Representation in Science and Trust in Scientists in the United States

doi:10.21203/rs.3.rs-4892837/v1

Download PDF

Article

Representation in Science and Trust in Scientists in the United States

https://doi.org/10.21203/rs.3.rs-4892837/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

American scientists are notably unrepresentative of the population. The disproportionately small number of scientists who are women, Black, Hispanic or Latino, from rural areas, religious, and from lower socioeconomic backgrounds has consequences. Specifically, it means that, relative to their counterparts, individuals who identify as such are more dissimilar and more socially distant from scientists. These individuals, in turn, have less trust in scientists, which has palpable implications for health decisions and, potentially, mortality. Increasing the presence of underrepresented groups among scientists can increase trust, highlighting a vital benefit of diversifying science. This means expanding representation across several divides—not just gender and race but also rurality and socioeconomic circumstances.

Social science/Politics and international relations

Social science/Sociology

Scientific community and society/Scientific community

The Nobel prize signifies work that, according to Alfred Nobel’s original vision, has had the greatest benefit to humankind. By this accounting, no country has contributed (absolutely) more to science than the United States: American scientists have won more than 40% of the scientific Nobel prizes. No other country has won more than 15%. Underlying this ostensible success is remarkable demographic homogeneity. Through 2022, the composition of American prize winners is 96% men, 99% white, 94% from non-rural (urban or suburban) areas, and 77% with a parent who had attended a higher education institution. While not as extreme, the contemporary scientific workforce in the U.S. is similarly configured: approximately 66% men, 65% white, 92% from non-rural areas, and 74% (at least) second-generation college students. The respective numbers for the current general population are 50%, 59%, 80%, and 56%. Moreover, more than 40% of scientists identify as atheist, dwarfing the percentage in the general population where 28% report having no religious affiliation (which surely includes some non-atheists) (see supporting information for all data details). In short: the institutions of science, especially at their apex, look very different from U.S. society more generally. This misalignment of representation has had long-standing implications for trust in science, and affects the efficacy of societal response to crises such as the one we have faced with COVID-19.

The strata of society that have been underrepresented in science have also distrusted science. Data from the General Social Survey (GSS) show that women, Black, rural, religious, less educated, and lower/work class individuals exhibit relatively less confidence in the scientific community. This has been the case for at least the last half-century (Fig. 1; supporting information). Trust in scientists may be high relative to trust in other institutions (e.g., members of Congress, financial institutions, or the press) (1), but it has long standing demographic chasms.

These trust gaps reflect social difference (i.e., how different an individual perceives themselves to be from others, based on ascriptive characteristics) and social distance (i.e., the interconnectivity of different sets of individuals). In survey data (N = 27,960), we find that, compared to their counterparts, women, rural residents, and religious people feel they have significantly fewer similarities with scientists and are significantly less likely to have family or friends who are scientists (see supporting information). Moreover, both feeling dissimilar to scientists and having fewer of them as friends or family members are associated with lower trust in scientists, controlling for demographic, geographic, and political factors (see supporting information). This suggests possible mediation by social difference and social distance; it also is consistent with work on trust and similarity (2–5) and trust and social distance (6, 7). Distrust in scientists also can stem from certain populations not being well served by science via abuse or omission. For example, unethical and/or risky medical studies historically relied on subjects from underrepresented groups, most notably Black Americans (8, 9). Alternatively, scientific research often lacks relevant data (and, hence, knowledge) for racial or ethnic minorities and women (10–15), and insufficient infrastructure in rural communities (16). The disparities reflect, in part, scientists’ tendency to study reflections of themselves (17). That is, social difference and distance may make people less trusting of scientists; but further, those factors might make scientists less attentive to these strata as well.

Trust in science matters. Science “is our best guide to developing factual understandings” (18). This proved true when it came to life-saving vaccination decisions during the COVID-19 pandemic. In a large, over-time survey (N = 2,941) with repeat respondents, we find a clear relationship between individuals’ trust in scientists and researchers to do the right thing in handling the pandemic (on a four-point scale) measured in May-August, 2020, and their vaccination status a year later in June-July 2021 (Fig. 2A; see supporting information). The relationship holds in a multivariate analysis; trust has a sizable substantive impact, considerably larger than partisanship which was a widely discussed driver of COVID-19 behaviors (19, 20) (Fig. 2B; see supporting information). Compared to those who do not trust scientists “at all,” those who trust scientists “a lot” have roughly a 35% higher probability of having received a COVID-19 vaccine dose.

Taken together, the stability of the demographic-trust correlations along with the consequences of trust for COVID-19 vaccination mean that one can anticipate how likely a given group of individuals will be to follow scientific advice. Indeed, we used 1970s GSS trust in scientists data impute, based on demographics, “predicted trust in scientists”—that is, what we could anticipate an individuals’ anticipated trust given their demographics and data from the 1970s (see supporting information). We find that predicted trust strongly relates to vaccination decisions (Fig. 2C). The dynamics of trust in scientists are such that one could have forecasted vaccination reactions based on information from the 1970s.

