DDRIG in DRMS: Measuring Persuasion Without Measuring a Prior Belief: A New Application of Planned Missing Data Techniques

DRMS 中的 DDRIG：在不衡量先验信念的情况下衡量说服力：计划丢失数据技术的新应用

• 批准号: 2242100
• 负责人: David Budescu
• 金额: $ 2.46万
• 依托单位: Fordham University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-01 至 2024-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2242100&HistoricalAwards=false
• 关键词: DDRIG; DRMS; Measuring; Persuasion; Without

Experiments on advice taking typically require a person receiving advice (an “advisee”) express their belief about the topic they are being advised about before receiving the actual advice. Then, after advice is given, the advisee reports a new belief. Researchers can then study the effects of advice by looking at how the advisee’s belief changed. However, there is a potential problem with this setup. Making the advisee explicitly consider their expectation before receiving advice can change its effects, but there is no way to study how the advisee’s belief changes without knowing what they believed beforehand. This research uses cutting edge statistical methods to estimate advisees’ prior beliefs based on other factors that are known about them, allowing researchers to understand the influence of advice under more realistic conditions, where advisees don’t have to directly think about what they believe before being advised. The scientific term for situations where measurement affects subsequent behavioral processes is “measurement reactivity”. This project’s approach to curing this measurement reactivity is to treat advisees’ prior beliefs as missing data. Advances in statistical methods allow researchers to impute missing data with without bias under the right set of circumstances. These methods are typically utilized when data are missing incidentally, but this project will experimentally randomize when data are missing, ensuring the correct conditions for the imputation to work effectively. Because these advisees never directly report their prior beliefs, the researchers can study how advice utilization occurs in the absence of measurement reactivity. This work has important implications for how advisors can help advisees optimize their expertise in many real-life scenarios. The methodological innovation of using a planned missing data experimental design to cure measurement reactivity also has the potential to generalize to other scientific domains where it is a potential confound.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

接受建议的实验通常需要接受建议（“咨询”）的人在收到实际建议之前对他们所建议的主题表示信念。然后，在提出建议后，咨询报告了一种新的信念。然后，研究人员可以通过查看咨询所相信的如何改变来研究建议的效果。但是，此设置存在潜在的问题。在收到建议之前，明确考虑他们的期望可以改变其效果，但是没有办法研究咨询的信仰如何改变而不知道他们事先相信什么。这项研究使用最先进的统计方法来估计咨询的基于对其的其他因素的先前信念，从而使研究人员能够在更现实的条件下了解建议的影响，在这种情况下，咨询不必直接考虑他们在被告知之前的信念。测量结果影响后续行为过程的科学术语是“测量反应性”。该项目治愈这种测量反应性的方法是将建议的先前信念视为缺失的数据。统计方法的进步使研究人员可以在正确的情况下实施没有偏见的丢失数据。这些方法通常在偶然缺少数据时使用，但是当数据丢失时，该项目将在实验中随机化，从而确保插定有效工作的正确条件。由于这些建议从未直接报告其先前的信念，因此研究人员可以研究在没有测量反应性的情况下如何进行建议利用。这项工作对顾问如何帮助建议在许多现实生活中优化其专业知识具有重要意义。使用计划中的数据实验设计来治愈测量反应性的方法论创新也有可能概括到其他科学领域，在该领域中，这是一种潜在的混杂。该奖项反映了NSF的法定任务，并通过使用基金会的知识分子优点和更广泛的影响审查标准来通过评估来诚实地认为通过评估来诚实地支持。