Knowledge as a driver of public perceptions about climate ... · Knowledge as a driver of public...

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Knowledge as a driver of public perceptions about climate change reassessed

1. Method and measures

1.1 Sample

In the cross-country study, the age of the participants ranged between 20 and 79 years,

with approximately 20% of the participants (n = 80) belonging to each age group among the

surveyed countries. Participants’ educational level was generally higher than the average,

with approximately 30% of respondents having received university degrees in Canada,

Germany, Switzerland, and the United Kingdom. Participants from China and the United

States possessing university degrees accounted for 57% and 37% of the samples, respectively

(Table S1). In the additional Swiss study, the sample was similar with the cross-country

study. About 20% of the participants were in each age group. 8% (n = 26) of the respondents

fell into the “low education” category and 22% (n = 74) people had a university degree (“high

education”).

Table S1. Demographic characteristics of participants in the six different countries:

Frequencies and percentages are shown (gender and age were quota variables).

Canada (N = 426)

China (N = 420)

Germany (N = 421)

Switzerland (N = 404)

UK (N = 401)

US (N = 423)

n % n % n % n % n % n %

Gender

male 205 48% 211 50% 208 49% 194 48% 197 49% 201 48% female 221 52% 209 50% 213 51% 210 52% 204 51% 222 53%

Age

20-29 68 16% 86 21% 83 20% 77 19% 69 17% 63 15% 30-39 77 18% 82 20% 77 18% 75 19% 74 19% 75 18% 40-49 95 22% 79 19% 86 20% 78 19% 79 20% 93 22% 50-59 89 21% 91 22% 85 20% 87 22% 85 21% 91 22% 60+ 97 23% 82 20% 90 21% 87 22% 94 23% 101 24%

Education#

low 17 4% 24 6% 134 32% 34 8% 15 4% 19 5% middle 294 69% 127 30% 153 36% 253 63% 253 63% 248 59% high 115 27% 238 57% 123 29% 106 26% 111 28% 156 37% Notes. Totals of percentages are not 100 for every demographic characteristic and country because of rounding and missing responses. # Low education indicates the highest degree participants received is below high school (including no education, primary school, secondary school, middle school, and some high school in different countries). Middle education indicates the participants have received the degree of high school or higher but not yet a university degree (including high school, vocational school, college, and some university). High education indicates the participants have received a university degree or higher (including Bachelor, Master, and PhD).

Knowledge as a driver of public perceptions about climate change reassessed

© 2016 Macmillan Publishers Limited. All rights reserved.

http://dx.doi.org/10.1038/nclimate2997

2 NATURE CLIMATE CHANGE | www.nature.com/natureclimatechange

SUPPLEMENTARY INFORMATION DOI: 10.1038/NCLIMATE2997

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1.2 Knowledge scale

Our objective knowledge scale consisted of 12 items used in previous studies1,2 and had

three subscales representing three different forms of knowledge: knowledge about physical

characteristics assessed fundamental knowledge about the physics underlying climate change,

knowledge about the causes of climate change measured the reasons that climate change is

happening, and knowledge about the consequences of climate change included items about

the different natural hazards and environmental effects of climate change. Other authors

discussed the relevance of procedural or action-related knowledge to explain ecological

behaviors3. Because procedural knowledge was not expected to explain people’s perception

of environmental issues1,2, this was not investigated in the current research.

Respondents answered each item with “true,” “false” or “don’t know.” The latter

response option was provided to avoid guesses from the participants. The wordings of the

knowledge items were carefully selected in order to avoid indicating the correctness of the

statements. Respondents’ answers on the knowledge scale were first reversed for knowledge

items with incorrect statements. Hence, the results indicated whether the answers were

correct, wrong, or whether the respondent did not know the answer. “Don’t know” answers

were recoded as wrong answers thereby allowing for an analysis of binary data (1 = “correct”;

0 = “wrong” and “don’t know”).

In the additional survey in Switzerland, we also included a self-assessed knowledge

measure. Participants were asked to rate their level of knowledge about climate change on a

6-point scale ranging between “very low knowledge” and “very high knowledge”.

