Many topics that scientists investigate speak to people’s ideological worldviews. We report three studies—including an analysis of large-scale survey data—in which we systematically investigate the ideological antecedents of general faith in science and willingness to support science, as well as of science skepticism of climate change, vaccination, and genetic modification (GM). The main predictors are religiosity and political orientation, morality, and science understanding. Overall, science understanding is associated with vaccine and GM food acceptance, but not climate change acceptance. Importantly, different ideological predictors are related to the acceptance of different scientific findings. Political conservatism best predicts climate change skepticism. Religiosity, alongside moral purity concerns, best predicts vaccination skepticism. GM food skepticism is not fueled by religious or political ideology. Finally, religious conservatives consistently display a low faith in science and an unwillingness to support science. Thus, science acceptance and rejection have different ideological roots, depending on the topic of investigation.

Pilot Study Method Participants (105 MTurk workers, 42 women; M age = 30.19, SD = 8.73) were first asked to respond to four science rejection items (Lewandowsky, Gignac, & Oberauer, 2013; Lewandowsky, Oberauer, & Gignac, 2013). Two items reflected medical facts: “The HIV virus causes AIDS” and “Smoking causes lung cancer.” The other two items were more contentious: “Human CO 2 emissions cause climate change” and “Vaccinations cause autism.” All items were scored on 7-point scales ranging from 1 (strongly disagree) to 7 (strongly agree). The first three items were reverse-scored. Participants then completed a scientific literacy test (nine true-false items, a maximum score of 9; α = .594; (B. C. Hayes & Tarick, 2000; Kahan et al., 2012; see Appendix A).5 After completing an attention check (Oppenheimer et al., 2009), participants indicated their gender, age, nationality, occupation, religious identity (“Do you consider yourself to be a religious person?”), religious affiliation (i.e., denomination), belief in God (100-point slider scale ranging from not at all to very much), and political conservatism (100-point slider scale ranging from very liberal to very conservative). Results and Discussion We used hierarchical regression analysis to assess which variables best predict science rejection. Controlling for age, gender, and profession, we entered political conservatism in Model 1, religiosity in Model 2, and scientific literacy in Model 3. For an overview of the means (SD), correlations, and regression tables, please see Appendix B. The results yielded a number of initial insights. Although all four science rejection items were statistically related to scientific literacy, they differed in important ways in terms of how well they were predicted by religious and political ideology. The publicly accepted medical facts that HIV causes AIDS and smoking causes lung cancer were not ideologically fueled. Rejection of anthropogenic climate change was best predicted by political conservatism (and scientific literacy), but not by religion (Model 3 explained 20% of the variance), F(6, 97) = 4.67, p < .001. In contrast, vaccine skepticism was clearly grounded in religious belief. Scientific literacy however was the strongest predictor of vaccine skepticism, which together with religiosity accounted for 47% of the explained variance, F(6, 97) = 14.08, p < .001. Political conservatism was a weaker predictor6 of vaccine skepticism. This suggests that these two prominent forms of science rejection have different ideological antecedents.

Study 2 Having established that different forms of science acceptance and rejection have different antecedents, we next sought to test whether this pattern of results generalizes beyond the MTurk population. To do so, we used data from the 2010 wave of the ISSP (Environment III) conducted in the United States (ISSP Research Group, 2012) to conceptually replicate the results obtained thus far by using a representative sample of the U.S. population. We identified measures of climate change skepticism, faith in science, as well as political conservatism and religiosity. In addition, the dataset contained a GM food skepticism measure. There were no items on vaccine skepticism, morality, and scientific literacy. Method We downloaded the dataset of the ZA5500: International Social Survey Programme: Environment III–ISSP 2010 at https://dbk.gesis.org/dbksearch/sdesc2.asp?ll=10%C2%ACabs=&af=&nf=&search=&search2=&db=e&no=5500. The U.S. data (N = 1,430; M age = 48.08, SD age = 17.81; 823 women) was collected early 2010 by the National Opinion Research Center (NORC–General Social Survey). There were no data exclusions.12 The following variables were identified as relevant to the purpose of the current research: Science skepticism Two items were identified as proxies of climate change and GM food skepticism, respectively: “In general, do you think that a rise in the world’s temperature caused by climate change is . . . ” and “And do you think that modifying the genes of certain crops is . . . ” Both items had an answer scale ranging from 1 (extremely dangerous for the environment) to 5 (not dangerous at all for the environment), and including a “can’t choose” option. Responses to the GM food item were reverse-scored as to signal skepticism. Faith in science Two items (r = .37) in the dataset were identified as measuring faith in science: “We believe too often in science, and not enough in feelings and faith” and “Overall, modern science does more harm than good.” Respondents were asked to indicate their agreement on a scale ranging from 1 (agree strongly) to 5 (disagree strongly). There was also a “can’t choose” option. Religion, political ideology, demographic variables In addition to respondents’ age and gender, political preference was measured on a 5-point scale consisting of the following response options: 1 (far left), 2 (left/center left), 3 (center/liberal), 4 (right/conservative), and 5 (far right). In total, 500 respondents identified as left/center left, 561 as center/liberal, and 322 as right/conservative. A total of 35 respondents ticked other/no specification. Religious denomination was measured (including “no religion”). As no measure of religious orthodoxy was included, we looked at frequency of religious service attendance as a proxy. Attendance frequency was measured with the item “How often do you attend religious services,” with the following response options: 1 (several times a week or more), 2 (once a week), 3 (2 or 3 times a month), 4 (once a month), 5 (several times a year), 6 (once a year), 7 (less frequently than once a year), and 8 (never). Mean response was 4.67 (SD = 2.46). Results Zero-order correlations can be found in Table 4. As in Study 1, we used hierarchical regression analyses to assess which variables best predict faith in science and science skepticism. We included general demographic variables and added political ideology in Model 2, religious denomination (dichotomized to no religion vs. religion) and religious attendance in Model 3, and faith in science in Model 4 (except in the regression analysis of faith in science) which is depicted in Table 5 (see Appendix C, Tables C5-C7 for the complete regression analysis). Table 4. Correlations Matrix, Study 2. View larger version Table 5. Final Models of Hierarchical Regression Analyses of Faith in Science, Climate Change Skepticism, and GM Skepticism, Study 2. View larger version Climate change Model 3 explained 9.2% of the variance, F(3, 1297) = 44.80, p < .001. Political conservatism (alongside age) was a significant predictor of climate change skepticism, Beta = .27, p < .001, 95% CI [0.28, 0.42]. Adding religious denomination, religious attendance frequency, and faith in science did not lead to meaningful increases in explained variance. GM food Model 4 explained 6.6% of the variance, F(6, 1162) = 14.70, p < .001. Political conservatism weakly contributed to the explained variance (with conservatives being slightly less skeptical), while religious denomination and religious attendance frequency did not predict GM food skepticism. Faith in science (alongside small effects of gender and age) was a significant predictor, Beta = −.19, p < .001, 95% CI [−0.15, −0.29]. Faith in science Model 3 explained 9.6% of the variance, F(5, 1383) = 30.23, p < .001. As can be seen in Table 5, the only significant predictors (alongside small effects of gender and age) were religious denomination (Beta = −.16, p < .001, 95% CI [−0.50, −0.24]) and religious attendance frequency, Beta = .19, p < .001, 95% CI [0.05, 0.09] (note that lower scores indicate higher attendance rates). Discussion Although the ISSP dataset did not include all measures of interest to the current project, with the data available, we were able to conceptually replicate the findings of Study 1 among a large, representative, sample of the U.S. general population. Again, climate change skepticism was found to be primarily political, with religiosity playing no meaningful role. Moreover, faith in science was best predicted by religious orthodoxy, using a measure gauging frequency of religious attendance. Finally, we also found that GM food skepticism was best predicted by faith in science, and not religious or political ideology (political conservatism had a small negative effect).

