Fig 1.
Distribution of frequency of religious attendance.
Table 1.
Religious identities of participants.
Fig 2.
Strength of supernatural beliefs by religious identity.
Fig 3.
Belief in karma by religious identity.
Fig 4.
Participants by self-reported economic values (0-most liberal; 10-most conservative).
Fig 5.
Distribution of social political values (0- most liberal; 10-most conservative).
Fig 6.
Distribution of nationalist attitudes.
Fig 7.
Distributions of nationalism by religious identity.
Fig 8.
Heat map showing clusters of nationalism by age.
Fig 9.
Heatmap of nationalism by age for males.
Fig 10.
Heatmap of nationalism by age for females.
Table 2.
Regressions predicting nationalism levels from threats.
Fig 11.
Levels of perceived threat by gender.
Table 3.
Results of GLM to assess effects of threat on anti-immigrant sentiment.
Table 4.
Effect of threats on supernatural beliefs.
Table 5.
Predicting levels of religious and national social identity (sid) and identity fusion (ift) as functions of social threats.
Table 6.
Regressions showing the effects of threats on different personality traits from the Big-5 personality measures.
Fig 12.
Correlation heatmap of variables in the study used to create our structural equation model.
Fig 13.
A plausible structural equation model explaining data trends to inform a system dynamics model of nationalism and political sentiment related to threats and supernatural beliefs.
Table 7.
a Factor loadings, regressions, and variances for the Structure Equation Model in Fig 12.
b Residuals, latent variances and fit indices for the Structure Equation Model in Fig 12.
Fig 14.
Importance of key factors in predicting if someone will hold anti-immigrant sentiment.
Fig 15.
A visual depiction of the system dynamics model used to model threat increases and decreases as functions of stimulus and habituation.
Fig 16.
Correlation heatmap of simulation output results.
Fig 17.
Cluster plot of relationship between liberal (left-wing) social values and anti-immigrant sentiment.
Table 8.
Regression data from simulated data regarding anti-immigrant sentiment as a dependent variable and threat inputs as independent variables.
Fig 18.
Cluster plot of social conservativism (ring wing social values) and anti-immigrant sentiment.
Fig 19.
Cluster plot of social political values (both liberalism and conservativism) and anti-immigrant sentiment.
On the Y axis, the 0 point could be considered political neutrality, while negative numbers can be considered left-wing and positive numbers can be considered right-wing.
Fig 20.
Distribution of anthropomorphic promiscuity outputs from the simulation runs.
Fig 21.
Distribution of religious frequency on nationalism level from the simulated data runs.
Table 9.
Regression analysis of anti-immigrant sentiment as it results from other model variables.
Model 1 is for all data extracted from the simulation; Model 2 uses data from simulations that only resulted in low levels of nationalism; Model 3 uses only data from those simulations resulting in high levels of nationalism.
Fig 22.
Levels of antiimmigrant sentiment and threat for high nationalism simulations.
Fig 23.
Levels of antiimmigrant sentiment and threat for low nationalism simulations.
Table 10.
Anti-immigrant sentiment as dependent variable in 4 regression models where data is drawn from simulations with low sociographic prudery (model 1), high sociographic prudery (model 2), low anthropomorphic promiscuity (model 3), and high anthropomorphic promiscuity (model 4).