1.3 Value orientations and cultural worldviews

We adopted three general value orientations in the present paper from De Groot and

Steg4: 1) egoistic values reveal people’s desire for personal interests and advancements; 2)

altruistic values explore to what extent do participants care about the welfare of others; and 3)

biospheric values indicate whether people concern about the environment and about

supporting living organisms. The value items were assessed on a nine-point response scales,



NATURE CLIMATE CHANGE | www.nature.com/natureclimatechange 3


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ranging from -1 (“opposed to my values”), 0 (“not important“) to 7 (“extremely important”).

The reliability coefficients suggested three reliable scales (Cronbach’s αs ≥ 0.84, Table S2).

Table S2. Value orientations: The items, their means, standard deviations, corrected item-

total correlations, as well as internal reliabilities, means and standard deviations of the scales

of the cross-country study.

Items per scale M SD Corrected item-total correlation

Egoism (Cronbach’s α = 0.84, M = 2.71, SD = 1.81)

Social power: control over others, dominance. 1.73 2.40 0.66

Wealth: material possessions, money. 3.06 2.07 0.63

Authority: the right to lead or command. 2.68 2.17 0.78

Influence: having an impact on people and events. 3.38 2.13 0.65

Altruism (Cronbach’s α = 0.90, M = 5.63, SD = 1.41)

Equality: equal opportunity for all. 5.52 1.67 0.75

A world at peace: free of war and conflict. 5.87 1.56 0.76

Social justice: correcting injustice, care for the weak.

5.63 1.62 0.84

Helpful: working for the welfare of others. 5.49 1.59 0.76

Biospherism (Cronbach’s α = 0.95, M = 5.62, SD = 1.48)

Preventing pollution: protecting natural resources. 5.57 1.59 0.86

Respecting the earth: harmony with other species. 5.67 1.58 0.91

Unity with nature: fitting into nature. 5.50 1.65 0.88

Protecting the environment: preserving nature. 5.75 1.53 0.89

In the additional study in Switzerland, we also measured participants’ cultural

worldviews by adopting the 12-item scale from the study of Kahan et al.5 (egoism-

communitarianism, hierarchy-egalitarianism) and slightly changed the items to better fit the

Swiss society2. All items were assessed on six-point Likert scales ranging from 1 (“strongly

disagree”) to 6 (“strongly agree”). The internal consistency of these two subscales was fairly

good, with Cronbach’s α = 0.77, N = 6 for the individualism dimension and Cronbach’s α =

0.68, N = 6 for the hierarchy dimension.





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1.4 Concern about climate change

We studied people’s concern about climate change using a scale consisting of four items

taken from previous research in the cross-country study1,2 (see Table S3). Participants rated

their concern about climate change on these items on 6-point Likert scales, with higher scores

indicating more concern about climate change. The internal reliability of the scale was high,

Cronbach’s α = 0.94.

Table S3. Climate change concern: The items, their means, standard deviations, and

corrected item-total correlations in the cross-country study.

Items M SD Corrected item-total correlation

I worry that the state of climate is changing. 4.51 1.44 0.81

Climate change has severe consequences for humans and nature.

4.94 1.28 0.85

Climate protection is important for our future. 5.07 1.28 0.88

We must protect the climate’s equilibrium. 5.01 1.29 0.88

Scale’s Cronbach’s α = 0.94, M = 4.88, SD = 1.21

Likewise, we assessed people’s concern about climate change using the same approach

in the additional Swiss study (see Table S4). The internal reliability suggested a sufficiently

good scalability factor with Cronbach’s α = 0.90.





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Table S4. Climate change concern: The items, their means, standard deviations, and

corrected item-total correlations in the additional Swiss study.

Items M SD Corrected item-total correlation

I worry that the state of climate is changing. 4.42 1.31 0.75

Climate change has severe consequences for humans and nature.

4.89 1.17 0.77

Climate protection is important for our future. 5.12 1.15 0.79

We must protect the climate’s equilibrium. 5.10 1.12 0.82

Scale’s Cronbach’s α = 0.90, M = 4.88, SD = 1.04

2. Data analysis

Mokken scale analysis was conducted to test the quality of our knowledge scales. This

method is subject to stricter assumptions than reliability analyses based on Cronbach’s alpha6;

instead of assuming that all items have the same frequency distribution, a Mokken scale

analysis explicitly takes into account (1) the latent knowledge of respondents about the

subject of interest (i.e., climate change) and (2) key characteristics of the survey items

(question difficulty on our case). Not only can a Mokken scale analysis rank participants

according to their probability of positive responses (i.e., based on latent traits such as

knowledge), it can also scale items based on their probability of being answered correctly.