Coda A recent editorial in the prestigious science journal Nature (Nature Editorial, 2017) argued for a more nuanced view on modern anti-science sentiments, given that science is not a single entity that people are either for or against. Speaking to this view, the current article extends the statement that “science does not speak with a single voice” (p. 134) to science skepticism, which—like science itself—is a more heterogeneous phenomenon than previously assumed.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Notes 1.

In the Lewandowsky, Gignac, and Oberauer (2013) article, political conservatism even had a weak opposite effect on acceptance of vaccinations. 2.

Participants in the pilot study were paid 0.40 dollars for participation; participants in Study 1 and 3 where paid 0.50 dollars for participation. 3.

Studies 1 and 3 also included two items measuring agreement with publicly accepted medical facts (HIV-AIDS and smoking-lung cancer). We added those to the climate change and vaccination items to ascertain that ideology did not simply make people more skeptical about facts in general. 4.

Removing one item (“It’s the father’s gene that decides whether the baby is a boy or a girl”) increased reliability to α = .64. We report analyses using all items in the scale; using the scale without the aforementioned item did not change the pattern of results. 5.

Participants also completed two stereotypes about scientists’ measures: An intuitive moral stereotype measure (i.e., conjunction fallacy measure) and an explicit measure designed to tap into moral stereotypes of scientists (e.g., “A scientist prefers knowledge acquisition over preventing harm”; α = .62; Rutjens & Heine, 2016). 6.

Note that in Studies 1 and 3, political conservatism did not predict vaccine skepticism. 7.

We included the scientific literacy test again in Study 3. 8.

To limit the number of predictors in the hierarchical regression, we only report general political conservatism in the results below, and not the additional social and economic conservatism measures as these did not explain unique variance over and beyond political conservatism. For the same reason, we do not report belief in God in the regression analyses below; note that orthodoxy correlated more strongly with the variables of interest. Moreover, we were mainly interested in teasing apart religious identity and religious orthodoxy. In none of the regression analyses did belief in God contribute to the variance over and beyond religious orthodoxy and religious identity. 9.

Note that in the pilot study, the only variable that predicted the medical facts was scientific literacy, which we did not include in the current study. 10.

Male participants ranked science higher than female participants and indicated more faith in science. This gender difference remained when controlling for religious orthodoxy and any of the other measures. 11.

Gender also predicted science support: Women ranked science lower than men, and men had more faith in science than women. One possible reason for this finding could be that science is implicitly associated with men more so than with women (Nosek et al., 2009); this gender stereotype might lead men to assign more priority to science. However, it is important to note that female participants were more orthodox in this sample, and that orthodoxy actually explained this difference. 12.

Note that we treated “can’t choose” answers as missing data in the analyses, which accounts for the differences in the degrees of freedom reported in the “Results” section. 13.

The only difference being that we changed the target year from 2016 to 2017. 14.

The effect of gender on genetic modification (GM) food skepticism was significant, p < .001. Women also had significantly less faith in science (which was the strongest predictor of GM food skepticism), p = .027. 15.

We thank an anonymous reviewer for pointing this out. 16.

Note that we only include the Explicit Stereotypes scale in the regression analyses, because the intuitive measure did not correlate with any of the variables in the study, see Table 1.

Supplemental Material

Supplementary material is available online with this article.