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在行为研究中人工智能增强内容验证:RATER系统的开发和评估

Jean-Charles Pillet¹, Kai R Larsen², David Dobolyi²

¹TBS Business School, 31000 Toulouse, France.

MIS quarterly : management information systems

|October 22, 2025

概括

内容验证对于测量仪器至关重要. 一个新的免费工具,RATER (专家评级可复制方法),使用人工智能为行为研究尺度提供快速,可靠的内容有效性见解.

科学领域:

心理测量心理测量心理测量
行为科学行为科学
人工智能的人工智能

背景情况:

内容验证对于测量仪器的开发至关重要,确保尺度准确地捕获预期的构造.
研究人员经常因为高成本和专业专业知识要求而跳过内容验证.
现有的内容验证方法资源密集,限制了它们在行为研究中的广泛使用.

研究的目的:

引入RATER (专家评级可复制方法),这是一个免费的,基于Web的系统,旨在简化内容有效性评估.
为研究人员,审核人员和学生提供可访问和可靠的工具来评估测量仪器.
通过自动化内容有效性洞察来增强行为研究中的规模开发和验证过程.

主要方法:

RATER利用心理测量理论来评估尺度项目与预期的构造,构造的独特性和域名表示.
该系统包含两个人工智能模型,RATER_C和RATER_D,在来自不同学科的2,443个心理测量尺度上进行训练.
杆先进的大型语言模型架构,包括BERT和GPT,用于复杂的分析.

主要成果:

六项研究证实了RATER系统的准确性,可靠性和实用性.
雷特 (RATER) 证明了它能够快速和可靠地提供有关测量仪器内容有效性的洞察力.
该系统有效地评估尺度项是否与其预期的构造保持一致,并全面地代表域.

结论:

通过提供高效可靠的内容验证方法,RATER显著增加了规模开发和验证过程.
该工具使访问内容有效性评估变得民主化,克服了以前成本和专业知识的障碍.
通过提高测量仪器的质量,实施RATER可以在行为研究中带来更有效和更强大的发现.

关键词:

行为研究行为研究.内容有效性内容有效性设计科学研究设计科学研究大型语言模型 (LLM)机器学习是机器学习.心理测量是指心理测量.研究研究严谨严谨的研究.规模发展发展规模发展.

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