| Literature DB >> 24717972 |
Abstract
Scientific research yields inconsistent and contradictory evidence relating religion to moral judgments and outcomes, yet most people on earth nonetheless view belief in God (or gods) as central to morality, and many view atheists with suspicion and scorn. To evaluate intuitions regarding a causal link between religion and morality, this paper tested intuitive moral judgments of atheists and other groups. Across five experiments (N = 1,152), American participants intuitively judged a wide variety of immoral acts (e.g., serial murder, consensual incest, necrobestiality, cannibalism) as representative of atheists, but not of eleven other religious, ethnic, and cultural groups. Even atheist participants judged immoral acts as more representative of atheists than of other groups. These findings demonstrate a prevalent intuition that belief in God serves a necessary function in inhibiting immoral conduct, and may help explain persistent negative perceptions of atheists.Entities:
Mesh:
Year: 2014 PMID: 24717972 PMCID: PMC3981659 DOI: 10.1371/journal.pone.0092302
Source DB: PubMed Journal: PLoS One ISSN: 1932-6203 Impact factor: 3.240
Figure 1Schematic summary of methods used across experiments, illustrated with the serial killer description used in Experiment 1.
Note: For the Buddhist, Hindu, and Muslim conditions, the character was called “a man” rather than “Dax.”.
Figure 2Conjunction error rates (proportion), Experiments 1–3.
A) Given a description of serial murder and animal torture, participants were significantly more likely to commit a conjunction error for the atheist target than for any of five religious targets. B) Given a description of consensual incest, participants were significantly more likely to commit a conjunction error for the atheist target than for any of five religious targets. C) Given a description of a man having sex with, then eating, a dead chicken, participants were significantly more likely to commit a conjunction error for the atheist target than for any of five ethnic targets. Error bars represent 95% confidence intervals of the mean.
Logistic regression summaries, Experiments 1–3.
| OR | Low | High |
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| All | 10.98 | 4.79 | 25.78 | 2×10−8 |
| Buddhist | 35.89 | 6.59 | 672.03 | 8×10−4 |
| Christian | 3.54 | 1.31 | 10.28 | .015 |
| Hindu | 15.11 | 4.44 | 70.48 | 7×10−5 |
| Jewish | 40.61 | 7.48 | 759.40 | 5×10−4 |
| Muslim | 8.50 | 2.43 | 40.31 | .002 |
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| All | 8.11 | 3.69 | 18.20 | 2×10−7 |
| Buddhist | 3.75 | 1.41 | 10.72 | .01 |
| Christian | 18.50 | 4.72 | 124.05 | 2×10−4 |
| Hindu | 9.33 | 2.72 | 43.78 | .001 |
| Jewish | 30.00 | 5.52 | 561.21 | .001 |
| Muslim | 5.80 | 1.96 | 19.95 | .002 |
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| All | 7.76 | 3.52 | 17.56 | 5×10−7 |
| Asian | 8.37 | 2.91 | 27.16 | 2×10−4 |
| Black | 40.71 | 7.23 | 768.59 | 6×10−4 |
| Hispanic | 7.24 | 2.50 | 23.64 | 4×10−4 |
| Nat. Am. | 5.26 | 1.92 | 15.58 | .002 |
| White | 5.82 | 2.06 | 18.01 | .001 |
For each experiment, results from six logistic regression models are presented, comparing (1) the atheist target to all targets (All), followed by (2–6) comparisons of the atheist target to each other target individually. Odds ratios, as well as upper and lower bounds of a 95% confidence interval of the odds ratio, are presented, along with p-values.
Logistic regression summaries, Experiment 4.
| Atheist | Gay | OR | Low | High |
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| 47.1 | 14.7 | 5.16 | 1.70 | 18.04 | .006 |
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| 33.3 | 0.0 | 30.30 | 3.49 | ∞ | .02 |
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| 38.5 | 14.7 | 3.63 | 1.21 | 12.48 | .03 |
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| 31.4 | 6.1 | 7.10 | 1.70 | 48.79 | .02 |
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| 43.3 | 0.0 | 57.86 | 6.99 | ∞ | .006 |
Results from five logistic regression models are presented, comparing the atheist target to the gay target for each Moral Foundation violation. The % of conjunction errors in atheist and gay conditions, odds ratios, upper and lower bounds of a 95% confidence interval of the odds ratio, and p-values are presented. Note: In the Fairness and Purity conditions, no participants committed conjunction errors with a potential gay target, rendering traditional logistic regression models impossible. Instead, bias-reduced GLM analyses were performed using the brglm package in R.