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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.
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1 to 10 of 320 Results
Jan 4, 2018
Kim, In Song; Londregan, John; Ratkovic, Marc, 2017, "Replication Data for: "Estimating Spatial Preferences from Votes and Text"", doi:10.7910/DVN/AGUVBE, Harvard Dataverse, V2, UNF:6:z43UphYjiN7rtOj4uBV/7g==
This folder contains the scripts and data necessary to implement Sparse Factor Analysis (SFA) as outline in Kim, Londregan, and Ratkovic (2018). The README file contains all relevant information.
Dec 1, 2017
Horiuchi, Yusaku; Smith, Daniel M.; Yamamoto, Teppei, 2017, "Replication Data for: Measuring Voters' Multidimensional Policy Preferences with Conjoint Analysis: Application to Japan's 2014 Election", doi:10.7910/DVN/KUMMUJ, Harvard Dataverse, V1
Replication archive for the results reported in the article.
Nov 16, 2017
Miratrix, Luke W.; Sekhon, Jasjeet S.; Theodoridis, Alexander G.; Campos, Luis F., 2017, "Replication Data for: Worth Weighting? How to Think About and Use Weights in Survey Experiments", doi:10.7910/DVN/52UGJT, Harvard Dataverse, V1
Replication materials for the forthcoming publication entitled "Worth Weighting? How to Think About and Use Weights in Survey Experiments."
Nov 14, 2017
Schneider, Carsten Q., 2017, "Replication Data for: Realists and Idealists in QCA", doi:10.7910/DVN/WM3X3D, Harvard Dataverse, V1
The file Schneider_replication_file_reply.R contains all the code for first obtaining the data and then performing the analyses performed in the article. The file Schneider_PA_supplement.pdf contains information about the results of 84 analyses of sufficiency.
Nov 8, 2017
Denny, Matthew; Spirling, Arthur, 2017, "Replication Data for: Text Preprocessing For Unsupervised Learning: Why It Matters, When It Misleads, And What To Do About It", doi:10.7910/DVN/XRR0HM, Harvard Dataverse, V1
Data and code to replicate findings in "Text Preprocessing For Unsupervised Learning: Why It Matters, When It Misleads, And What To Do About It," forthcoming in Political Analysis.
Oct 24, 2017
Tahk, Alexander, 2017, "Replication Data for: Nonparametric Ideal-Point Estimation and Inference", doi:10.7910/DVN/WIRN6R, Harvard Dataverse, V1, UNF:6:v+zAWAFABTnFjZGegcS2iA==
Replication code and data to accompany "Nonparametric Ideal-Point Estimation and Inference."
Oct 13, 2017
Quinn, Kevin; Abrajano, Marisa; Elmendorf, Christopher, 2017, "Replication Data for: Labels vs. Pictures: Treatment-Mode Effects in Experiments About Discrimination", doi:10.7910/DVN/DFEH8S, Harvard Dataverse, V1, UNF:6:abrrhxR2xkGTYmtBg5vQGw==
This is replication data and code for "Labels vs. Pictures: Treatment-Mode Effects in Experiments About Discrimination" published in Political Analysis.
Oct 3, 2017
Magleby, Daniel; Daniel Mosesson, 2017, "Replication Data for: A New Approach for Developing Neutral Redistricting Plans", doi:10.7910/DVN/R9GKJA, Harvard Dataverse, V1
Computers hold the potential to draw legislative districts in a neutral way. Existing approaches to automated redistricting may introduce bias and encounter difficulties when drawing districts of large and even medium-sized jurisdictions. We present a new algorithm that can neutr...
Oct 1, 2017
Arel-Bundock, Vincent; Pelc, Krzysztof, 2017, "When Can Multiple Imputation Improve Regression Estimates?", doi:10.7910/DVN/S9G9XS, Harvard Dataverse, V1
These materials serve to replicate the simulations and empirical analyses in "When Can Multiple Imputation Improve Regression Estimates?"
Sep 7, 2017
Blackwell, Matthew, 2017, "Replication Data for: Game Changers: Detecting Shifts in Overdispersed Count Data", doi:10.7910/DVN/SKGWTS, Harvard Dataverse, V1, UNF:6:jIVKdWOAWyhNOhoO2KYdNQ==
Data and code to replicate findings in "Game Changers: Detecting Shifts in Overdispersed Count Data," forthcoming in Political Analysis.
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