91 to 100 of 586 Results
Oct 4, 2022
Rosenman, Evan T. R.; McCartan, Cory; Olivella, Santiago, 2022, "Replication Data for: Recalibration Of Predicted Probabilities Using the "Logit Shift": Why does it work, and when can it be expected to work well?", https://doi.org/10.7910/DVN/7MRDUW, Harvard Dataverse, V1
The output of predictive models is routinely recalibrated by reconciling low-level predictions with known quantities defined at higher levels of aggregation. For example, models predicting vote probabilities at the individual level in U.S. elections can be adjusted so that their aggregation matches the observed vote totals in each county, thus prod... |
Sep 29, 2022
Di Cocco, Jessica; Monechi, Bernardo, 2021, "Replication Material for "How Populist Are Parties? Measuring Degrees of Populism in Party Manifestos Using Supervised Machine Learning"", https://doi.org/10.7910/DVN/BMJYAN, Harvard Dataverse, V2
One of the main challenges in comparative studies on populism concerns its temporal and spatial measurements within and between a large number of parties and countries. Textual analysis has proved useful for these purposes, and automated methods can further improve research in this direction. Here we propose a method to derive a score of parties’ l... |
Kates, Sean; Paulsen, Tine; Yntiso, Sidak; Tucker, Joshua A., 2022, "Replication Data for: Bridging the Grade Gap: Reducing Assessment Bias in a Multi-Grader Class", https://doi.org/10.7910/DVN/BIORH8, Harvard Dataverse, V1
Many large survey courses rely on multiple professors or teaching assistants to judge student responses to open-ended questions. Even following best practices, students with similar levels of conceptual understanding can receive widely varying assessments from different graders. We detail how this can occur and argue that it is an example of differ... |
Sep 1, 2022
Tarr, Alex; Imai, Kosuke; Hwang, June, 2022, "Replication Data for: Automated Coding of Political Campaign Advertisement Videos: An Empirical Validation Study", https://doi.org/10.7910/DVN/6SWKPR, Harvard Dataverse, V1
Video advertisements, either through television or the Internet, play an essential role in modern political campaigns. For over two decades, researchers have studied television video ads by analyzing the hand-coded data from the Wisconsin Advertising Project and its successor, the Wesleyan Media Project (WMP). Unfortunately, manually coding more th... |
Aug 23, 2022
Beiser-McGrath, Janina; Beiser-McGrath, Liam F., 2022, "The Consequences of Model Misspecification for the Estimation of Non-Linear Interaction Effects", https://doi.org/10.7910/DVN/S44D0E, Harvard Dataverse, V1
Recent research has shown that interaction effects may often be non-linear (Hainmueller, Mummolo and Xu, 2019). As standard interaction effect specifications assume a linear interaction effect, i.e. the moderator conditions the effect at a constant rate, this can lead to bias. However, allowing non-linear interaction effects, without accounting for... |
Aug 23, 2022
Hare, Christopher; Kutsuris, Mikayla, 2022, "Replication Data for: Measuring Swing Voters with a Supervised Machine Learning Ensemble", https://doi.org/10.7910/DVN/CLQY6O, Harvard Dataverse, V1
Theory has long suggested that swing voting is a response to cross-pressures arising from a mix of individual attributes and contextual factors. Unfortunately, existing regression-based approaches are ill-suited to explore the complex combinations of demographic, policy, and political factors that produce swing voters in American elections. This ga... |
Jun 29, 2022
Park, Jong Hee; Yamauchi, Soichiro, 2022, "Replication Data for: Change-point Detection and Regularization in Time Series Cross Sectional Data Analysis", https://doi.org/10.7910/DVN/MCQTYC, Harvard Dataverse, V1
Researchers of time series cross sectional (TSCS) data regularly face the change-point problem, which re- quires them to discern between significant parametric shifts that can be deemed structural changes and minor parametric shifts that must be considered noise. In this paper, we develop a general Bayesian method for change-point detection in high... |
Widmann, Tobias; Wich, Maximilian, 2022, "Replication Data for: Creating and Comparing Dictionary, Word Embedding, and Transformer-based Models to Measure Discrete Emotions in German Political Text", https://doi.org/10.7910/DVN/C9SAIX, Harvard Dataverse, V1, UNF:6:k84+XwFnZIk9ZuzANS/miA== [fileUNF]
Previous research on emotional language relied heavily on off-the-shelf sentiment dictionaries that focus on negative and positive tone. These dictionaries are often tailored to non-political domains and use bag-of-words approaches which come with a series of disadvantages. This paper creates, validates, and compares the performance of (1) a novel... |
May 5, 2022
Kubinec, Robert, 2022, "Replication Data for Ordered Beta Regression: A Parsimonious, Well-Fitting Model for Continuous Data with Lower and Upper Bounds", https://doi.org/10.7910/DVN/5XYO7O, Harvard Dataverse, V1, UNF:6:LGP00JRsQf1X+9SAoOLJxQ== [fileUNF]
I propose a new model, ordered Beta regression, for continuous distributions with both lower and upper bounds, such as data arising from survey slider scales, visual analog scales, and dose-response relationships. This model employs the cutpoint technique popularized by ordered logit to fit a single linear model to both continuous (0,1) and degener... |
May 3, 2022
Bansak, Kirk; Hainmueller, Jens; Hopkins, Daniel J.; Yamamoto, Teppei, 2022, "Replication Materials for: Using Conjoint Experiments to Analyze Election Outcomes: The Essential Role of the Average Marginal Component Effect (AMCE)", https://doi.org/10.7910/DVN/NBRBHO, Harvard Dataverse, V1
Political scientists have increasingly deployed conjoint survey experiments to understand multidimensional choices in various settings. In this paper, we show that the Average Marginal Component Effect (AMCE) constitutes an aggregation of individual-level preferences that is meaningful both theoretically and empirically. First, extending previous r... |
