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Elaboration Likelihood Analysis×Gain-Loss Message Framing Analysis×
NozareCommunicationCommunication
SaimeProcess / pipelineProcess / pipeline
Izcelsmes gads19861997
AutorsRichard Petty & John CacioppoRothman & Salovey (health framing synthesis); roots in Kahneman & Tversky
TipsDual-process experimental analysis of attitude changeExperiment comparing gain- versus loss-framed persuasive messages
PirmavotsPetty, R. E., & Cacioppo, J. T. (1986). The elaboration likelihood model of persuasion. In Communication and Persuasion (pp. 1–24). New York: Springer. DOI ↗Rothman, A. J., & Salovey, P. (1997). Shaping perceptions to motivate healthy behavior: The role of message framing. Psychological Bulletin, 121(1), 3–19. DOI ↗
Citi nosaukumiELM analysis, Dual-process persuasion experiment, Central and peripheral route analysis, Ayrıntılandırma Olasılığı Modeli AnaliziMessage framing analysis, Gain-loss framing study, Valence framing experiment, Kazanç-Kayıp Mesaj Çerçeveleme Analizi
Saistītās44
KopsavilkumsElaboration likelihood analysis applies Petty and Cacioppo's 1986 Elaboration Likelihood Model (ELM) to study persuasion through experiments that cross message argument quality with peripheral cues under varying levels of audience motivation and ability to think. It identifies whether attitude change travels the central route — effortful scrutiny of arguments — or the peripheral route, reliance on simple cues like source attractiveness or message length.Gain-loss message framing analysis is an experimental method for testing whether a persuasive appeal works better when it stresses the benefits of acting (gain frame) or the costs of not acting (loss frame). Grounded in prospect theory and synthesized for health communication by Rothman and Salovey, it predicts that loss frames are more persuasive for risky detection behaviors while gain frames win for safe prevention behaviors.
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ScholarGateSalīdzināt metodes: Elaboration Likelihood Analysis · Gain-Loss Message Framing Analysis. Izgūts 2026-06-24 no https://scholargate.app/lv/compare