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Objectivity Data were collected either by trained interviewers or self-completion. For both, standardized formats and written instructions ensured the objectivity of the application. The written instructions, the ordered and labeled categories, the coding scheme for missing/ambiguous responses, the scoring procedure, and the country-specific statistics provided in the Appendix ensure the objectivity of the scoring and interpretation. Reliability We estimated Cronbach’s alpha as well as McDonald’s omega as indicators of internal consistency (Cronbach, 1951; McDonald, 1999). We did this only for the 12 countries, where the one-factor model with one residual covariance provided an acceptable fit. Values for omega varied between ω = .67–.78 and alpha varied between α = .67–.77 (Denmark and Austria). Table 4 shows the exact country-specific values. Table 4 Country-Specific values for Cronbach’s Alpha and McDonald’s Omega Country Omega Alpha Austria .78 .77 Croatia .72 .74 Denmark .67 .67 Finland .75 .76 France .69 .69 Germany .69 .70 Iceland .67 .68 Japan .70 .70 Lithuania .70 .71 Sweden .72 .71 Switzerland .71 .72 USA .75 .75 Validity Content Validity The definition of environmental efficacy has two parts (Bandura, 1977, 1982). The first part refers to whether people believe that they can engage in pro-environmental behavior. These beliefs are covered by items 1, 2, and 6. The second part refers to whether people believe that their behavior and the environment are mutually influencing each other. These beliefs are captured by items 3, 4, 5, and 7. Thus, the items of the environmental efficacy scale cover the content of the definition of environmental efficacy. Factorial Validity We tested factorial validity above (see Tables 2 and 3). For the remaining 12 countries, the model fit was acceptable and the standardized loadings sufficiently high. Therefore, we can assume factorial validity for these 12 countries. Nomological network We examined convergent, divergent, and criterion-related validity using manifest items and scale scores. We estimated product-moment correlations between the environmental efficacy scale and several other constructs for each of the remaining 12 countries (N = 19,164). In addition, we used meta-analytic techniques (random effects model with DerSimonian-Laird estimator; DerSimonian & Laird, 1986) to estimate the mean correlation across countries. The validation constructs were age, gender, left-right scale, nationalism, institutional trust (trust in media, science, parliament, industry), environmental concerns, cause of climate change (natural vs. man-made), impact of climate change (for world and country), environmental attitudes, willingness to change (act pro-environment despite higher costs), consumption (recycling and reduce consumption), appreciation of nature, as well as political behavior in the form of signing a petition, donating money, and participating in a demonstration. We report the items, their scale, and their labels in the ISSP data set in the Appendix. Table 5 shows the country-specific correlations of the environmental-efficacy scale, as well as the mean effect and its 95%-confidence interval (CI). Overall, the environmental efficacy scale showed high associations with environmental knowledge, the attitudes, and behaviors, which is consistent with previous studies (Bradley et al., 2020; ElHaffar et al., 2020; Hamann & Reese, 2020; Milfont, 2012; Miller et al., 2022; Sheeran & Webb, 2016; Steffen et al., 2015; Yuriev et al., 2020). Furthermore, the lower correlations with socio-demographics also align with previous studies (Bradley et al., 2020; Milfont, 2012). Thus, the correlations found indicate good convergent, divergent, and criterion-related validity. Table 5 Country-Specific and Mean Correlates of the Environmental Efficacy Scale Variable Austria Croatia Denmark Finland France Germany Iceland