One of the important assumptions of a Mokken scale analysis is double monotonicity.

Monotonicity firstly means that the expected order of respondents should be monotonically

non-decreasing for each item. That is, the items that are responded to correctly by persons

with little knowledge should also be answered correctly by persons with more knowledge.

Secondly, the items should be monotonically ordered for each person: people who are able to

correctly answer the difficult questions should also respond correctly to the less difficult and

easier items.





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In a Mokken scale analysis, scalability is measured by Loevinger's coefficient H for the

entire scale and Hi for each item i. The Loevinger scalability coefficient H indicates the extent

to which respondents can be accurately ordered by the suggested set of items, while

scalability coefficient Hi indicates the degree to which each item could be accurately ordered

by the participants. Hi and H will take values between 0 and 1, and the larger the H and Hi

values, the higher is the confidence in this ordering. A scale with a set of items with H values

between 0.3 and 0.4 is considered to be an acceptable scale. H = 0.4–0.5 indicates medium

scalability, and H = 0.5–1 suggests a strong scale. Concerning the individual items, the

scalability coefficients Hi for each individual item should be Hi ≥ 0.3.

The items kept in the knowledge scale were proved to be of good quality through the

Mokken scale analysis both in the cross-country study (see Fig. 1) and the additional study.

After conducting the Mokken scale analysis, we summed the number of correct responses for

each respondent, for each of the three knowledge scales, and calculated the proportion of

correct responses for each type of knowledge to examine what kind of knowledge was more

present and which was less present in our participants. The three sum scores of the knowledge

scales were included in the regression analyses described below.

Further, in order to investigate the additional effects of knowledge on public concern

about climate change after controlling for demographics and value orientations, we conducted

hierarchical linear regression analyses with public concern as dependent variable in the cross-

country study. In Model 1, demographics, and value orientations were included as

independent variables. In Model 2, the three types of knowledge were added as independent

variables. The analysis was done for each country separately. In the main paper, we only

reported the total effects of these predictors (Model 2 only) on concern about climate change

for each different country (Table I). Here, we also present the results of the hierarchical

regression analyses (Table S7).

In order to examine the country differences regarding the three types of knowledge

about climate change, a multivariate ANOVA was conducted7. We included education level

as covariate, country as independent variable (IV), and knowledge about physical





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characteristics, knowledge about causes and knowledge about consequences as dependent

variables (DVs). Likewise, using MANCOVA, we examined countries that differed in terms

of the value orientations with egoistic value, altruistic value, and biospheric value as

dependent variables and country as independent variable, and education level as covariate. In

addition, a one-way ANCOVA was conducted to illustrate the country differences on the

concern about climate change, with education level as covariate.

In the additional study in Switzerland, we conducted hierarchical linear regression

analysis to examine the impacts of cultural worldviews [Model 3(a)] and self-assessed

knowledge [Model 3(b)]. With the concern about climate change as dependent variable, we

included cultural worldviews in Model 3(a) and self-assessed knowledge in Model 3(b) in

addition to Model 2 with demographics, value orientations, and objectively measured

knowledge.

3. Additional results

3.1 Different types of knowledge

According to the results in Fig. 1, our respondents seemed to be reasonably well-

informed about climate change related questions. Specifically, in the cross-country study,

within the full sample of 2,495 people, average correct response constituted 49.2% for

knowledge about the physical characteristics, 59.7% for knowledge about the causes and

70.4% for knowledge about the consequences of climate change.

The scale with four items about the physical aspects of climate change was acceptable,

H = 0.41, Hi ≥ 0.27 and ρ = 0.57 (Fig. 1). Respondents appeared least knowledgeable on this

dimension: more than half of the participants managed to respond correctly about how CO2 is

produced (item 1) and that CO2 is not harmful for plants (item 2), which accounted for 80%

and 56% correct responses, respectively. By contrast, few people knew about the CO2

emissions during the operation of nuclear power plants (item 3, 31% correct) and about the

comparison of environmental impact between CO2 and methane (item 4, 29% correct).

In addition, the four items that intended to assess respondents’ knowledge regarding





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climate change causes formed a satisfactory one-dimensional scale, H = 0.42, Hi ≥ 0.38, and ρ

= 0.56 (Fig. 1). Most participants knew about the CO2 increase in the atmosphere (item 5,

83% correct responses). Also, the items about the facts that human activities are responsible

for climate change and temperature increases in the past centuries were well known (item 6:

75% correct on average and item 7: 64% correct responses). However, only 18% of the

respondents knew about the changes in CO2 concentrations during the past centuries (item 8).