This translates into state-level variation in outcomes: once vaccines became widely available in the US, pre-vaccine/lagged state-level trust in scientists substantially correlates with a state’s vaccination rate (Fig. 3A; see supporting information). Moreover, similar to what we did with the individual level data, we can predict average state level trust based on the 1970s GSS data. State average predicted trust significantly correlates with vaccine decisions (in similar ways to the contemporary measure) (Fig. 3B). We show in supporting information that vaccine uptake, in turn, correlates with lower state mortality during the period in which vaccines became available until the post-Omicron variant period (by which time almost everyone would have acquired immunity either through vaccination or through infection) (21). This means there also is a relationship between state-level trust / state level predicted trust and state level mortality (Fig. 3C and Fig. 3D).

In sum, those from demographic groups not well represented in science have less similarity with scientists, fewer ties to scientists, and less trust in scientists. Trust in scientists shapes decisions that can have life-altering implications. The relationships we document cohere with other data showing relatively higher COVID-19 mortality rates among Black and Hispanic people (23), rural residents (24), religious individuals (25), and those with low socioeconomic status (26).

Interventions to build trust in scientists need to acknowledge that uncritical faith not only is counter to science itself but also could breed harmful consequences. When distrust stems in part from underrepresentation, however, it raises the question of whether expanding representation can play a role in building trust. This is important given relative exclusion from the practice of science and lower trust in scientists might preclude groups from the potential benefits of science (27). In line with our earlier argument about social similarity/difference, we sought to test whether individuals prefer to follow (i.e., trust) the advice of scientists with whom they share characteristics (28).

We implemented a survey experiment with a nationally representative sample (N = 1,120). Participants chose one of two scientists (e.g., A or B) from whom they would follow advice for taking a vaccine or which of two doctors (e.g., A or B) they would prefer to have as their primary care physician (see supporting information). We merged analyses for scientists and doctors as the overall results were similar for each. Henceforth we refer to “scientists” for efficiency. Each scientist was described along seven dimensions: experience (low or high), gender (male or female), race (white, Black, Hispanic, or Asian American), education (public institution or Ivy League), religiosity (e.g., speaks to religious organizations or to civic organizations), rural or urban upbringing, and class background (lower, middle, or upper). We included education and class background to capture socioeconomic profiles. Experience provides a benchmark to assess the impact of demographic characteristics.

A respondent would receive a table that described Scientist A’s and Scientist B’s characteristics. The information for Scientist A might describe them as high experience, male, white, Ivy League educated, religious, rural, and middle class. Scientist B might be portrayed as low experience, male, Black, Ivy League educated, non-religious, urban, and middle class. The precise attributes for each scientist were probabilistically determined and could be the same (or not) for A and B (in the example, A and B are both male, but A is white and B is Black). We then computed whether each demographic attribute was a “match” or “not a match” for the respondent. For instance, if the respondent were a Black religious male, then we would code Scientist A as being a gender and religiosity match but not a race match; Scientist B would be a gender and race match but not a religiosity match. The one exception was experience, which was not a variable that was matched; rather, it was simply an indicator (0/1) of whether the Scientist was experienced. Our interest is in whether the likelihood of choosing Scientist A or Scientist B increases in the presence of a given demographic match.Figure 4. Impact of Match on Trust. OLS coefficients and 95% intervals for a regression of choosing Scientist B (over Scientist A) on whether the respondent’s given demographic attribute matched Scientist B (“Match B”) or Scientist A (“Match A”) (or, in the case of experience, was experienced). (A) Regression with all respondents. (B) Regression with “Overrepresented groups” that includes respondents from the demographic group noted in the figure who are overrepresented among scientists (e.g., the Gender Match A (Male) indicates whether Scientist A was a Male for Male respondents). (C) Regression with “Underrepresented groups” that includes respondents from the demographic group noted in the figure who are underrepresented among scientists (e.g., the Gender Match A (Female) indicates whether Scientist A was a Female for Female respondents). All data come from a conjoint experiment (with a total of 12,220 observations). Details are in the supporting information.

Several findings stand out. First, experience dwarfs any single demographic, with respondents strongly preferring the more experienced option, increasing the probability of a given choice by nearly 25 percentage points. Second, the results reveal that a demographic match (e.g., a female scientist for a female respondent or a scientist who grew up in a rural setting for a rural respondent) affects the probability of selecting the scientist. This holds for gender (roughly a 4.5 percentage point change), race (roughly a 9.5 percentage point change), religiosity (roughly a 7.5 percentage point change), and urban/rural (roughly a 3 percentage point change). Education and class do not exhibit meaningful effects. Demographic effects are small relative to experience, but they build upon one another; on average, each additional match increased the likelihood of a choice by about 4 percentage points, meaning that if all six attributes match, it boosts the likelihood of that choice by roughly 24 percentage points, equaling the experience effect (see supporting information). Third, if the two choices are both matches on a given attribute (e.g., race), the impact of a match on that attribute (e.g., race) cancels out, which is sensible.

