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Too strong to care? Investigating the links between formidability, worldviews, and views on climate and disaster

Published online by Cambridge University Press:  06 December 2022

Marjorie L. Prokosch*
Affiliation:
University of Florida Rochester Institute of Technology
Colin Tucker Smith
Affiliation:
University of Florida
Nicholas Kerry
Affiliation:
University of Pennsylvania
Jason von Meding
Affiliation:
University of Florida
*
*Correspondence author. Email: mlpgsh@rit.edu

Abstract

People vary in climate change skepticism and in their views on disaster cause and prevention. For example, the United States boasts higher rates of climate skepticism than other countries, especially among Republicans. Research into the individual differences that shape variation in climate-related beliefs represents an important opportunity for those seeking ways to mitigate climate change and climate-related disasters (e.g., floods). In this registered report, we proposed a study examining how individual difference in physical formidability, worldview, and affect relate to attitudes about disaster and climate change. We predicted that highly formidable men would tend to endorse social inequality, hold status quo defensive worldviews, report lower levels of empathy, and report attitudes that promote disaster risk accumulation via lesser support for social intervention. The results of an online study (Study 1) support the notion that men’s self-perceived formidability is related to disaster and climate change beliefs in the predicted direction and that this relationship is mediated by hierarchical worldview and status quo defense but not empathy. An analysis of a preliminary sample for the in-lab study (Study 2) suggests that self-perceived formidability relates to disaster views, climate views, and status quo maintaining worldviews.

Information

Type
Life Science in Politics: Methodological Innovations and Political Issues
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
© The Author(s), 2022. Published by Cambridge University Press on behalf of the Association for Politics and the Life Sciences
Figure 0

Figure 1. Theoretical model for proposed studies. Letter strings and symbols (e.g., H1a +) indicate the relevant hypothesis and direction of prediction for a specific model path (e.g., Hypothesis 1a, positive relationship).

Figure 1

Figure 2. Planned analytical model for online study. Squares represent survey scale composites (observed variables), while circles denote latent variables. Black arrows represent scale factor loadings onto latent variables, while blue arrows represent regression pathways.

Figure 2

Table 1. Sample characteristics for Study 1.

Figure 3

Table 2. Zero-order correlations between Study 1 variables.

Figure 4

Table 3. Study 1, zero-order correlations by gender.

Figure 5

Table 4. Study 1 model fit statistics.

Figure 6

Figure 3. Study 1 analytical model fit within men and women. Standardized estimates and values are depicted here. * p < .05; ** p ≤ .01; *** p ≤ .001.

Figure 7

Table 5. Publication sample characteristics for Study 2.

Figure 8

Figure 4. Analytical model for lab study. Squares represent survey scale composites (observed variables), while circles denote latent variables. Black arrows represent scale factor loadings onto latent variables, while blue arrows represent regression pathways.

Figure 9

Table 6. Zero-order correlations between Study 2 variables in publication sample.

Figure 10

Table 7. Zero-order correlations between Study 2 variables by gender in publication sample.

Figure 11

Table 8. Study 2 structural equation model fit statistics.

Figure 12

Table 9. Summary of support for registered hypotheses.

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