Persuasion with Precision: Using Natural Language Processing to Improve Instrument Fidelity for Risk Communication Experimental Treatments

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