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Persuasion with Precision: Using Natural Language Processing to Improve Instrument Fidelity for Risk Communication Experimental Treatments
Journal of Mixed Methods Research ( IF 5.746 ) Pub Date : 2022-05-09 , DOI: 10.1177/15586898221096934
Ann Marie Reinhold 1, 2, 3 , Eric D. Raile 4 , Clemente Izurieta 2 , Jamie McEvoy 3, 5 , Henry W. King 2 , Geoffrey C. Poole 1, 3 , Richard C. Ready 3, 6 , Nicolas T. Bergmann 5 , Elizabeth A. Shanahan 4
Affiliation  

Instrument fidelity in message testing research hinges upon how precisely messages operationalize treatment conditions. However, numerous message testing studies have unmitigated threats to validity and reliability because no established procedures exist to guide construction of message treatments. Their construction typically occurs in a black box, resulting in suspect inferential conclusions about treatment effects. Because, a mixed methods approach is needed to enhance instrument fidelity in message testing research, this article contributes to the field of mixed methods research by presenting an integrated multistage procedure for constructing precise message treatments using an exploratory sequential mixed methods design. This work harnesses the power of integration through crossover analysis to improve instrument fidelity in message testing research through the use of natural language processing (NLP).



中文翻译:

精确说服:使用自然语言处理提高风险交流实验处理的仪器保真度

信息测试研究中的仪器保真度取决于信息如何精确地操作治疗条件。然而,大量的消息测试研究对有效性和可靠性有很大的威胁,因为没有既定的程序来指导构建的消息处理。它们的构造通常发生在黑匣子中,从而导致关于治疗效果的可疑推断结论。因为,需要一种混合方法方法来提高消息测试研究中的仪器保真度,所以本文通过提出使用探索性顺序混合方法设计构建精确消息处理的集成多阶段程序,为混合方法研究领域做出贡献。这项工作通过交叉分析利用集成的力量,通过使用自然语言处理 (NLP) 来提高消息测试研究中的仪器保真度。

更新日期:2022-05-12
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