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Identifying the underlying psychological constructs from self-expressed anti-vaccination argumentation

Author

Listed:
  • Dawn Holford

    (University of Bristol)

  • Ezequiel Lopez-Lopez

    (Max Planck Institute for Human Development)

  • Angelo Fasce

    (University of Coimbra)

  • Linda C. Karlsson

    (University of Turku)

  • Stephan Lewandowsky

    (University of Bristol
    University of Potsdam
    University of Western Australia)

Abstract

People’s negative attitudes to vaccines can be motivated by psychological factors—such as fears, ideological beliefs, and cognitive patterns—known as ‘attitude roots’. This study had two primary objectives: (1) to identify which of 11 known attitude roots are featured in individuals’ self-expressed reasons for negative vaccine attitudes (i.e., a linguistic analysis); (2) to explore how attitude roots present in self-expressed texts are linked to specific psychological measures. To achieve Objective 1, our study collected data from December 2022 to January 2023 from 556 participants from the US, who wrote texts to explain the reasons for their negative vaccine attitudes. The texts encompassed 2327 conceptually independent units of anti-vaccination argumentation, that were each coded for its attitude root(s) by at least two psychological experts. By allowing participants to spontaneously express their attitudes in their own words, we were able to observe how this differed from what participants reported to endorse when presented with a list of arguments. We found that there were four groups of attitude roots based on linguistic similarity in self-expression. In addition, latent class analysis of participants’ coded texts identified three distinct groups of participants that were characterised by their tendency to express combinations of arguments related to (1) fears, (2) anti-scientific conceptions, and (3) politicised perspectives. To achieve Objective 2, we collected participants’ responses to 11 validated measures of psychological constructs expected to underlie the respective 11 attitude roots, and used a correlational design to investigate how participants’ self-expressed attitude roots were linked to these measures. Logistic regressions showed that an expected psychological construct was the strongest, and significant, predictor for expression of three out of the four attitude root groups. We discuss the implications of these findings for health communicators and practitioners.

Suggested Citation

  • Dawn Holford & Ezequiel Lopez-Lopez & Angelo Fasce & Linda C. Karlsson & Stephan Lewandowsky, 2024. "Identifying the underlying psychological constructs from self-expressed anti-vaccination argumentation," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-13, December.
    • Handle: RePEc:pal:palcom:v:11:y:2024:i:1:d:10.1057_s41599-024-03416-4
    DOI: 10.1057/s41599-024-03416-4
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    References listed on IDEAS

    as
    1. Jana Lasser & Segun T. Aroyehun & Fabio Carrella & Almog Simchon & David Garcia & Stephan Lewandowsky, 2023. "From alternative conceptions of honesty to alternative facts in communications by US politicians," Nature Human Behaviour, Nature, vol. 7(12), pages 2140-2151, December.
    2. Angelo Fasce & Philipp Schmid & Dawn L. Holford & Luke Bates & Iryna Gurevych & Stephan Lewandowsky, 2023. "A taxonomy of anti-vaccination arguments from a systematic literature review and text modelling," Nature Human Behaviour, Nature, vol. 7(9), pages 1462-1480, September.
    3. Anna Soveri & Linda C Karlsson & Otto Mäki & Jan Antfolk & Otto Waris & Hasse Karlsson & Linnea Karlsson & Mikael Lindfelt & Stephan Lewandowsky, 2020. "Trait reactance and trust in doctors as predictors of vaccination behavior, vaccine attitudes, and use of complementary and alternative medicine in parents of young children," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-16, July.
    4. Linzer, Drew A. & Lewis, Jeffrey B., 2011. "poLCA: An R Package for Polytomous Variable Latent Class Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 42(i10).
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