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要求接受建议的人（“被建议者”）在接受实际建议之前表达他们对建议主题的信念。然后，在给出建议后，被劝告者报告了一个新的信念。研究人员可以通过观察受试者的信念如何改变来研究建议的效果。但是，这种设置存在一个潜在的问题。在接受建议之前，让受试者明确考虑他们的期望可以改变建议的效果，但是如果不知道受试者事先相信什么，就没有办法研究他们的信念是如何变化的。这项研究使用最先进的统计方法，根据已知的其他因素来估计被咨询者的先前信念，使研究人员能够在更现实的条件下了解建议的影响，被咨询者在被咨询之前不必直接思考他们相信什么。测量影响后续行为过程的情况的科学术语是“测量反应性”。这个项目治疗这种测量反应性的方法是将被测量者的先前信念视为缺失数据。统计方法的进步使研究人员能够在正确的情况下无偏倚地估算缺失数据。这些方法通常在数据偶然缺失时使用，但本项目将在数据缺失时进行实验性随机化，确保插补有效工作的正确条件。因为这些被调查者从不直接报告他们先前的信念，研究人员可以研究在没有测量反应的情况下如何利用建议。这项工作对于顾问如何帮助被访者在许多现实生活场景中优化他们的专业知识具有重要意义。使用计划的缺失数据实验设计来治疗测量反应性的方法创新也有可能推广到其他科学领域，在这些领域它是一个潜在的混淆。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。