Computational protocol: Can you catch Ebola from a stork bite? Inductive reasoning influences generalization of perceived zoonosis risk

Similar protocols

Protocol publication

[…] Our primary goal was to test how mean intentions to report bird and mammal bites related to beliefs about mammal-to-mammal, bird-to-bird, and between bird and mammal disease transmission. To this end, we calculated subject means for each of these variables. Cronbach’s α was used to test whether responses to different mammal and bird questions were reliable within subjects, which is a prerequisite to averaging across the individual questions. To test how mean bite reporting measures were associated with mean beliefs about interspecies disease transmission, we present the associations using Kendall’s tau (τ), a non-linear correlation appropriate for ordinal data. However, the results are qualitatively identical with standard linear correlations.To test whether mean bite reporting and interspecies disease transmission varied across any of the animal species, we used linear mixed effects models, implemented in R’s nlme package []. Random effect terms included random intercepts and random species effects for participants. Omnibus F-tests were used to assess whether there was significant variance between species on any measure, which were followed by paired samples t-tests to reveal the direction of the effects. Effect sizes for the F-tests are reported as partial eta-squared, and effect sizes for the t-tests are reported as Cohen’s d.To test whether any demographic variables influenced the primary associations reported above, we ran a series of multiple linear regression models examining whether the effect of between bird and mammal disease transmission ratings on bird bite reporting or mammal-to-mammal disease transmission ratings on mammal bite reporting varied as a function of the following demographics: age, sex, pet ownership, education, and political orientation. We did not test ethnicity and sexual orientation as potential moderators because of the low number of respondents who were non-white or non-heterosexual. Likewise, because of low numbers of respondents in many categories for education, we coded education as a binary factor based on whether the respondent had received a college degree (0 = No college degree, n = 136; 1 = College degree, n = 153). For political orientation, we treated the spectrum from liberal to conservative as a linearly spaced continuous variable. […]

Pipeline specifications

Software tools lme4, nlme
Application Mathematical modeling
Organisms Homo sapiens
Diseases Communicable Diseases