five

PUMA Survey 5.3. Insights in societal changes in Austria

收藏
CESSDA2024-09-14 更新2024-08-10 收录
下载链接:
https://datacatalogue.cessda.eu/detail?lang=en&q=7a0e85154e2de3fef05c20babeee17c92ecdf88e07eaf6dff13ffc86beb30ad1
下载链接
链接失效反馈
资源简介:
Full edition for scientific use. PUMA Surveys consist of separate modules designed and prepared by different principle investigators. This PUMA Survey consists of two modules: MODULE 1 "Trick of the Traits. An experimental study on trait ownership and mediated leader effects", MODULE 2 "An Experimental Assessment of Approval and Evaluative Voting". Fieldwork was conducted by MARKETAGENT.<br><br/> MODULE 1: Trick of the Traits. An experimental study on trait ownership and mediated leader effects (Loes Aalerding, Sophie Lecheler) <br> This study tests, by means of a survey experiment, how leader perceptions are affected by media portrayals of party leaders in terms of their leadership traits, and to what extent partisan stereotypes and trait ownership moderates this relationship. Research has shown that citizens’ subjective party leader perceptions, especially in terms of leadership traits, affect voting behavior (e.g., Bittner, 2011; Aarts, Blais, & Schmitt, 2013). What remains a largely unresolved question, however, is which trait evaluations matter most. The main goal of this study is to test how media messages of party leaders in terms of their leadership traits affects voters’ perception of those party leaders and to what extent trait ownership moderates this relation. The contribution of the study is threefold. First, it takes into account that current political life is highly mediatized by focusing on mediated leader effects. Second, it strengthens the causal claim of (the conditionality) of leader effects by using an experimental research design as opposed to correlational data. Third, it is the first to test the theory of trait ownership in Austria and therefore (completely) outside the two-party context of the US. <br/><br>MODULE 2: An Experimental Assessment of Approval and Evaluative Voting (Philipp Harfst, Jean-Francois Laslier, Damien Bol) <br> In our PUMA module, we ran an online survey experiment in which we asked a representative sample of the Austrian population to cast a vote. We created a ballot to similar to the one of the 2017 election of the National Council. The respondents saw on their screen the main parties and the main candidates of these parties. Then, they had to indicate their preference for one of the parties and for 15 individual candidates within this party. The experimental treatment is the type of preference vote the respondents could cast to express their preference for individual candidates. A third of the respondents (randomly selected) could choose to approve each of the candidates or not [0,1]. This binary system is often called Approval Voting (AV). Another third of the respondents (randomly selected) could give 0, 1, or 2 points to each of the candidates. The last third of the respondents could give a positive, a negative, or no points to each of the candidates [-1,0,1]. These last two systems are two different versions to what is usually referred to as Evaluative Voting (EV). The goal of our research is to study the effect of the type of preference voting on voters’ decisions. The survey was fielded in June 2018 and targeted the population of eligible Austrian voters. The sample size is 700 respondents, and is representative of the Austrian population in terms of gender, age and education. The survey was conducted online, which is the best survey model for this type of study. Unlike telephone interviews, online surveys allow for a visualisation of the ballot, which helps improve the quality of responses. Also, this way of asking for respondents’ vote choice has already been successfully implemented in other contexts (Laslier et al. 2015). <br/>
提供机构:
The Austrian Social Science Data Archive
创建时间:
2019-04-12
用户留言
有没有相关的论文或文献参考?
这个数据集是基于什么背景创建的?
数据集的作者是谁?
能帮我联系到这个数据集的作者吗?
这个数据集如何下载?
点击留言
数据主题
具身智能
数据集  4098个
机构  8个
大模型
数据集  439个
机构  10个
无人机
数据集  37个
机构  6个
指令微调
数据集  36个
机构  6个
蛋白质结构
数据集  50个
机构  8个
空间智能
数据集  21个
机构  5个
5,000+
优质数据集
54 个
任务类型
进入经典数据集
热门数据集

TPTP

TPTP(Thousands of Problems for Theorem Provers)是一个包含大量逻辑问题的数据集,主要用于定理证明器的测试和评估。它包含了多种逻辑形式的问题,如一阶逻辑、高阶逻辑、命题逻辑等。

www.tptp.org 收录

O*NET

O*NET(Occupational Information Network)是一个综合性的职业信息数据库,提供了关于各种职业的详细描述,包括技能要求、工作活动、知识领域、工作环境等。该数据集被广泛用于职业分析、教育和劳动力市场研究。

www.onetonline.org 收录

CE-CSL

CE-CSL数据集是由哈尔滨工程大学智能科学与工程学院创建的中文连续手语数据集,旨在解决现有数据集在复杂环境下的局限性。该数据集包含5,988个从日常生活场景中收集的连续手语视频片段,涵盖超过70种不同的复杂背景,确保了数据集的代表性和泛化能力。数据集的创建过程严格遵循实际应用导向,通过收集大量真实场景下的手语视频材料,覆盖了广泛的情境变化和环境复杂性。CE-CSL数据集主要应用于连续手语识别领域,旨在提高手语识别技术在复杂环境中的准确性和效率,促进聋人与听人社区之间的无障碍沟通。

arXiv 收录

抖音用户行为数据集

(自用)本数据集搜集并收录了122539条2022年7月24日至31日的一周时间内,1000名抖音用户观看短视频的行为记录数据,每条数据都包含6个词条,包括用户ID、视频ID、视频主题、是否喜欢、是否转发、时间戳等数据。

阿里云天池 收录

中国交通事故深度调查(CIDAS)数据集

交通事故深度调查数据通过采用科学系统方法现场调查中国道路上实际发生交通事故相关的道路环境、道路交通行为、车辆损坏、人员损伤信息,以探究碰撞事故中车损和人伤机理。目前已积累深度调查事故10000余例,单个案例信息包含人、车 、路和环境多维信息组成的3000多个字段。该数据集可作为深入分析中国道路交通事故工况特征,探索事故预防和损伤防护措施的关键数据源,为制定汽车安全法规和标准、完善汽车测评试验规程、

北方大数据交易中心 收录