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Research Article

Quantifying Emotional Flow: Testing the Emotional Flow Hypothesis from a Longitudinal Latent Growth Curve (LGC) Modeling Approach

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References

  • Adams, E. T., Nabi, R., Noar, S., Evans, R., & Widman, L. (2021) How emotional shifts affect youth perceptions of opioid risk and efficacy: Testing a Know the Truth campaign narrative. Health Communication. https://doi.org/10.1080/10410236.2021.1921349.
  • Alam, N., & So, J. (2020). Contributions of emotional flow in narrative persuasion: An empirical test of the emotional flow framework. Communication Quarterly, 68(2), 161–182. https://doi.org/10.1080/01463373.2020.1725079
  • Bollen, K. A., & Paxton, P. (1998). Interactions of latent variables in structural equation models. Structural Equation Modeling, 5(3), 267–293. https://doi.org/10.1080/10705519809540105
  • Dillard, J. P., & Anderson, J. (2004). The role of fear in persuasion. Psychology & Marketing, 21(11), 909–926. https://doi.org/10.1002/mar.20041
  • Dillard, J. P., Li, R., Meczkowski, E., Yang, C., & Shen, L. (2017). Fear responses to threat appeals: Functional form, methodological considerations, and correspondence between static and dynamic data. Communication Research, 44(7), 997–1018. https://doi.org/10.1177/2F0093650216631097
  • Dillard, J. P., & Shen, L. (2018). Threat appeals as multi-emotion messages: An argument structure model of fear and disgust. Human Communication Research, 44(2), 103–126. https://doi.org/10.1093/hcr/hqx002
  • Dolinski, D., & Nawrat, R. (1998). “Fear-then-relief “ procedure for producing compliance: Beware when the danger is over. Journal of Experimental Social Psychology, 34(1), 27–50. https://doi.org/10.1006/jesp.1997.1341
  • Fisher, A. J., Medaglia, J. D., & Jeronimus, B. F. (2018). Lack of group-to-individual generalizability is a threat to human subjects research. PNAS, 115(27), E6106–6115. https://doi.org/10.1073/pnas.1711978115
  • Folwarczny, M., Kaczmarek, M. C., Dolinski, D., & Szczepanowski, R. (2018). Emotional sea-saw affects rationality of decision-making: Evidence for metacognitive impairments. Acta Psychologica, 186, 126–132. https://doi.org/10.1016/j.actpsy.2018.04.012
  • Grimm, K. J., Ram, N., & Estabrook, R. (2017). Growth modeling: Structural equation and multilevel modeling approaches. Guilford.
  • Guido, G., Pichierri, M., & Pino, G. (2018). Place the good after the bad: Effects of emotional shifts on consumer memory. Marketing Letters, 29(1), 49–60. https://doi.org/10.1007/s11002-017-9439-0
  • Hamaker, E. L., Asparouhov, T., Brose, A., Schmiedek, F., & Muthén, B. (2018). At the frontiers of modeling intensive longitudinal data: Dynamic structural equation models for the affective measurements from the COGITO study. Multivariate Behavioral Research, 53(6), 820–841. https://doi.org/10.1080/00273171.2018.1446819
  • Kolenikov, S., & Bollen, K. A. (2012). Testing negative error variances: Is a Heywood case a symptom of misspecification? Sociological Methods & Research, 41(1), 124–167. https://doi.org/10.1177/2F0049124112442138
  • Little, T. D. (2013). Longitudinal structural equation modeling. Guilford.
  • Mauss, I. B., & Robinson, M. D. (2009). Measures of emotion: A review. Cognition and Emotion, 23(2), 209–237. https://doi.org/10.1080/02699930802204677
  • Molenaar, P. C. M. (2004). A manifesto on psychology as idiographic science: Bringing the person back into scientific psychology, this time forever. Measurement, 2(4), 201–218. https://doi.org/10.1207/s15366359mea0204_1