The four items about the consequences of climate change formed a reliable one-

dimensional scale with a Loevinger scalability coefficient of H = 0.41, the individual

scalability coefficients where Hi ≥ 0.37, and a reliability coefficient of ρ = 0.61 (Fig. 1).

Respondents were best informed about the consequences of climate change. Most participants

knew about the rising see level (Fig. 1, item 9) and the increase of extreme weather events

(item 10), which accounted for 90% and 89% correct responses, respectively. By contrast,

participants seemed to be less knowledgeable about the expected pattern in climate change

(item 11) and precipitation change (item 12), which made up 52% and 51% correct responses

respectively.

In the additional Swiss study, the Loevinger scalability coefficients (H) and reliabilities

(ρ) for the three scales of knowledge about climate change were H = 0.43, ρ = 0.57 for

knowledge about physical characteristics; H = 0.48, ρ = 0.62 for knowledge about causes; and

H = 0.50, ρ = 0.62 for knowledge about consequences. The individual scalability coefficients

(Hi) for each item were above 0.40 in each knowledge scale. Consistent with the results of the

cross-country study, the proportions of correct responses for knowledge about the physical

characteristics, about the causes, and about the consequences of climate change were 50%,

59% and 75%, respectively. Participants tended to report their own knowledge level about

climate change as moderate on a scale from 1 to 6 (M = 3.53, SD = 1.11).

3.2 Differences among countries regarding different types of knowledge

We examined the differences between the six countries for the three types of knowledge

(Table S5). The MANOVA results indicated significant differences among countries on





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knowledge about physical characteristics, causes, and consequences of climate change (see

Table S5). To be more specific, respondents from Germany and Switzerland had significantly

higher scores on knowledge about physical aspects of climate change than participants from

Canada and the US. Chinese respondents knew significantly more about the causes of climate

change than the respondents from the other countries. German and Swiss respondents were

most knowledgeable about the consequences of climate change. In contrast, participants from

the US had the lowest level of knowledge about climate change among the six countries we

surveyed, independent of the type of knowledge. The effect size (η2) suggested that the

sample size used in this study was sufficient to detect the significance (at p < 0.05) of country

differences about climate change knowledge (Table S5)8.

Table S5. Knowledge about physical characteristics, causes, and consequences of climate

change: Means and 95% confidence intervals per country.

Physical knowledge Causes knowledge Consequences knowledge

M 95%CI M 95%CI M 95%CI Canada 0.45b [0.42, 0.48] 0.60b [0.57, 0.62] 0.67b [0.64, 0.69] China 0.50a,b [0.47, 0.53] 0.68a [0.65, 0.71] 0.69b [0.66, 0.71] Germany 0.55a [0.52, 0.57] 0.62b [0.59, 0.64] 0.79a [0.76, 0.81] Switzerland 0.53a [0.50, 0.56] 0.61b [0.58, 0.63] 0.77a [0.75, 0.80] UK 0.49a,b [0.46, 0.52] 0.58b [0.55, 0.61] 0.71b [0.69, 0.74] US 0.45b [0.43, 0.48] 0.52c [0.49, 0.54] 0.61c [0.59, 0.64]

F(df1, df2) 7.59***(5, 2413) 16.64*** (5, 2413) 26.22*** (5, 2413)

η2 0.02 0.03 0.05 Notes. Means with different superscripts within a column are significantly different from each other at p < 0.05 (after Bonferroni correction). *** p < 0.001. Education level was included as a covariate.

3.3 Differences between countries regarding value orientations

In addition, differences between countries regarding the three value orientations were

investigated (Table S6). Results of the MANOVA suggested significant differences among

countries on egoism, altruism, and biospherism (see Table S6). More exactly, the participants

from China had significantly higher scores on egoism than the ones from other countries.





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They were followed by people from Canada, the UK, and the US while German and Swiss

participants appeared to have least interest in self-importance and self-benefits.

Chinese and Swiss respondents were found to be most altruistic in our research.

Respondents from the United States, by contrast, found it least important to consider other

people when making decisions. Likewise, respondents from China and Switzerland reported

significantly higher biospheric values than the respondents from other countries.