Figure 4B shows the influence of matches for those who are overrepresented in science on the given attribute. For instance, for gender, it displays the impact of the option (A or B) being male for male respondents. For race, the figure reports the impact of a racial match for white respondents (and for the other attributes, respectively, college educated, non-religious, urban, and middle or upper class). Figure 4C shows the effects for those underrepresented among scientists, such as when the option (A or B) is Black for Black respondents, and so on, as noted in the figure. The figures make clear that female, Black, Hispanic or Latino, rural, and lower-class respondents displayed significantly stronger preferences for a scientist who matched their acute respective demographic than their better-represented counterparts who, in fact, are largely indifferent to the relevant demographics (also see 29). That said, those from overrepresented groups relatively prioritize religiosity and education. Specifically, non-religious respondents strongly prefer a non-religious choice to a greater extent than religious individuals prefer a choice that signals religiosity. Also, educated individuals show a match effect, whereas less educated individuals do not. Experience has nearly identical effects for respondents from under- and overrepresented groups. Overall, those underrepresented in science in terms of gender, race, rurality, and class prefer scientists who share their backgrounds (more than their overrepresented counterparts).

Expanding representation also can increase general trust in scientists. In the experiment, respondents reported their general trust in scientists on a scale from 0 to 100 prior to choosing between the scientists, an exercise they did ten times (i.e., they received ten profiles and made ten choices, and the results in Fig. 4 include each of those choices). They were then asked about their general trust after the scenarios. We find that as the number of precise matches received across the ten choices increased, so did general trust in scientists—this is particularly the case for female, Black, and religious respondents. For example, as female respondents receive more female scientist choices over the course of the experiment, their overall trust increases. The same is true for Black respondents (for matches regarding race) and religious respondents (for matches regarding religiosity) (see supporting information). Notably, this occurs for trust in scientists but not for trust in pharmaceutical companies (which serves as a placebo).

Efforts to increase diversity in science are widespread, though whether these initiatives will generate a fully representative workforce remains unclear and, regardless, is difficult to determine given science training can span decades (30). Common justifications to address underrepresentation include a moral equality imperative and potential gains in innovation and work quality (31–33). We have identified a distinct benefit: to increase trust in scientists among those underrepresented in science fields, including women, non-white people, people who reside in rural areas, religious individuals, and those from less advantaged socioeconomic backgrounds. Increased trust, in turn, makes it more likely that individuals follow the advice of scientists in crucial life-saving situations. Science and scientists have obvious limitations, but they should be accessible and potentially helpful to all citizens regardless of their backgrounds. Given the profound history of inequalities in the field, this likely requires expanding the diversity of scientists and, as Graves and colleagues (34) state, science should be “equitably distributed across society and… not entail costs borne by the already disadvantaged.”

We posited that lower trust among particular demographic groups partially reflects less similarity with and more social distance (fewer ties) to scientists. The latter can be addressed in the near term by building bridges to underrepresented communities via partnerships (35, 36). The former involves longer-term generational efforts that can be accelerated by reducing social distance—each initiative builds upon the other. Importantly, these initiatives will best succeed when including the full range of underrepresented populations, an often overlooked element of diversity efforts. These include gender, race, religiosity, geography, socioeconomic background, and possibly other characteristics (37). Such efforts are vital to ensure equitable access to the benefits of science.

We received IRB approval from Northeastern University and Northwestern University. All participants consented to participate in the data collections.

Acknowledgements: We thank Arlo Cohen, Maryarita Kobotis, Dot Sawler, Oliver Sun, and Max Weylandt for their research assistance.

Funding: This work was supported by the National Science Foundation under grants SES-2029292, SES-2029792, SES-2116465, SES-2116189, SES-2116458, SES-211663, SES-2241884, SES-2241885, SES-2241886, and SES-2241887 as well as the John S. and James L. Knight Foundation, Amazon Web Services, and the Peter G. Peterson Foundation.

Author Contributions:

Conceptualization: DMJL, JND

Methodology: JND, KO, AS, JS, KLT, AAU, JG, MAB, AQM, HQ, RHP, DMJL

Investigation: JND, KO, AS, JS, KLT, AAU, JG, MAB, AQM, HQ, RHP, DMJL

Visualization: KO

Supervision: JND, DMJL

Writing—original draft: JND

Writing—review & editing: JND, KO, AS, JS, KLT, AAU, JG, MAB, AQM, HQ, RHP, DMJL

Competing Interests: The authors declare they have no competing interests.

