71 to 80 of 586 Results
Apr 22, 2023
Bølstad, Jørgen, 2023, "Replication Data for: Hierarchical Bayesian Aldrich-McKelvey Scaling", https://doi.org/10.7910/DVN/VX0YPW, Harvard Dataverse, V1
Estimating the ideological positions of political actors is an important step towards answering a number of substantive questions in political science. Survey scales provide useful data for such estimation, but also present a challenge, as respondents tend to interpret the scales differently. The Aldrich-McKelvey model addresses this challenge, but... |
Mar 13, 2023
Hanretty, Chris; Cohen, Denis, 2023, "Replication Data for: Simulating party shares", https://doi.org/10.7910/DVN/3WILXI, Harvard Dataverse, V1
We tackle the problem of simulating seat- and vote-shares for a party system of a given size. We show how these shares can be generated using unordered and ordered Dirichlet distributions. We show that a distribution with a mean vector given by the rule described in Taagepera and Allik (2006) fits real world data almost as well as a saturated model... |
Mar 13, 2023
Hartman, Erin; Huang, Melody, 2023, "Replication Data for: "Sensitivity Analysis for Survey Weights"", https://doi.org/10.7910/DVN/YJSJEX, Harvard Dataverse, V1
Survey weighting allows researchers to account for bias in survey samples, due to unit nonresponse or convenience sampling, using measured demographic covariates. Unfortunately, in practice, it is impossible to know whether the estimated survey weights are sufficient to alleviate concerns about bias due to unobserved confounders or incorrect functi... |
Häffner, Sonja; Hofer, Martin; Nagl, Maximilian; Walterskirchen, Julian, 2023, "Replication Data for: Introducing an Interpretable Deep Learning Approach to Domain-Specific Dictionary Creation: A Use Case for Conflict Prediction", https://doi.org/10.7910/DVN/Y5INRM, Harvard Dataverse, V1
Recent advancements in natural language processing (NLP) methods have significantly improved their performance. However, more complex NLP models are more difficult to interpret and computationally expensive. Therefore, we propose an approach to dictionary creation that carefully balances the trade-off between complexity and interpretability. This a... |
Alpino, Matteo; Crispino, Marta, 2023, "Replication Data for: "The role of majority status in close election studies"", https://doi.org/10.7910/DVN/GAK3QS, Harvard Dataverse, V1, UNF:6:SORyg9FB6zzSav1MbRXJoQ== [fileUNF]
Many studies exploit close elections in a regression discontinuity framework to identify partisan effects, i.e. the effect of having a given party in office on some outcome. We argue that, when conducted on single-member districts, such design may identify a compound effect: the partisan effect, plus the majority status effect, i.e, the effect of b... |
Ben-Michael, Eli; Feller, Avi; Hartman, Erin, 2023, "Replication Data for: Multilevel calibration weighting for survey data", https://doi.org/10.7910/DVN/J7BSXQ, Harvard Dataverse, V1, UNF:6:ac93SCtKAq6++7pVi4noAQ== [fileUNF]
In the November 2016 U.S. presidential election, many state level public opinion polls, particularly in the Upper Midwest, incorrectly predicted the winning candidate. One leading explanation for this polling miss is that the precipitous decline in traditional polling response rates led to greater reliance on statistical methods to adjust for the c... |
Feb 13, 2023
Chaturvedi, Rochana; Chaturvedi, Sugat, 2023, "Replication Data for: It’s All in the Name: A Character Based Approach to Infer Religion", https://doi.org/10.7910/DVN/JOEVPN, Harvard Dataverse, V1
Large-scale microdata on group identity are critical for studies on identity politics and violence but remain largely unavailable for developing countries. We use personal names to infer religion in South Asia - where religion is a salient social division, and yet, disaggregated data on it are scarce. Existing work predicts religion using a diction... |
Lo, Adeline; Judge-Lord, Devin; Hudson, Kyler; Mayer, Kenneth, 2023, "Mapping Literature with Networks: An Application to Redistricting", https://doi.org/10.7910/DVN/NV66YN, Harvard Dataverse, V1, UNF:6:xH3pGSqU+2t2AkpeWA1p2w== [fileUNF]
Understanding the gaps and connections across existing theories and findings is a perennial challenge in scientific research. Systematically reviewing scholarship is especially challenging for researchers who may lack domain expertise, including junior scholars or those exploring new substantive territory. Conversely, senior scholars may rely on lo... |
Wang, Yu, 2023, "Replication Data for: Topic Classification for Political Texts with Pretrained Language Models", https://doi.org/10.7910/DVN/FMT8KR, Harvard Dataverse, V1
Supervised topic classification requires labeled data. This often becomes a bottleneck as high-quality labeled data is expensive to acquire. To overcome the data scarcity problem, scholars have recently proposed to use cross-domain topic classification to take advantage of pre-existing labeled datasets. Cross-domain topic classification only requir... |
Jan 6, 2023
Zhukov, Yuri; Byers, Jason; Davidson, Marty; Kollman, Ken, 2023, "Replication Data for: Integrating Data Across Misaligned Spatial Units", https://doi.org/10.7910/DVN/TOSX7N, Harvard Dataverse, V1
Zhukov, Byers, Davidson, and Kollman, "Integrating Data Across Misaligned Spatial Units," Political Analysis (conditionally accepted 10/2022) |
