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Testing the cultural-invariance hypothesis: A global analysis of the relationship between scientific knowledge and attitudes to science [1]

['Patrick Sturgis', 'Department Of Methodology', 'London School Of Economics', 'Political Science', 'London', 'United Kingdom', 'Ian Brunton-Smith', 'Department Of Sociology', 'University Of Surrey', 'Guildford']

Date: 2024-02

A substantial body of research has demonstrated that science knowledge is correlated with attitudes towards science, with most studies finding a positive relationship between the two constructs; people who are more knowledgeable about science tend to be more positive about it. However, this evidence base has been almost exclusively confined to high and middle-income democracies, with poorer and less developed nations excluded from consideration. In this study, we conduct the first global investigation of the science knowledge-attitude relationship, using the 2018 Wellcome Global Monitor survey. Our results show a positive knowledge-attitude correlation in all but one of the 144 countries investigated. This robust cross-national relationship is consistent across both science literacy and self-assessed measures of science knowledge.

Copyright: © 2024 Sturgis et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Introduction

The association between scientific knowledge and attitudes to science and technology has been the subject of heated debate since the inception of the study of public understanding of science. The simple claim at the centre of this debate is that higher levels of science knowledge produce more positive evaluations of science. This has come to be known, somewhat pejoratively, as the ‘deficit model’ of public attitudes to science [1, 2]. From this perspective, public resistance to controversial areas of science and technology arises from a deficiency in scientific awareness and understanding. Where science knowledge is low or non-existent, the argument goes, fear of the unknown and mistaken beliefs drive negative responses to scientific research and technology.

In a landmark study in this area, Allum et al. (2008) investigated the strength and generality of the knowledge-attitude relationship using a meta-analysis of existing surveys [3]. They identified a small positive correlation, the magnitude of which varied according to the focus and specificity of the attitudinal measure considered. However, as with most attitudinal studies, the range of countries included was limited to high- and middle-income democracies, covering only a minority of peoples and cultures across the world [4]. The narrow range and homogeneity of the countries available in the 2008 data led these authors to call for a broader investigation of what they termed the ‘cultural invariance hypothesis’; the claim of a (near) universal positive relationship between science knowledge and attitudes towards science across countries, socio-political contexts, and cultures.

That task is the object of this study: to revisit the science knowledge-attitude nexus, but in a data set with far higher coverage of countries and societal contexts across the world. To do this, we use the 2018 Wellcome Global Monitor (WGM) survey, which includes over 149,000 respondents across 144 countries. The content of the WGM questionnaire enables us to estimate this correlation for two different measures of science knowledge commonly used in the existing literature: one which taps ‘science literacy’ [1] and another which uses respondents’ self-assessments of their understanding of science [5].

Background and relevant literature When scholars in the interdisciplinary field of public understanding of science began to assess the knowledge-attitude relationship some 40 years ago [6–8], they launched a strand of theoretical and empirical research on the nature and role of scientific knowledge in the formation of attitudes towards science and technology. Why was this focus on science knowledge thought to be interesting and worthwhile? Arguably it is because it embodied the default assumption of many policy makers and scientists about the roots of public support for (and opposition t0) scientific research programmes [9]; that ‘to know science is to love it’ [3]. And, by the same token, that public resistance to controversial areas of science is grounded in ignorance and misunderstanding, an orientation which remains prominent to this day [10, 11]. The basic premise of the deficit model’s proposition has been supported by findings from surveys in the US and Europe which showed that large majorities of the public were unable to recognise basic scientific facts, such as that the earth orbits the sun, or that electrons are smaller than atoms [12, 13]. Allum et al’s 2008 meta-analysis [3] demonstrated firstly that, on average, a small positive correlation existed between science knowledge and attitudes across high and middle-income countries. Secondly, it showed that the magnitude, and even the sign and of the correlation, varied according to the specific focus of the science–with some areas exhibiting considerably stronger correlations than others. The significance of these findings could be interpreted in two ways. On the one hand, they provided support to those who contend that science literacy matters for promoting public enthusiasm for science and technology. On the other hand, the very same findings offered evidence that science knowledge explains only a small amount of the total variation in science attitudes. This left space for other more important drivers of science attitudes, such as identity [14–16], transparency [17–19], and societal shocks [20, 21], to be foregrounded. There have been a number of further studies since 2008 that have examined the knowledge-attitude correlation across a range of scientific and technological contexts. These have mostly continued to find positive correlations. Studies have found, for example, that science knowledge increases support for research on stem cells [22], climate change [23, 24], evolution and nanotechnology [25], as well as general attitudes to science [26]. A smaller number have found negative correlations, usually when considering specific population sub-groups and for areas of science that are subject to political controversy or ethical debate [27, 28]. For instance, Cacciatore et al [29] found that perceptions of risk about biofuels were higher amongst those who knew more about the science underpinning biofuel extraction. A negative or null relationship has also been found when issue-relevant predispositions are included as moderators of the knowledge attitude relationship [14, 25, 30, 31]. In these cases, while a positive effect of knowledge is observed for the population as a whole, it is zero or negative for groups such as conservatives and those with religious convictions. While the moderating effect of ideology and values on the knowledge-attitude nexus is now well-established, the influence of country or other geospatial units has been afforded less attention in the empirical literature. An exception is Bauer et al [32] who found a curvilinear relationship between the strength of the knowledge–attitude correlation at the country level with gross domestic product (GDP). The correlation they observed, was lowest in European countries that are most economically advanced but markedly stronger in less developed societies. Bauer and colleagues’ interpretation of this relationship was that citizens of countries at an earlier stage of development are in thrall to the power of science and its potential for social and economic transformation. In ‘post-industrial’ nations, however, science and technology is taken for granted as a commonplace feature of everyday life and the public begins to question the ethics of research programmes and the often uneven distribution of their benefits [33]. Allum et al [34] confirmed this finding with a similar study showing considerable variation in the knowledge-attitude correlation. They found, additionally, that the heterogeneity they observed was partially explained by indicators of regional economic and technological development. Our objective in this paper is to return to the question of how scientific knowledge is related to attitudes to science but now adopting a truly global perspective. We fit multi-level models to survey data covering over 90% of the world’s population, providing by far the most comprehensive coverage of global attitudes to date. We assess the overall strength and direction of the knowledge-attitude relationship, as well as how it varies across country contexts and using two different measures of science knowledge.