  • Nabi, R. (2015). Emotional flow in persuasive health messages. Health Communication, 30(2), 114–124. https://doi.org/10.1080/10410236.2014.974129
  • Nabi, R., & Green, M. C. (2015). The role of narrative’s emotional flow in promoting persuasive outcomes. Media Psychology, 18(2), 137–162. https://doi.org/10.1080/15213269.2014.912585
  • Ophir, Y., Sangalang, A., & Cappella, J. N. (2021). The emotional flow hypothesis in entertainment-education narratives: Theory, empirical evidence, and open questions. In L. B. Frank, & P. Falzone (Eds.), Entertainment-education behind the scenes: Case studies for theory and practice (pp. 103–120). Palgrave Macmillan.
  • Petty, R. E., Gleicher, F., & Baker, S. M. (1991). Multiple roles for affect in persuasion. In J. P. Forgas (Ed.), Emotion and social judgments (pp. 181–200). Pergamon Press.
  • Rosseel, Y. (2012). Lavaan: An R package for structural equation modeling. Journal of Statistical Software, 48(2), 1–36. https://www.jstatsoft.org/v48/i02/
  • Rossiter, J. R., & Thornton, J. (2004). Fear-pattern analysis supports the fear-drive model for road safety TV-ads. Psychology & Marketing, 21(11), 945–960. https://doi.org/10.1002/mar.20042
  • Scherer, K. R. (2005). What are emotions? And how can they be measured? Social Science Information, 44(4), 695–729. https://doi.org/10.1177/0539018405058216
  • Shen, L. (2017). Putting the fear back again (and within individuals): Revisiting the role of fear in persuasion. Health Communication, 32, 1331–1341. https://doi.org/10.1080/10410236.2016.1220043
  • Shen, L., & Dillard, J. P. (2014). Threat, fear, and persuasion: Review and critique of questions about functional form. Review of Communication Research, 2, 94–114. https://doi.org/10.12840/issn.2255-4165.2014.02.01.004
  • Shen, L., Li, S. S., Sweeney, K., & Lee, D. (2022). Re-visiting Hope as a Discrete Emotion and its Role in Persuasion. Communication quarterly.
  • Siegenthaler, P., Ort, A., & Fahr, A. (2021). The influence of valence shifts in fear appeals on message processing and behavioral intentions: A moderated mediation model. PLOS ONE, 16(9), e0255113. https://doi.org/10.1371/journal.pone.0255113
  • Usami, S., Murayama, K., & Hamaker, E. L. (2019). A unified framework of longitudinal models to examine reciprocal relations. Psychological Methods, 24(5), 637–657. http://dx.doi.org/10.1037/met0000210
  • Vautier, S., & Raufaste, E. (2003). Measuring dynamic bipolarity in positive and negative activation. Assessment, 10(1), 49–55. https://doi.org/10.1177/2F1073191102250338
  • Wickrama, K. A. S., Lee, T. K., O’Neal, C. W., & Lorenz, F. O. (2022). Higher-order growth curves and mixture modeling with Mplus: A practical guide (2nd ed.). Routledge.
  • Winkler, J. R., Appel, M., Schmidt, M., & Richter, T. (2022). The experience of emotional shifts in narrative persuasion. Media Psychology, 1–31. https://doi.org/10.1080/15213269.2022.2103711
  • Zyphur, M. J., Allison, P. D., Tay, L., & Voelkle, M. C. (2020a). From data to causes I: Building a general cross-lagged panel model (GCLM). Organizational Research Methods, 23(4), 651–687. https://doi.org/10.1177/2F1094428119847278
  • Zyphur, M. J., Allison, P. D., Tay, L., & Voelkle, M. C. (2020b). From data to causes II: Comparing approaches to panel data analysis. Organizational Research Methods, 23(4), 688–716. https://doi.org/10.1177/2F1094428119847280

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