Political Analysis is the official journal of the Society for Political Methodology. We publish articles that provide original and significant advances in the general area of political methodology, including both quantitative and qualitative methodological approaches.
Featured Dataverses

In order to use this feature you must have at least one published or linked dataverse.

Publish Dataverse

Are you sure you want to publish your dataverse? Once you do so it must remain published.

Publish Dataverse

This dataverse cannot be published because the dataverse it is in has not been published.

Delete Dataverse

Are you sure you want to delete your dataverse? You cannot undelete this dataverse.

Advanced Search

11 to 20 of 586 Results
Replication Package for “Generalizing Trimming Bounds for Endogenously Missing Outcome Data Using Random Forests” (Samii, Wang, and Zhou, 2024)
Mar 20, 2025
Wang, Ye, 2025, "Replication Package for “Generalizing Trimming Bounds for Endogenously Missing Outcome Data Using Random Forests” (Samii, Wang, and Zhou, 2024)", https://doi.org/10.7910/DVN/JDSFJO, Harvard Dataverse, V1
This folder contains the code and materials necessary to replicate the results from the paper “Generalizing Trimming Bounds for Endogenously Missing Outcome Data Using Random Forests” (Samii, Wang, and Zhou, 2024).
Replication Data for: "Estimating the Local Average Treatment Effect Without the Exclusion Restriction"
Mar 11, 2025
Markovich, Zachary, 2025, "Replication Data for: "Estimating the Local Average Treatment Effect Without the Exclusion Restriction"", https://doi.org/10.7910/DVN/Y6JJ0A, Harvard Dataverse, V1
Existing approaches to conducting inference about the Local Average Treatment Effect or LATE require assumptions that are considered tenuous in many applied settings. In particular, Instrumental Variable techniques require monotonicity and the exclusion restriction while principal score methods rest on some form of the principal ignorability assump...
Replication Data for: Constructing Vec-tionaries to Extract Message Features from Texts: A Case Study of Moral Content
Mar 11, 2025
Duan, Zening; Shao, Anqi; Hu, Yicheng; Lee, Heysung; Liao, Xining; Suh, Yoo Ji; Kim, Jisoo; Yang, Kai-Cheng; Chen, Kaiping; Yang, Sijia, 2025, "Replication Data for: Constructing Vec-tionaries to Extract Message Features from Texts: A Case Study of Moral Content", https://doi.org/10.7910/DVN/YITNSV, Harvard Dataverse, V1
While researchers often study message featureslike moral content in text, such as party manifestos and social media, their quantification remains a challenge. Conventional human coding struggles with scalability and intercoder reliability. While dictionary-based methods are cost-effective and computationally efficient, they often lack contextual se...
Replication Data for: Attention and Political Choice: A Foundation for Eye Tracking in Political Science
Replication Data for: Attention and Political Choice: A Foundation for Eye Tracking in Political Science
Feb 26, 2025
Jenke, Libby; Sullivan, Nicolette, 2025, "Replication Data for: Attention and Political Choice: A Foundation for Eye Tracking in Political Science", https://doi.org/10.7910/DVN/BQWZJF, Harvard Dataverse, V1, UNF:6:tvObEx7kpVKSj49w79nHZQ== [fileUNF]
Replication data.
Replication Data for "What to Observe When Assuming Selection on Observables"
Feb 21, 2025
Quinn, Kevin M.; Liu, Guoer; Epstein, Lee; Martin, Andrew D., 2025, "Replication Data for "What to Observe When Assuming Selection on Observables"", https://doi.org/10.7910/DVN/69RUAD, Harvard Dataverse, V1, UNF:6:PntW10YwlNZswZX6ZR77RA== [fileUNF]
Replication code and data needed to reproduce results presented in-text and in the Supplemental Information provided for the study. Please report any errors to Guoer Liu; current contact info: guoerliu@ucsd.edu
Replication Data for: Mapping (A)Ideology: A Taxonomy of European Parties Using Generative LLMs as Zero-Shot Learners
Feb 19, 2025
Di Leo, Riccardo; Zeng, Chen; Dinas, Elias; Tamtam, Reda, 2025, "Replication Data for: Mapping (A)Ideology: A Taxonomy of European Parties Using Generative LLMs as Zero-Shot Learners", https://doi.org/10.7910/DVN/SECNCZ, Harvard Dataverse, V1
We perform the first mapping of the ideological positions of European parties using generative Artificial Intelligence (AI) as a “zero-shot” learner. We ask OpenAI’s Generative Pre-trained Transformer (GPT-3.5) to identify the more “right-wing” option across all possible duplets of European parties at a given point in time, solely based on their na...
Replication Data for: "Seeing Like a District: Understanding What Close-Election Designs for Leader Characteristics Can and Cannot Tell Us"
Replication Data for: "Seeing Like a District: Understanding What Close-Election Designs for Leader Characteristics Can and Cannot Tell Us"
Feb 3, 2025
Bertoli, Andrew; Hazlett, Chad, 2025, "Replication Data for: "Seeing Like a District: Understanding What Close-Election Designs for Leader Characteristics Can and Cannot Tell Us"", https://doi.org/10.7910/DVN/SCTVOF, Harvard Dataverse, V1
This replication archive contains the replication materials for “Seeing like a district: Understanding what close-election designs for leader characteristics can and cannot tell us.”
Replication Data for: Positioning Political Texts with Large Language Models by Asking and Averaging
Jan 9, 2025
Le Mens, Gaël; Gallego, Aina, 2024, "Replication Data for: Positioning Political Texts with Large Language Models by Asking and Averaging", https://doi.org/10.7910/DVN/YFM0BW, Harvard Dataverse, V2
We use instruction-tuned Large Language Models (LLMs) like GPT-4, Llama 3, MiXtral, or Aya to position political texts within policy and ideological spaces. We ask an LLM where a tweet or a sentence of a political text stands on the focal dimension and take the average of the LLM responses to position political actors such as US Senators, or longer...
Replication Data for: News Sharing on Social Media: Mapping the Ideology of News Media, Politicians, and the Mass Public
Jan 9, 2025
Eady, Gregory, 2024, "Replication Data for: News Sharing on Social Media: Mapping the Ideology of News Media, Politicians, and the Mass Public", https://doi.org/10.7910/DVN/1QMLOV, Harvard Dataverse, V2
This article examines the information sharing behavior of US politicians and the mass public by mapping the ideological sharing space of political news on social media. As data, we use the near-universal currency of online information exchange: web links. We introduce a methodological approach and software to unify the measurement of ideology acros...
Replication Data for: Priming bias versus post-treatment bias in experimental designs
Replication Data for: Priming bias versus post-treatment bias in experimental designs
Dec 10, 2024
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"
Add Data

Sign up or log in to create a dataverse or add a dataset.

Sign Up Log In
Share Dataverse

Share this dataverse on your favorite social media networks.

Link Dataverse
Reset Modifications

Are you sure you want to reset the selected metadata fields? If you do this, any customizations (hidden, required, optional) you have done will no longer appear.