Data and measures We use data from the 2018 Wellcome Global Monitor, a cross-national survey of adults aged 15+ living in households at non-institutional addresses. The achieved sample size was approximately 1,000 in each of the 144 countries, rising to 2,000 for China, India, and Russia, resulting in a total sample of 149,014 individuals. In countries with at least 80% phone coverage, interviews were carried out via Computer Assisted Telephone Interviewing (CATI), with face-to-face interviewing used in the remaining countries. For telephone interviews, sampling was implemented through either Random Digit Dialling (RDD) or simple random sampling from nationally representative lists of numbers. Dual frame sampling was used in countries with high rates of mobile phone penetration. Sampling for in-home interviews was implemented in 2-stages, where the first stage selected primary sampling units (PSU) with probability proportional to population size and the second stage selected a random sample of households within each PSU, using the random route method. The source questionnaire was produced in English, Spanish, and French and then translated using local translators into every language spoken by more than 5% of the resident population in each country using back translation. Further details about the methodology of the GWP can be found in the survey technical report (https://cms.wellcome.org/sites/default/files/wgm2018-methodology.pdf). We include two measures of science knowledge, one that approximates the standard type of science literacy index used in most existing studies, and a measure of self-assessed understanding of science. While covering the same underlying conceptual domain, there are important differences between these types of measures [35, 36]. Our purpose in including both is not to assess their relative performance in terms of validity and reliability but rather to provide a full descriptive picture of the global science knowledge-attitude association from the perspective of existing research on this question. The science literacy measure is derived from three items which tap, directly or indirectly, the respondent’s understanding of scientific concepts. It is taken as the predicted score from a 2-parameter Item Response Theory (IRT) model from the following three items (correct answers indicated (1) incorrect (0)): Do you think studying diseases is a part of science? Yes (1), No (0)

On this survey, when I say ‘science’ I mean the understanding we have about the world from observation and testing. When I say ‘scientists’ I mean people who study the Planet Earth, nature and medicine, among other things. How much did you understand the meaning of ‘science’ and ‘scientists’ that was just read? A lot (1), Some (0), not much (0), not at all (0)

A vaccine is given to people to strengthen their body’s ability to fight certain diseases. Sometimes people are given a vaccine as [insert country equivalent term for a shot or an injection], but vaccines can also be given by mouth or some other way. Before today, had you ever heard of a vaccine? Yes (1), No (0) We considered a fourth item which asked ‘do you think poetry is a part of science? (Yes(1), No(0) but the IRT model indicated that this did not scale with the other three items. These items were not designed with the intention of measuring science literacy and the scale is sub-optimal in both content coverage and specificity. We return to a consideration of the implications of these limitations in the discussion section. Self-assessed science knowledge is measured with a single item, ‘How much do you, personally, know about science? Do you know a lot, some, not much, or nothing at all’. The measure of general attitude to science was also derived using a 2-parameter IRT model applied to the following three items: In general, do you think the work that scientists do benefits most, some, or very few people in this country? A lot (1), Some (0), Not much (0), Not at all (0).

In general, do you think the work that scientists do benefits people like you in this country? Yes (1), No (0)

Overall, do you think that science and technology will help improve life for the next generation? Yes (1), No (0) Full details of the IRT models for the science literacy and attitude to science measures can be found in S1-S3 Tables and S1-S3 Figs in the S1 File and histograms of the two knowledge and the attitude to science measures for all countries in S1-S6 Figs in the S1 File. Data and code for all analyses are deposited at the corresponding author’s Open Science Framework page (https://osf.io/argep/).

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[1] Url: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0296860

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