101 to 110 of 586 Results
Tomkins, Sabina; Keniel Yao; Johann Gaebler; Tobias Konitzer; David Rothschild; Marc Meredith; Sharad Goel, 2022, "Replication Data for: Blocks as geographic discontinuities: The effect of polling place assignment on voting", https://doi.org/10.7910/DVN/45GQNA, Harvard Dataverse, V1, UNF:6:03yVASuFY89noc1a6T3Nkg== [fileUNF]
A potential voter must incur a number of costs in order to successfully cast an in-person ballot, including the costs associated with identifying and traveling to a polling place. In order to investigate how these costs affect voter turnout, we introduce two quasi-experimental designs that can be used to study how the political participation of reg... |
Curiel, John; DeLuca, Kevin, 2022, "Replication Data for: Validating the Applicability of Bayesian Inference with Surname and Geocoding to Congressional Redistricting", https://doi.org/10.7910/DVN/LXGDWZ, Harvard Dataverse, V1, UNF:6:0ZSAO4b97zDtP0T/gF4KpQ== [fileUNF]
Ensuring descriptive representation of racial minorities without packing minorities too heavily into districts is a perpetual difficulty, especially in states lacking voter file race data. One advance since the 2010 redistricting cycle is the advent of Bayesian Improved Surname Geocoding (BISG), which greatly improves upon previous ecological infer... |
Mar 25, 2022
Xu, Yiqing; Yang, Eddie, 2022, "Replication Data for: Hierarchically Regularized Entropy Balancing", https://doi.org/10.7910/DVN/QI2WP9, Harvard Dataverse, V1
We introduce hierarchically regularized entropy balancing as an extension to entropy balancing, a reweighting method that adjusts weights for control group units to achieve covariate balance in observational studies with binary treatments. Our proposed extension expands the feature space by including higher-order terms (such as squared and cubic te... |
Mar 20, 2022
Metzger, Shawna, 2022, "Replication Data for: Proportionally Less Difficult?: Reevaluating Keele’s ‘Proportionally Difficult’", https://doi.org/10.7910/DVN/ZYOJF6, Harvard Dataverse, V1
Replication material for Metzger's "Proportionally Less Difficult?". See "readme.html" in /code folder for further documentation. Unlike the paper, the CO capsule does *not* run 10000 simulation draws for run-time reasons. Please refer to the instructions in README to fully replicate the original results. The capsule does generate the tables/figure... |
Binding, Garret; Stoetzer, Lukas F., 2022, "Replication Data for: Non-separable Preferences in the Statistical Analysis of Roll Call Votes", https://doi.org/10.7910/DVN/XLY5AJ, Harvard Dataverse, V1
Conventional multidimensional statistical models of roll call votes assume that legislators' preferences are additively separable over dimensions. In this article, we introduce an item response model of roll call votes that allows for non-separability over latent dimensions. Conceptually, non-separability matters if outcomes over dimensions are rel... |
Bestvater, Samuel; Monroe, Burt, 2022, "Replication Data for: Sentiment is Not Stance: Target-Aware Opinion Classification for Political Text Analysis", https://doi.org/10.7910/DVN/MUYYG4, Harvard Dataverse, V1, UNF:6:7jt7bPthgQvQFKCKly4NPg== [fileUNF]
Sentiment analysis techniques have a long history in natural language processing and have become a standard tool in the analysis of political texts, promising a conceptually straightforward automated method of extracting meaning from textual data by scoring documents on a scale from positive to negative. However, while these kinds of sentiment scor... |
Feb 25, 2022
Zhirnov, Andrei; Moral, Mert; Sedashov, Evgeny, 2022, "Replication Data for: Taking Distributions Seriously: On the Interpretation of the Estimates of Interactive Nonlinear Models", https://doi.org/10.7910/DVN/ZJCYGP, Harvard Dataverse, V1, UNF:6:nlgh7DbLPydagOh2DNBjzw== [fileUNF]
The replication package contains the data files, Stata do-files, and R script necessary to replicate the illustrations that appear in "Taking Distributions Seriously: On the Interpretation of the Estimates of Interactive Nonlinear Models" by Andrei Zhirnov, Mert Moral, and Evgeny Sedashov. Abstract: In recent decades, political science literature h... |
Egami, Naoki; Yamauchi, Soichiro, 2022, "Replication Data for: Using Multiple Pre-treatment Periods to Improve Difference-in-Differences and Staggered Adoption Designs", https://doi.org/10.7910/DVN/SLIXNF, Harvard Dataverse, V1
While a difference-in-differences (DID) design was originally developed with one pre- and one post-treatment period, data from additional pre-treatment periods are often available. How can researchers improve the DID design with such multiple pre-treatment periods under what conditions? We first use potential outcomes to clarify three benefits of m... |
Jan 6, 2022
Pickup, Mark; Kellstedt, Paul, 2022, "Replication Data for: Balance as a Pre-Estimation Test for Time Series Analysis", https://doi.org/10.7910/DVN/G0XXSE, Harvard Dataverse, V1, UNF:6:EI+inksxZPS9nXglHSepIw== [fileUNF]
It is understood that ensuring equation balance is a necessary condition for a valid model of times series data. Yet, the definition of balance provided so far has been incomplete and there has not been a consistent understanding of exactly why balance is important or how it can be applied. The discussion to date has focused on the estimates produc... |
Ahlskog, Rafael; Oskarsson, Sven, 2021, "Replication Data for: Quantifying bias from measurable and unmeasurable confounders across three domains of individual determinants of political preferences", https://doi.org/10.7910/DVN/MGEN32, Harvard Dataverse, V1, UNF:6:phnYW+g6qZF/LeaiAs2EzA== [fileUNF]
A core part of political research is to identify how political preferences are shaped. The nature of these questions is such that robust causal identification is often difficult to achieve, and we are not seldom stuck with observational methods that we know have limited causal validity. The purpose of this paper is to measure the magnitude of bias... |