Table S6. Egoism, altruism, and biospherism: Means and 95% confidence intervals per

country.

Egoism value Altruism value Biospherism value

M 95%CI M 95%CI M 95%CI

Canada 2.57b [2.41, 2.73] 5.59a,b [5.46, 5.72] 5.53b [5.39, 5.67]

China 4.24a [4.07, 4.41] 5.76a [5.62, 5.91] 6.04a [5.89, 6.19]

Germany 2.10c [1.94, 2.26] 5.66a,b [5.52, 5.80] 5.55b [5.40, 5.69]

Switzerland 2.23b,c [2.07, 2.39] 5.73a [5.59, 5.87] 5.88a [5.73, 6.02]

UK 2.66b [2.49, 2.83] 5.66a,b [5.52, 5.80] 5.51b [5.37, 5.66]

US 2.54b [2.38, 2.70] 5.40b [5.26, 5.53] 5.28b [5.14, 5.42]

F (df1, df2) 81.80*** (5, 2413) 3.59** (5, 2413) 14.17*** (5, 2413)

η2 0.15 0.01 0.03 Notes. Means with different superscripts within a column are significantly different from each other at p < 0.05 (after Bonferroni correction). *** p < 0.001. Education level was included as a covariate.

3.4 Differences between countries regarding concern about climate change

A one-way ANCOVA revealed significant differences between countries about the

concern about climate change. As shown in Table S7, European respondents appeared to be

rather concern about climate change and Chinese participants were substantially more

concerned about climate change than people from the other countries. Participants from the

US seemed to be the least worried about climate change. The results seem to be in line with

previous studies9-11.





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Table S7. Concern about climate change: Means and 95% confidence intervals per country.

Concern about climate change

M 95%CI

Canada 4.84b [4.72, 4.95]

China 5.37a [5.25, 5.49]

Germany 4.90b [4.78, 5.01]

Switzerland 4.95b [4.83, 5.06]

UK 4.78b [4.66, 4.90]

US 4.51c [4.39, 4.62]

F (df1, df2) 22.37*** (5, 2413)

η2 0.04 Notes. Means with different superscripts within a column are significantly different from each other at p < 0.05 (after Bonferroni correction). *** p < 0.001. Education level was included as a covariate.

3.5 Hierarchical regression analyses on concern about climate change

According to the results of the hierarchical regression analyses of the cross-country

study, the introduction of demographics and value orientations in Model 1 was significant and

explained a great part of the variance (17%–34% explained variances) in concern about

climate change across the countries (Table S8). The inclusion of knowledge about physical

characteristics, causes, and consequences of climate change (Model 2) further increased the

explained variance of concern about climate change across the countries. That is, including

the three types of knowledge about climate change significantly improved the explained

variance with 2% to 18% (Table S8). In China, different types of knowledge about climate

change only marginally improved the regression model with 2%, Fchange(3, 378) = 2.55, p =

0.056. A possible explanation for this finding may be that the Chinese participants in general

had a higher level of knowledge about the causes of climate change than people from the

other countries (Table S4), the inclusion of the three types of knowledge therefore did not

additionally improve their concern about climate change. In all six countries, the correlations





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between the three types of knowledge were small (r < 0.4) and variance inflation factors

(VIF) were slightly bigger than 1 (1.10 < VIF < 1.47), so that multicollinearity was not a

concern7,12.

Table S8. Hierarchical regression analyses predicting public concern about climate change in

the cross-country survey.

Models and predictors per country R

2 ΔR

2 F (df1, df2) ΔF (df1, df2)

Canada

Model 1(without knowledge as predictor)

0.25 20.35*** (7, 418)

Model 2 (with knowledge as predictor)

0.35 0.10 22.55*** (10, 415) 20.91*** (3, 415)

China

Model 1 0.20 13.54*** (7, 381)

Model 2 0.22 0.02 10.35*** (10, 378) 2.55 (3, 378)

Germany

Model 1 0.21 15.06*** (7, 402)

Model 2 0.30 0.09 17.07*** (10, 399) 17. 45*** (3, 399)

Switzerland

Model 1 0.17 11.22*** (7, 385)

Model 2 0.35 0.18 20.42*** (10, 382) 34.94*** (3, 382)

UK

Model 1 0.34 27.72*** (7, 371)

Model 2 0.46 0.11 30.75*** (10, 368) 25.18*** (3, 368)

US

Model 1 0.29 24.11*** (7, 415)

Model 2 0.43 0.14 31.05*** (10, 412) 33.88*** (3, 412)

Notes. Model 1 included gender, age, dummy variables for low education vs. high education and for middle education vs. high education, egoistic values, altruistic values, and biospheric values; Model 2 additionally included knowledge about physical characteristics, causes, and consequences of climate change; *** p < 0.001.

