{"title":"将选民偏好与不同来源的政党立场估计相结合,研究投票行为和代表性","authors":"Anna-Sophie Kurella , Milena Rapp","doi":"10.1016/j.electstud.2023.102734","DOIUrl":null,"url":null,"abstract":"<div><p>Researchers interested in policy voting and substantive representation face the challenge to combine party positions with voter preference data on a common scale. One solution is to rely on voters’ perceptions of parties’ policy positions, as reported in surveys. However, this kind of data is often only available for the common left–right dimension, but not for more concrete policy scales, and it suffers from bias. We first discuss how to free perceptual data from bias by relying on a Bayesian version of the Aldrich–McKelvey rescaling technique. Then we discuss two prominent alternative sources of party position estimates: expert survey positions, and positions based on the CMP coding scheme of the manifesto project. While both types of party position estimates are considered to be of good quality, it is unclear how they fit into voter preference scales. This paper presents a simple rescaling technique that improves the matching.</p></div>","PeriodicalId":48188,"journal":{"name":"Electoral Studies","volume":null,"pages":null},"PeriodicalIF":2.9000,"publicationDate":"2023-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Combining voter preferences with party position estimates from different sources for studying voting behavior and representation\",\"authors\":\"Anna-Sophie Kurella , Milena Rapp\",\"doi\":\"10.1016/j.electstud.2023.102734\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Researchers interested in policy voting and substantive representation face the challenge to combine party positions with voter preference data on a common scale. One solution is to rely on voters’ perceptions of parties’ policy positions, as reported in surveys. However, this kind of data is often only available for the common left–right dimension, but not for more concrete policy scales, and it suffers from bias. We first discuss how to free perceptual data from bias by relying on a Bayesian version of the Aldrich–McKelvey rescaling technique. Then we discuss two prominent alternative sources of party position estimates: expert survey positions, and positions based on the CMP coding scheme of the manifesto project. While both types of party position estimates are considered to be of good quality, it is unclear how they fit into voter preference scales. This paper presents a simple rescaling technique that improves the matching.</p></div>\",\"PeriodicalId\":48188,\"journal\":{\"name\":\"Electoral Studies\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.9000,\"publicationDate\":\"2023-12-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Electoral Studies\",\"FirstCategoryId\":\"90\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0261379423001567\",\"RegionNum\":2,\"RegionCategory\":\"社会学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"POLITICAL SCIENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Electoral Studies","FirstCategoryId":"90","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0261379423001567","RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"POLITICAL SCIENCE","Score":null,"Total":0}
Combining voter preferences with party position estimates from different sources for studying voting behavior and representation
Researchers interested in policy voting and substantive representation face the challenge to combine party positions with voter preference data on a common scale. One solution is to rely on voters’ perceptions of parties’ policy positions, as reported in surveys. However, this kind of data is often only available for the common left–right dimension, but not for more concrete policy scales, and it suffers from bias. We first discuss how to free perceptual data from bias by relying on a Bayesian version of the Aldrich–McKelvey rescaling technique. Then we discuss two prominent alternative sources of party position estimates: expert survey positions, and positions based on the CMP coding scheme of the manifesto project. While both types of party position estimates are considered to be of good quality, it is unclear how they fit into voter preference scales. This paper presents a simple rescaling technique that improves the matching.
期刊介绍:
Electoral Studies is an international journal covering all aspects of voting, the central act in the democratic process. Political scientists, economists, sociologists, game theorists, geographers, contemporary historians and lawyers have common, and overlapping, interests in what causes voters to act as they do, and the consequences. Electoral Studies provides a forum for these diverse approaches. It publishes fully refereed papers, both theoretical and empirical, on such topics as relationships between votes and seats, and between election outcomes and politicians reactions; historical, sociological, or geographical correlates of voting behaviour; rational choice analysis of political acts, and critiques of such analyses.