF] Every decade following the Census, states and municipalities must redraw districts for Congress, state houses, city councils, and more. The goal of the 50-State Simulation Project is to enable researchers, practitioners, and the general public to use cutting-edge redistricting simulation analysis to evaluate enacted congressional districts. Evaluat...

Presidential Campaign Ads Metadata: 1952-2012

Mar 26, 2025 - Campaign Television Advertisement Project

Dietrich, Bryce Jensen; Breuer, Adam; Crespin, Michael H.; Pryse, J. A.; Butler, Matthew; Imai, Kosuke, 2025, "Presidential Campaign Ads Metadata: 1952-2012", https://doi.org/10.7910/DVN/MCKTUA, Harvard Dataverse, V1, UNF:6:bpDu+MGJRDum/k8kUaTdPQ== [fileUNF]

This file contains all of the metadata for the 9,707 presidential ads posted within this dataverse. Please refer to the "Notes" section for the codebook.

Campaign Television Advertisement Project(Purdue University)

Mar 26, 2025Harvard Dataverse

This Dataverse contain 9,707 campaign ads from United States Presidential Elections (1952-2012).

Replication Data for: Estimating Heterogeneous Causal Effects of High-Dimensional Treatments: Application to Conjoint Analysis

Jan 16, 2025 - Max Goplerud Dataverse

Goplerud, Max; Imai, Kosuke; Pashley, Nicole E., 2025, "Replication Data for: Estimating Heterogeneous Causal Effects of High-Dimensional Treatments: Application to Conjoint Analysis", https://doi.org/10.7910/DVN/YAHPEH, Harvard Dataverse, V1

Replication data for the results in Goplerud, Imai, and Pashley (2025): "Estimating Heterogeneous Causal Effects of High-Dimensional Treatments: Application to Conjoint Analysis".

Replication Data for: Priming bias versus post-treatment bias in experimental designs

Dec 10, 2024 - Political Analysis Dataverse

Brown, Jacob; Blackwell, Matthew; Hill, Sophie; Imai, Kosuke; Yamamoto, Teppei, 2024, "Replication Data for: Priming bias versus post-treatment bias in experimental designs", https://doi.org/10.7910/DVN/JZ55TF, Harvard Dataverse, V1, UNF:6:hxomurJ816d2j2kWHylbZQ== [fileUNF]

The repository contains replication code and data for the article: "Priming bias versus post-treatment bias in experimental designs"

Replication Data and Code for: A Summer Bridge Program for First-Generation Low-income Students Stretches Academic Ambitions with no Adverse Impacts on First-year GPA

Nov 5, 2024 - Harvard Dataverse

Johnson, Rebecca; Simko, Tyler; Imai, Kosuke, 2024, "Replication Data and Code for: A Summer Bridge Program for First-Generation Low-income Students Stretches Academic Ambitions with no Adverse Impacts on First-year GPA", https://doi.org/10.7910/DVN/DLBFNS, Harvard Dataverse, V1, UNF:6:u90ojMYsmKVl/5y3lZMKpg== [fileUNF]

This repository contains data and replication code for the following article: "A Summer Bridge Program for First-Generation Low-Income Students Stretches Academic Ambitions with No Adverse Impacts on First-year GPA" The data is deidentified in a way that protects student privacy.

Replication data for "Evaluating Bias and Noise Induced by the U.S. Census Bureau's Privacy Protection Methods"

Nov 5, 2024 - Algorithm-Assisted Redistricting Methodology (ALARM) Project

Kenny, Christopher; McCartan, Cory; Kuriwaki, Shiro; Simko, Tyler; Imai, Kosuke, 2024, "Replication data for "Evaluating Bias and Noise Induced by the U.S. Census Bureau's Privacy Protection Methods"", https://doi.org/10.7910/DVN/TMIN3H, Harvard Dataverse, V3

The United States Census Bureau faces a difficult trade-off between the accuracy of Census statistics and the protection of individual information. We conduct the first independent evaluation of bias and noise induced by the Bureau's two main disclosure avoidance systems: the TopDown algorithm employed for the 2020 Census and the swapping algorithm...

47-Prefecture Redistricting Simulations

Sep 3, 2024 - Algorithm-Assisted Redistricting Methodology (ALARM) Project

Miyazaki, Sho; Yamada, Kento; Yatsuhashi, Rei; Imai, Kosuke, 2022, "47-Prefecture Redistricting Simulations", https://doi.org/10.7910/DVN/Z9UKSH, Harvard Dataverse, V3, UNF:6:p19IftGnCWCsP62d33rkSQ== [fileUNF]

The goal of the 47-Prefecture Simulation Project is to generate and analyze redistricting plans for the single-member districts of the House of Representatives of Japan using a redistricting simulation algorithm. In this project, we analyzed the partisan bias of the 2022 redistricting for 25 prefectures subject to redistricting. Our simulations are...

Replication Data for: Measuring and Modeling Neighborhoods

Feb 2, 2024 - American Political Science Review Dataverse

McCartan, Cory; Brown, Jacob R; Imai, Kosuke, 2024, "Replication Data for: Measuring and Modeling Neighborhoods", https://doi.org/10.7910/DVN/SDSUQG, Harvard Dataverse, V1, UNF:6:Sjio5x6DrAyaH10J0S0liw== [fileUNF]

Granular geographic data present new opportunities to understand how neighbor- hoods are formed, and how they influence politics. At the same time, the inherent subjectivity of neighborhoods creates methodological challenges in measuring and mod- eling them. We develop an open-source survey instrument that allows respondents to draw their neighborh...

Replication Data for: Using Machine Learning to Test Causal Hypotheses in Conjoint Analysis

Nov 29, 2023 - Political Analysis Dataverse

Ham, Dae Woong; Kosuke Imai; Lucas Janson, 2023, "Replication Data for: Using Machine Learning to Test Causal Hypotheses in Conjoint Analysis", https://doi.org/10.7910/DVN/ENI8GF, Harvard Dataverse, V1

Conjoint analysis is a popular experimental design used to measure multidimensional preferences. Many researchers focus on estimating the average marginal effects of each factor while averaging over the other factors. Although this allows for straightforward design-based estimation, the results critically depend on the ways in which factors interac...

50-State Redistricting Simulations

May 2, 2023 - Algorithm-Assisted Redistricting Methodology (ALARM) Project

McCartan, Cory; Kenny, Christopher T.; Simko, Tyler; Kuriwaki, Shiro; Garcia, George, III; Wang, Kevin; Wu, Melissa; Imai, Kosuke, 2021, "50-State Redistricting Simulations", https://doi.org/10.7910/DVN/SLCD3E, Harvard Dataverse, V14, UNF:6:T2SKZdDrEKt7cKDAK9/IFQ== [fileUNF]

Every decade following the Census, states and municipalities must redraw districts for Congress, state houses, city councils, and more. The goal of the 50-State Simulation Project is to enable researchers, practitioners, and the general public to use cutting-edge redistricting simulation analysis to evaluate enacted congressional districts. Evaluat...

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