Japan Lithuania Sweden Switzerland USA mean effect [CI] Age .04 -.08 .02 .03 -.04 -.02 .00 .11 -.13 -.01 .02 .08 .00 [-.03, .04] Gender .13 .15 .16 .25 .10 .13 .19 .01 .02 .20 .13 .07 .13 [.09, .16] left-right scale -.03 -.01 -.01 -.06 .00 .01 .02 .12 -.07 .04 -.01 -.02 .00 [-.03, .03] nationalism .13 .13 .25 .34 .24 .30 .27 .15 .15 .31 .24 .39 .24 [.20, .29] institutional trust .12 .12 .13 .30 .11 .22 .21 .14 .24 .18 .08 .31 .18 [.13, .23] environmental concern .51 .38 .46 .59 .54 .51 .52 .36 .48 .52 .51 .58 .50 [.46, .53] cause of climate change .36 .19 .37 .48 .32 .36 .37 .26 .29 .39 .37 .48 .35 [.31, .4] impact of climate change -.46 -.16 -.27 -.42 -.37 -.38 -.39 -.25 -.16 -.42 -.39 -.50 -.35 [-.41, -.29] environmental attitudes .41 .30 .42 .37 .47 .39 .47 .43 .34 .49 .40 .46 .41 [.38, .44] willingness to change -.35 -.38 -.48 -.59 -.44 -.51 -.45 -.49 -.43 -.49 -.53 -.54 -.47 [-.51, -.44] consumption -.41 -.26 -.22 -.33 -.24 -.19 -.24 -.18 -.33 -.22 -.19 -.35 -.26 [-.31, -.22] petition -.08 -.10 -.23 -.16 -.22 -.19 -.19 .00 -.11 -.22 -.22 -.20 -.16 [-.20, -.12] donation -.35 -.19 -.27 -.30 -.35 -.28 -.31 -.16 -.27 -.24 -.34 -.36 -.28 [-.32, -.25] demonstration -.26 -.04 -.21 -.30 -.24 -.20 -.26 -.11 -.11 -.30 -.30 -.33 -.22 [-.27, -.17] appreciation of nature .28 .02 .16 .28 .16 .12 .29 .19 .16 .23 .25 .27 .20 [.16, .24] Descriptive statistics (scaling) Table 5 presents country-specific descriptive statistics of the environmental efficacy scale scores for the remaining 12 countries. Table 5 Country-specific Scale Statistics Country N M SD Skewness Excess POMP Austria 1,254 3.53 0.71 -0.15 -0.62 63.30 Croatia 999 3.18 0.70 -0.37 0.07 54.54 Denmark 1,115 3.32 0.69 -0.11 -0.15 57.92 Finland 1,116 3.47 0.66 -0.52 0.43 61.65 France 1,496 3.51 0.62 -0.05 -0.32 62.71 Germany 1,645 3.57 0.62 -0.39 -0.02 64.27 Iceland 1,122 3.40 0.59 -0.16 -0.05 60.11 Japan 1,404 3.16 0.63 -0.02 0.41 53.96 Lithuania 1,161 3.09 0.58 -0.17 0.02 52.37 Sweden 1,840 3.45 0.62 -0.21 -0.17 61.37 Switzerland 4,228 3.55 0.63 -0.24 -0.15 63.69 USA 1,784 3.41 0.63 -0.07 -0.16 60.25 Further quality criteria We tested measurement invariance for the 12 countries, where the psychometric properties were acceptable, using multi-group CFA. We did this by successively testing three levels of measurement invariance (Leitgöb et al., 2023). The first level is configural invariance. This level is tested by estimating an unconstrained multi-group CFA. If the absolute model fit is acceptable, configural invariance can be accepted. This means that measurement models are the same for the countries examined. The second level is metric invariance. For metric invariance, factor loadings are constrained to be equal across countries. If the delta CFI is less than .01 compared to the configural model, metric measurement invariance can be accepted (Chen, 2007). This means that latent variances and covariances can be compared. The third level is scalar invariance. For scalar invariance, the intercepts are constrained to be equal in addition to the constrained loadings, but the latent means are allowed to vary across countries. If the delta CFI is smaller than .01 compared to the metric model, scalar measurement invariance can be accepted (Chen, 2007). This means that latent means can be compared across countries. The fit of the configural model was acceptable (CFI = .932, RMSEA = .077; SRMR = .036). However, the constraints of the metric model led to a substantial decrease in fit (ΔCFI = .019). We therefore tested for partial metric invariance by successively freeing loadings as suggested by the modification indices. According to Pokropek, Davidov, & Schmidt (2019) having at least 20% of invariant items is sufficient to receive unbiased latent (co-)variances. This criterion was met since item 4 and 7 (29% of all items) were invariant across all countries. We needed to free the loadings of the other five items in four countries (Item 1 and Item 2 in