51 to 60 of 586 Results
Dec 20, 2023
Fukumoto, Kentaro, 2023, "Replication Data for: Normal Mode Copulas for Nonmonotonic Dependence", https://doi.org/10.7910/DVN/X94ITA, Harvard Dataverse, V1
Copulas are helpful in studying joint distributions of two variables, in particular, when confounders are unobserved. However, most conventional copulas cannot model joint distributions where one variable does not increase or decrease in the other in a monotonic manner. For instance, suppose that two variables are linearly positively correlated for... |
Dec 14, 2023
Mauerer, Ingrid; Tutz, Gerhard, 2023, "Replication Data for: Vote Choices and Valence: Intercepts and Alternate Specifications", https://doi.org/10.7910/DVN/SKWTGS, Harvard Dataverse, V1
Valence is a crucial concept in studying spatial voting and party competition. The widely adopted approach is to rely on intercepts of vote choice models and to infer, based on their size and direction, how valence affects party strategies in empirical settings. The approach suffers from fundamental statistical flaws. This contribution provides the... |
Slough, Tara, 2023, "Replication Data for: Making a Difference: The Consequences of Electoral Experiments", https://doi.org/10.7910/DVN/1UWD3M, Harvard Dataverse, V1, UNF:6:xV+QAnIELKJwLNk109odtA== [fileUNF]
While experiments on elections represent a popular tool in social science, the possibility that experimental interventions could affect who wins office remains a central ethical concern. I formally characterize electoral experimental designs to derive an upper bound on aggregate electoral impact under different assumptions about interference. I the... |
Lai, Angela; Brown, Megan A.; Bisbee, James; Tucker, Joshua A.; Nagler, Jonathan; Bonneau, Richard, 2023, "Replication Data for: Estimating the Ideology of YouTube Videos", https://doi.org/10.7910/DVN/WZZFTW, Harvard Dataverse, V1
Abstract: We present a method for estimating the ideology of political YouTube videos. The subfield of estimating ideology as a latent variable has often focused on traditional actors such as legislators while more recent work has used social media data to estimate the ideology of ordinary users, political elites, and media sources. We build on thi... |
Nov 29, 2023
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... |
Nov 1, 2023
Canen, Nathan; Sugiura, Ko, 2023, "Replication Data for: Inference in Linear Dyadic Data Models with Network Spillovers", https://doi.org/10.7910/DVN/VLMMZQ, Harvard Dataverse, V1
Abstract: When using dyadic data (i.e., data indexed by pairs of units), researchers typically assume a linear model, estimate it using Ordinary Least Squares and conduct inference using ``dyadic-robust" variance estimators. The latter assumes that dyads are uncorrelated if they do not share a common unit (e.g., if the same individual is not presen... |
Oct 25, 2023
Palmer, Maxwell; Schneer, Benjamin; DeLuca, Kevin, 2023, "Replication Data for: A Partisan Solution to Partisan Gerrymandering: The Define-Combine Procedure", https://doi.org/10.7910/DVN/XBYFE1, Harvard Dataverse, V1
Redistricting reformers have proposed many solutions to the problem of partisan gerrymandering, but they all require either bipartisan consensus or the agreement of both parties on the legitimacy of a neutral third party to resolve disputes. In this paper we propose a new method for drawing district maps, the Define-Combine Procedure, that substant... |
Wang, Yu, 2023, "Replication Data for: On Finetuning Large Language Models", https://doi.org/10.7910/DVN/7PCLRI, Harvard Dataverse, V1, UNF:6:WCE6XqvobjJiZXpWiLGqOA== [fileUNF]
A recent paper by Häffner et al. 2023 introduces an interpretable deep learning approach for domain specific dictionary creation, where it is claimed that the dictionary-based approach outperforms finetuned language models in predictive accuracy while retaining interpretability. We show that the dictionary-based approach's reported superiority over... |
Cerina, Roberto; Barrie, Christopher; Ketchley, Neil; Zelin, Aaron, 2023, "Replication Data for: Explaining Recruitment to Extremism: A Bayesian Hierarchical Case-Control Approach", https://doi.org/10.7910/DVN/HYOQCD, Harvard Dataverse, V1, UNF:6:WUaf8qcx6+C5VpyLtz693A== [fileUNF]
Who joins extremist movements? Answering this question is beset by method- ological challenges as survey techniques are infeasible and selective samples pro- vide no counterfactual. Recruits can be assigned to contextual units, but this is vulnerable to problems of ecological inference. In this article, we elaborate a tech- nique that combines surv... |
Sep 22, 2023
Metzger, Shawna, 2023, "Replication Data for: Implementation Matters: Evaluating the Proportional Hazard Test’s Performance", https://doi.org/10.7910/DVN/D56UWV, Harvard Dataverse, V1
Replication material for Metzger's "Implementation Matters" (forthcoming, Political Analysis). See "readme.html" in /code folder for further documentation. Abstract: Political scientists commonly use Grambsch and Therneau’s (1994, Biometrika) ubiquitous Schoenfeld-based test to diagnose proportional hazard violations in Cox duration models. However... |