In the additional Swiss study, the regression analysis with concern about climate change

as dependent variable, the introduction of cultural worldviews, in addition to demographics,

value orientations and three types of knowledge, slightly but significantly increased the





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explained variance of the dependent variable with 6.9%. Individualism was negatively related

to concern about climate change while hierarchy had no significant relation (see Model 3(a)

in Table S9). Similarly, we included self-assessed knowledge after the demographics, value

orientations, and three types of knowledge. No significant impact of subjective measured

knowledge was found on concern about climate change (see Model 3(b) in Table S9).

Additionally, the main results remained the same after including cultural worldviews and

subjective measured knowledge at the same time (see Model 4 in Table S9).

In short, including cultural worldviews somewhat improved the model fit and therefore

seem to have a unique relation with concern about climate change, which is independent of

the relation between value orientations and concern about climate change. The additional

explained variance of cultural worldviews to the model is however not as large as that of the

value orientations and knowledge together (R2 = 0.35). It is thus likely that there is some

overlap between cultural worldviews and value orientations. Nevertheless, an interesting

question to address in a future study would be whether cultural worldviews are more

important in explaining public concern about climate change in some countries than in other

countries.





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Table S9. Hierarchical regression analysis predicting public concern about climate change in

the additional Swiss survey.

Model 2 Model 3(a) Model 3(b) Model 4

B 95%CI B 95%CI B 95%CI B 95%CI

Gender# -0.26* [-0.46, -0.06] -0.22* [-0.42, -0.03] -0.26* [-0.46, -0.05] -0.22* [-0.42, -0.03]

Age 0.00 [-0.01, 0.01] 0.00 [0.00, 0.01] 0.00 [-0.01, 0.01] 0.00 [-0.01, 0.01]

Low education Ϯ

0.06 [-0.33, 0.46] 0.26 [-0.12, 0.64] 0.06 [-0.34, 0.46] 0.26 [-0.12, 0.64]

Middle education Ϯ

0.08 [-0.15, 0.31] 0.17 [-0.05, 0.39] 0.08 [-0.15, 0.31] 0.17 [-0.05, 0.39]

Egoistic values

0.02 [-0.04, 0.09] 0.00 [-0.06, 0.07] 0.02 [-0.04, 0.09] 0.01 [-0.06, 0.07]

Altruistic values

-0.01 [-0.11, 0.09] -0.02 [-0.12, 0.09] -0.01 [-0.11, 0.09] -0.02 [-0.12, 0.09]

Biospheric values

0.30*** [0.20, 0.40] 0.27*** [0.17, 0.36] 0.30*** [0.20, 0.40] 0.27*** [0.17, 0.36]

Physical knowledge

-0.08 [-0.44, 0.29] -0.05 [-0.40, 0.30] -0.08 [-0.45, 0.30] -0.05 [-0.41, 0.30]

Causes knowledge

1.00*** [0.64, 1.37] 0.84*** [0.49, 1.19] 1.00*** [0.64, 1.37] 0.84*** [0.49, 1.19]

Result knowledge

0.35 [-0.06, 0.75] 0.36 [-0.03, 0.75] 0.35 [-0.07, 0.76] 0.36 [-0.03, 0.75]

Individualism -0.30*** [-0.40, -0.20] -0.30*** [-0.40, -0.20]

Hierarchy -0.03 [-0.15, 0.10] -0.03 [-0.16, 0.10]

Self-assessed knowledge

0.00 [-0.10, 0.10] 0.00 [-0.09, 0.09]

R2 0.35 0.42 0.35 0.42

F(df1, df2) 17.40*** (10, 324) 19.28*** (12, 322) 15.77*** (11, 323) 17.74*** (13, 321)

△F(df1, df2) N.A. 19.02*** (2, 322) 0.00 (1, 323) 12.64*** (3, 321)

Notes. # Gender: 0 = female, 1 = male. Ϯ Dummy variable with high education as reference group. * p < 0.05, ** p < 0.01, *** p < 0.001.





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