Japan, Item 3 and Item 5 in Croatia as well as Item 6 in Finland and Sweden) to achieve an acceptable decrease in fit of the metric model (ΔCFI = .010). We, thus, can compare latent (co-)variances and tested for scalar measurement invariance in a next step. A comparison of the fit of the scalar model with the partial metric model revealed a substantial decreased in fit (ΔCFI = .265). Because of the large decrease, we decided not to test for partial scalar measurement invariance. This means that neither latent means, nor manifest (co-)variances and means can be compared across countries. In summary, both configural and metric measurement invariance could be accepted. This means that measurement models as well as latent variances and covariances of the environmental efficacy scale can be compared across Austria, Croatia, Denmark, Finland, France, Germany, Iceland, Japan, Lithuania, Sweden, Switzerland, and the USA. Table 6 Fit Indices for Measurement Invariance Across Countries Model df p CFI RMSEA SRMR AIC BIC Adj. BIC Configural 1,598.806 156 < .001 .932 .077 .036 361,884 363,960 363,121 Metric 2,088.911 222 < .001 .913 .073 .051 362,242 363,799 363,170 Partial Metric 1,885.942 216 < .001 .922 .070 .046 362,052 363,655 363,007 Scalar 7,553.643 288 < .001 .657 .127 .100 367,575 368,613 368,193 Note. N = 19,164. Acknowledgement We would like to thank Piotr Koc for his feedback on this documentation.
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Instruction How much do you agree or disagree with each of these statements? Items Table 1 English Items of the Scale Environmental Efficacy Scale No. Item Polarity 1 It is just too difficult for someone like me to do much about the environment + 2 I do what is right for the environment, even when it costs more money or takes more time – 3 There are more important things to do in life than protect the environment + 4 There is no point in doing what I can for the environment unless others do the same + 5 Many of the claims about environmental threats are exaggerated + 6 I find it hard to know whether the way I live is helpful or harmful to the environment + 7 Environmental problems have a direct effect on my everyday life – The Environmental Efficacy scale (ISSP) was initially developed in English. Translations are available for the following languages: Afrikaans, Assamese, Bengali, Bikol, Cebuano, Chinese, Danish, German, Filipino, Finnish, French, Gujarati, Hiligaynon, Hindi, Hungarian, Iloko, Icelandic, Italian, Japanese, Kannada, Korean, Lithuanian, Malayalam, Marathi, Norwegian, Oriya, Polish, Punjabi, Russian, Slovak, Slovenian, Spanish, Swedish, Tamil, Telugu, Thai, Tswana, Xhosa, Zulu, Waray, Maguindanon. The respective questionnaires can be found on the ISSP website. Response specifications Participants responded on a five-point fully labelled Likert scale. The labels were 1 = Agree strongly, 2 = Agree, 3 = Neither agree nor disagree, 4 = Disagree, 5 = Disagree strongly. Moreover, participants could answer with -8 = Can’t choose, which we coded as missing value for our analyses. Scoring The scores of the items 2 and 7 need to be inverted, so that higher scores on the items correspond to higher environmental efficacy. After inverting, the environmental efficacy scale score can be calculated as the unweighted mean of the seven items. However, we recommend estimating the scale score only if valid responses are available for at least five of the seven items to have complete values for approximately 75% of the items (Mazza, Enders, & Ruehlman, 2015). Application field The environmental efficacy scale is part of the ISSP Environmental module (ISSP Research Group, 2023), which deals mainly with attitudes towards environmental issues. The module has been caried out four times, in 1993, 2000, 2010, and 2020. The environmental efficacy scale was part of each of these modules. However, the number of items increased from two to seven over the module