Political Analysis最新文献_第7页

Using Conjoint Experiments to Analyze Election Outcomes: The Essential Role of the Average Marginal Component Effect 联合实验分析选举结果:平均边际成分效应的重要作用

IF 5.4 2区社会学

Political Analysis Pub Date : 2022-06-30 DOI: 10.1017/pan.2022.16

Kirk Bansak, Jens Hainmueller, D. Hopkins, Teppei Yamamoto

{"title":"Using Conjoint Experiments to Analyze Election Outcomes: The Essential Role of the Average Marginal Component Effect","authors":"Kirk Bansak, Jens Hainmueller, D. Hopkins, Teppei Yamamoto","doi":"10.1017/pan.2022.16","DOIUrl":"https://doi.org/10.1017/pan.2022.16","url":null,"abstract":"Abstract 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 results to allow for arbitrary randomization distributions, we show how the AMCE represents a summary of voters’ multidimensional preferences that combines directionality and intensity according to a probabilistic generalization of the Borda rule. We demonstrate why incorporating both the directionality and intensity of multi-attribute preferences is essential for analyzing real-world elections, in which ceteris paribus comparisons almost never occur. Second, and in further empirical support of this point, we show how this aggregation translates directly into a primary quantity of interest to election scholars: the effect of a change in an attribute on a candidate’s or party’s expected vote share. These properties hold irrespective of the heterogeneity, strength, or interactivity of voters’ preferences and regardless of how votes are aggregated into seats. Finally, we propose, formalize, and evaluate the feasibility of using conjoint data to estimate alternative quantities of interest to electoral studies, including the effect of an attribute on the probability of winning.","PeriodicalId":48270,"journal":{"name":"Political Analysis","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44645179","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 19

Proportionally Less Difficult?: Reevaluating Keele’s “Proportionally Difficult” 比例难度更低?:重新评价基尔的“比例难度”

IF 5.4 2区社会学

Political Analysis Pub Date : 2022-06-20 DOI: 10.1017/pan.2022.13

Shawna K. Metzger

引用次数: 2

Sensitivity Analysis for Survey Weights 测量权重的灵敏度分析

IF 5.4 2区社会学

Political Analysis Pub Date : 2022-06-14 DOI: 10.1017/pan.2023.12

E. Hartman, Melody Y. Huang

引用次数: 4

PAN volume 30 issue 3 Cover and Back matter PAN第30卷第3期封面和封底

IF 5.4 2区社会学

Political Analysis Pub Date : 2022-06-01 DOI: 10.1017/pan.2022.18

引用次数: 0

PAN volume 30 issue 3 Cover and Front matter PAN第30卷第3期封面和封面

IF 5.4 2区社会学

Political Analysis Pub Date : 2022-06-01 DOI: 10.1017/pan.2022.17

引用次数: 0

Non-Separable Preferences in the Statistical Analysis of Roll Call Votes 点名投票统计分析中的不可分离偏好

IF 5.4 2区社会学

Political Analysis Pub Date : 2022-05-24 DOI: 10.1017/pan.2022.11

Garret Binding, Lukas F. Stoetzer

引用次数: 3

Validating the Applicability of Bayesian Inference with Surname and Geocoding to Congressional Redistricting 基于姓氏和地理编码的贝叶斯推理在国会选区划分中的适用性验证

IF 5.4 2区社会学

Political Analysis Pub Date : 2022-05-20 DOI: 10.1017/pan.2022.14

K. DeLuca, John A. Curiel

引用次数: 5

A Nonparametric Bayesian Model for Detecting Differential Item Functioning: An Application to Political Representation in the US 一个非参数贝叶斯模型检测差异项目功能:在美国政治代表中的应用

IF 5.4 2区社会学

Political Analysis Pub Date : 2022-05-12 DOI: 10.1017/pan.2023.1

Y. Shiraito, James Lo, S. Olivella

{"title":"A Nonparametric Bayesian Model for Detecting Differential Item Functioning: An Application to Political Representation in the US","authors":"Y. Shiraito, James Lo, S. Olivella","doi":"10.1017/pan.2023.1","DOIUrl":"https://doi.org/10.1017/pan.2023.1","url":null,"abstract":"Abstract A common approach when studying the quality of representation involves comparing the latent preferences of voters and legislators, commonly obtained by fitting an item response theory (IRT) model to a common set of stimuli. Despite being exposed to the same stimuli, voters and legislators may not share a common understanding of how these stimuli map onto their latent preferences, leading to differential item functioning (DIF) and incomparability of estimates. We explore the presence of DIF and incomparability of latent preferences obtained through IRT models by reanalyzing an influential survey dataset, where survey respondents expressed their preferences on roll call votes that U.S. legislators had previously voted on. To do so, we propose defining a Dirichlet process prior over item response functions in standard IRT models. In contrast to typical multistep approaches to detecting DIF, our strategy allows researchers to fit a single model, automatically identifying incomparable subgroups with different mappings from latent traits onto observed responses. We find that although there is a group of voters whose estimated positions can be safely compared to those of legislators, a sizeable share of surveyed voters understand stimuli in fundamentally different ways. Ignoring these issues can lead to incorrect conclusions about the quality of representation.","PeriodicalId":48270,"journal":{"name":"Political Analysis","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2022-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47706513","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Sentiment is Not Stance: Target-Aware Opinion Classification for Political Text Analysis 情绪不是立场：政治文本分析的目标意识观点分类

IF 5.4 2区社会学

Political Analysis Pub Date : 2022-04-22 DOI: 10.1017/pan.2022.10

Samuel E. Bestvater, B. Monroe

{"title":"Sentiment is Not Stance: Target-Aware Opinion Classification for Political Text Analysis","authors":"Samuel E. Bestvater, B. Monroe","doi":"10.1017/pan.2022.10","DOIUrl":"https://doi.org/10.1017/pan.2022.10","url":null,"abstract":"Abstract 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 scores can capture the overall tone of a document, the underlying concept of interest for political analysis is often actually the document’s stance with respect to a given target—how positively or negatively it frames a specific idea, individual, or group—as this reflects the author’s underlying political attitudes. In this paper, we question the validity of approximating author stance through sentiment scoring in the analysis of political texts, and advocate for greater attention to be paid to the conceptual distinction between a document’s sentiment and its stance. Using examples from open-ended survey responses and from political discussions on social media, we demonstrate that in many political text analysis applications, sentiment and stance do not necessarily align, and therefore sentiment analysis methods fail to reliably capture ground-truth document stance, amplifying noise in the data and leading to faulty conclusions.","PeriodicalId":48270,"journal":{"name":"Political Analysis","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2022-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47418268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 11

Statistically Valid Inferences from Differentially Private Data Releases, with Application to the Facebook URLs Dataset 统计上有效的推论从不同的私人数据发布，与应用到Facebook url数据集

IF 5.4 2区社会学

Political Analysis Pub Date : 2022-04-20 DOI: 10.1017/pan.2022.1

Georgina Evans, Gary King

引用次数: 25