rounds. The scale is available in 43 languages and has been used in 28 countries and in different survey modes (PAPI, CAPI, CASI, CAWI, CATI, self-completion). However, the psychometric properties were acceptable in only 12 countries (Austria, Croatia, Denmark, Finland, France, Germany, Iceland, Japan, Lithuania, Sweden, Switzerland, and the USA). In these countries, the scale is suitable for research purposes, but not for the reliable assessment of the individual level of environmental efficacy as internal consistencies were too low for this purpose. Nevertheless, cross-cultural studies can use the economic (< 2 minutes according to authors’ estimation) environmental efficacy scale to assess environmental efficacy and compare its latent variances and covariances since partial metric invariance held across the 12 countries.
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Item generation and selection The environmental efficacy scale was first used in the 1993 ISSP environmental module (ISSP Research Group, 1995). At that time, the scale included the first two items of the current scale (Item 1 and Item 2). The number of items was increased to the first five items in ISSP 2000 (ISSP Research Group, 2003). The sixth item was added in ISSP 2010 (ISSP Research Group, 2019) and the seventh item was added in ISSP 2020 (ISSP Research Group, 2023). All items were originally developed in English. Translations are available for 43 languages. Samples The data set of the ISSP 2020 environmental module includes data from 28 countries with a total of 44,100 participants (ISSP Research Group, 2023). The data were collected between February 2020 and May 2023 using simple random sampling or multistage sampling. The survey modes were PAPI, CAPI, CASI, CAWI, CATI, and self-completion. Of the 44,100 participants, we excluded 1,268 cases due to missing values for at least three of the seven items of the environmental efficacy scale. Of the remaining N = 42,832 participants 20,107 (46.9%) reported male gender and 22,662 (52.9%) reported female gender (0.15% missing). Regarding education, 1,205 (2.8%) participants reported no formal education, 2,852 (6.7%) reported primary education, 5,931 (13.9%) reported lower secondary education, 14,266 (33.3%) reported upper secondary, 1,317 (3.1%) reported post-secondary education, 4,316 (10.1%) reported short cycle tertiary education, 6,765 (15.8%) reported lower tertiary education, 4,779 (11.16%) reported upper tertiary education, and 653 (1.5%) reported a PhD as highest level of education (1.8% missing). Except for Finland (15+) and South Africa (16+) participants were 18 years or older, with a mean age of M =50 years (SD = 17.4; 0.9% missing). We report country-specific background variables in the Appendix Table A1. Item analyses All analyses were performed in R (4.4.1; R Core Team, 2024). We used the following packages for our analyses: psych (2.4.6.26; Revelle, 2022), lavaan (0.6-18; Rosseel, 2012), semTools (0.5-6; Jorgensen et al., 2022), purrr (1.0.2; Wickham & Henry, 2023), and metafor (4.6-0; Viechtbauer, 2010). We provide our code as Appendix. As a first step, we tested the factorial structure of the environmental efficacy scale. We did this by running single group CFAs in each country, assuming a one-factorial structure for the seven items[1]. We used a robust maximum likelihood estimator (MLR), identified the model via standardized latent variables (latent variance equals unity, latent mean fixed to zero), and used full information maximum likelihood (FIML) to handle missing values. We report the model fit for each country in Table 2. Table 2 Fit Statistics for Single-Group CFAs Assuming a one-Factorial Model Country df p CFI RMSEA SRMR AIC BIC Adj. BIC Australia 172.027 14 < .001 .889 .101 .049 20,855 20,96 20,893 Austria 115.415 14 < .001 .949 .075 .037 24,153 24,261 24,194 China 117.608 14 < .001 .907 .053 .033 53,304 53,427 53,360 Taiwan 109.806 14 < .001 .845 .062 .039 32,054 32,170 32,103 Croatia 114.603 14 < .001 .921 .084 .046 20,053 20,156 20,090 Denmark 91.949 14 < .001 .919 .071 .038 22,615 22,721 22,654 Finland 153.329 14 < .001 .913 .095 .046 20,645 20,751 20,684 France 142.209 14 < .001 .910 .078 .040 28,802 28,913 28,846 Germany 20.733 14 < .001 .884 .092 .048 31,233 31,346 31,280 Hungary 54.455 14 < .001 .960 .052 .033 18,395 18,498 18,431 Iceland 103.711 14 < .001 .908 .075 .041 21,166 21,272 21,205 India 189.475 14 < .001 .594 .099 .058 28,435 28,544 28,478 Italy 149.135 14 < .001 .887 .093 .050 21,421 21,526 21,460 Japan 115.669 14 < .001 .924 .072 .038 26,558 26,668 26,601 South Korea 156.952 14 < .001 .780 .092 .057 20,298 20,405 20,339 Lithuania 146.903 14 < .001 .887 .093 .048 20,554 20,66 20,593 New Zealand 143.995 14 < .001 .868 .098 .050 18,645 18,748 18,681 Norway 168.101 14 < .001 .833 .101 .054 20,644 20,749 20,682 Philippines 112.141 14 < .001 .789 .068 .043 28,127 28,239 28,172 Russia 153.897 14 < .001 .860 .081 .048 35,081 35,193 35,127 Slovakia 404.083 14 < .001 .737 .167 .088 20,365 20,468 20,401 Slovenia 118.430 14 < .001 .901 .083 .047 20,279 20,384 20,317 South Africa 289.108 14 < .001 .888 .084 .049 53,157 53,282 53,215 Spain 294.919 14 < .001 .852 .098 .052 41,609 41,728 41,661 Sweden 247.664 14 < .001 .894 .097 .048 33,918 34,034 33,967 Switzerland 382.369 14 < .001 .916 .080 .039 79,803 79,936 79,869 Thailand 271.604 14 < .001 .799 .119 .061 25,029 25,139 25,072 USA 256.800 14 < .001 .894 .099 .048 32,828 32,943 32,876 Examination of the model fits and the correlation residuals revealed that the one-factor model was unable to account for the covariance between Item 2 and Item 7 of the environmental efficacy scale. Since these items are the two inverted items, we allowed the residuals of these items to be correlated to improve the local model fit. Table 3 shows the model fit for each country. The one-factor with one residual covariance fitted the data better in most countries. However, the model fit was not acceptable in all countries. Referring to Hu and Bentler (1999) and the large sample sizes per country (usually exceeding 1,000), we excluded countries with CFI < .90, RSMEA > .10, or SRMR > .06. These rather liberal criteria ensured a low type I error, while we aimed to reduce type II error by further investigating the factorial structure. Nevertheless, following these model fit criteria resulted in the exclusion of 10 of the 28 countries. Australia, Taiwan, India, South Korea, New Zealand, Norway, the Philippines, Slovakia, Spain, and Thailand were excluded from the following analyses. Table 3 Fit Statistics for Single-Group CFAs Assuming a one-Factorial Model With one Residual Covariance Country df p CFI RMSEA SRMR AIC BIC Adj. BIC Australia 160.373 13 > .001 .896 .101 .047 20,845 20,955 20,885 Austria 109.164 13 > .001 .952 .076 .035 24,149 24,262 24,192 China 72.747 13 > .001 .948 .041 .025 53,261 53,390 53,320 Taiwan 80.041 13 > .001 .892 .054 .032 32,027 32,148 32,078 Croatia 46.257 13 > .001 .975 .049 .028 19,987 20,095 20,025 Denmark 83.384 13 > .001 .927 .070 .035 22,609 22,719 22,649 Finland 124.291 13 > .001 .930 .088 .042 20,618 20,729 20,659 France 117.469 13 > .001 .926 .073 .035 28,779 28,896 28,826 Germany 124.947 13 > .001 .931 .074 .035 31,159 31,278 31,208 Hungary 33.321 13 .002 .982 .037 .022 18,376 18,484 18,44 Iceland 78.672 13 > .001 .933 .066 .035 21,143 21,253 21,184 India 165.725 13 > .001 .644 .096 .062 28,413 28,528 28,458 Italy 104.635 13 > .001 .924 .079 .038 21,378 21,489 21,419 Japan 96.196 13 > .001 .938 .068 .034 26,540 26,656 26,586 South Korea 82.124 13 > .001 .895 .066 .039 20,225 20,337 20,268 Lithuania 120.390 13 > .001 .908 .087 .042 20,529 20,640 20,570 New Zealand 120.560 13 > .001 .891 .092 .043 18,624 18,731 18,661 Norway 152.550 13 > .001 .849 .099 .051 20,630 20,740 20,670 Philippines 105.560 13 > .001 .801 .069 .041 28,123 28,240 28,170 Russia 83.609 13 > .001 .930 .059 .032 35,013 35,130 35,061 Slovakia 286.483 13 > .001 .815 .146 .070 20,249 20,357 20,288 Slovenia 70.083 13 > .001 .947 .062 .034 20,233 20,343 20,273 South Africa 109.349 13 > .001 .962 .051 .026 52,979 53,110 53,040 Spain 260.458 13 > .001 .870 .096 .048 41,576 41,701 41,631 Sweden 176.354 13 > .001 .926 .084 .037 33,849 33,970 33,900 Switzerland 285.701 13 > .001 .938 .071 .033 79,708 79,848 79,778 Thailand 219.575 13 > .001 .840 .110 .052 24,979 25,094 25,024 USA 235.981 13 > .001 .903 .098 .045 32,809 32,930 32,860 Note. The residuals of item 2 and item 7 were allowed to covary. Item parameters We computed mean, standard deviation, skewness, excess, percentage of maximum possible (POMP) and percentage missing for each item in each country and across countries. Moreover, we report standardized factor loadings of the one-factor model with one residual covariance as item selectivity. While we report country specific statistics in the Appendix Table A2, we present overall statistics here. Overall, the item means varied between 3.18 and 3.47. Neither skewness (max. |skewness| = 1.44) nor excess (max. |skewness| = 1.57) showed worrisome levels for any item in any country (West, Finch, & Curran, 1995). Missing value rates were low. A higher missing percentage occurred only for the following countries and items: Denmark: Item 6 (6.55%), Item 7 (13.54%); India: Item 6 (8.12%); Japan: Item 7 (5.41%); Lithuania: Item 5 (7.15%); Russia: Item 6 (5.85%), Sweden: Item 7 (5.54%). Table 4 Overall Descriptive Item Statistics Item N M SD Skewness Excess POMP %-Missing It is just too difficult for someone like me to do much about the environment 42,467 3.33 1.15 -0.35 -0.85 58.3 0.85 I do what is right for the environment, even when it costs more money or takes more time 42,332 3.47 0.92 -0.59 0.03 61.8 1.17 There are more important things to do in life than protect the environment 42,373 3.18 1.12 -0.09 -0.85 54.4 1.07 There is no point in doing what I can for the environment unless others do the same 42,586 3.23 1.24 -0.22 -1.09 55.6 0.57 Many of the claims about environmental threats are exaggerated 41,645 3.41 1.14 -0.33 -0.82 60.3 2.77 I find it hard to know whether the way I live is helpful or harmful to the environment 41,808 3.19 1.06 -0.11 -0.87 54.8 2.39 Environmental problems have a direct effect on my everyday life 41,799 3.23 1.07 -0.30 -0.67 55.7 2.41 Note. Statistics were estimated across all 28 countries. Selectivities (i.e., the correlation between the item and the latent environmental efficacy variable) were measured via standardized factor loadings of the CFAs and were low (< .20) for some items in some countries. Therefore, we also excluded countries with low selectivities. This resulted in the exclusion of six countries, namely China, Hungary, Italy, Russia, Slovenia, and South Africa. We report the overall selectivties based on a CFA assuming the one-factor model with one residual covariances using country as cluster in Figure 1. Note that we only included the 12 countries with sufficient model fit and selectivities in this analysis (N = 19,164). Selectivities for all countries can be found in the Appendix Table A3. For the remaining 12 countries, selectivities ranged from λ = .47–.72 (Lithuania and Japan) for Item 1, λ = .27–.52 (Denmark and Finland) for Item 2, λ = .47–.69 (Croatia and Sweden) for Item 3, λ = .49–.72 (Japan and Finland) for Item 4, λ = .47–.71 (Iceland and Austria) for Item 5, λ = .30–.65 (Finland / Sweden and Austria) for Item 6, and λ = .20–.43 (Denmark and the USA) for Item 7. Figure 1 Measurement Model of Environmental Efficacy Note. Standardized factor loadings of a CFA using factor as cluster are displayed ((13) = 201.877, CFI = .934, RMSEA = .074, SRMR = .035) [1] Screeplots for each country did not indicate the presence of a second factor, wherefore we only tested a one-factorial structure.
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