Data and Materials Availability: All data needed to evaluate the conclusions in the paper will be available in a public permanent data archive.

Krause, Nicole M., Dominique Brossard, Dietram A. Scheufele, Michael A. Xenos, and Keith Franke. 2019. “Trends—Americans’ Trust in Science and Scientists.” Public Opinion Quarterly 83(4): 817-836.
Byrne, Donn., Charles R. Ervin, and John Lamberth. 1970. “Continuity Between the Experimental Study of Attraction and Real-life Computer Dating.” Journal of Personality and Social Psychology 16(1): 157–165
Wilson, Elizabeth J., and Daniel L. Sherrell. 1993. “Source Effects in Communication and Persuasion Research: A Meta-analysis of Effect Size. Journal of the Academy of Marketing Science 21: 101-112.
Minozzi, William, Hyunjin Song, David M. J. Lazer, Michael A. Neblo, and Katherine Ognyanova. 2020. “The Incidental Pundit: Who Talks Politics with Whom, and Why?” American Journal of Political Science 64(1): 135-151.
Druckman, James N. 2024. “Persuasive Political Targeting,” In Richard E. Petty, Andrew Luttrell, and Jacob D. Teeny, eds., The Handbook of Personalized Persuasion: Theory and Application. New York: Taylor & Francis/Routledge.
Binzel, Christine and Dietmar Fehr. 2013. “Social distance and Trust: Experimental Evidence from a Slum in Cairo.” Journal of Development Economics 103: 99-106.
Martin, Sean R., Julia J. Lee, and Bidhan Lalit Parmar. 2021. “Social Distance Trust and Getting ‘Hooked’: A Phishing Expedition.” Organizational Behavior and Human Decision Processes 166): 39-48.
Byrd, W. Michael, and Linda A. Clayton. 2001. “Race, Medicine, and Health Care In the United States: A Historical Survey.” Journal of the National Medical Association 93(3, Suppl.): 11S-34S.
Washington, Harriet A. 2006. Medical Apartheid: The Dark History of Medical Experimentation on Black Americans from Colonial Times to the Present. New York: Doubleday.
Söderström M. 2001. “Why Researchers Excluded Women from Their Trial Populations.” Lakartidningen 98(13):1524-1528.
Hussain-Gambles, Mahvash, Karl Atkinl, and Brenda Leese. 2004. “Why Ethnic Minority Groups are Under-represented in Clinical Trials: A Review of the Literature.” Health and Social Care in the Community 12(5), 382-388.
Konkel, Lindsey. 2015. “Racial and Ethnic Disparities in Research Studies: The Challenge of Creating More Diverse Cohorts.” Environmental Health Perspectives 123(12): 297-302.
Curno, Mirjam J., Samuela Rossi, Ioannis Hodges-Mameletzis, Rowena Johnston, Matt A. Price, and Shirin Heidari. 2016. “A Systematic Review of the Inclusion (or Exclusion) of Women in HIV Research: From Clinical Studies of Antiretrovirals and Vaccines to Cure Strategies.” JAIDS Journal of Acquired Immune Deficiency Syndromes 71(2): 181-188.
Woitowich, Nicole C., Annaliese Beery, and Teresa Woodruff. 2020. “Meta-Research: A 10-year Follow-up Study of Sex Inclusion in the Biological Sciences.” eLife 9: e56344.
Stillwell, R. Craig. 2022. “Exclusion of Women from COVID-19 Studies Harms Women's Health and Slows our Response to Pandemics. Biology of Sex Differences 13(1): 27.
Minner, Daphne D., and Elisabeth Hiles. 2005. “Rural School-Community Partnerships: The Case of Science Education.” Issues in Teacher Education 14(1): 81-94.
Kozlowski, Diego, Vincent Larivière, Cassidy R. Sugimoto, and Thema Monroe-White. 2022. “Intersectional Inequalities in Science.” Proceedings of the National Academy of Sciences 119(2): e2113067119.
Dietz, Thomas. 2013. “Bringing Values and Deliberation to Science Communication.” Proceedings of the National Academy of Sciences 110(3): 14081-14087.
Gadarian, Shana Kushner, Sara Wallace Goodman, and Thomas B. Pepinsky. 2022. Pandemic Politics: The Deadly Toll of Partisanship in the Age of COVID. Princeton: Princeton University Press.
Druckman, James N., Samara Klar, Yanna Krupnikov, Matthew Levendusky, and John Barry Ryan. 2024. Partisan Hostility and American Democracy. Chicago, IL: University of Chicago Press.
Syal, Akshay. 2023. “Natural Immunity as Protective as Covid Vaccine against Severe Illness.”NBC News. February 16. https://www.nbcnews.com/health/health-news/natural-immunity-protective-covid-vaccine-severe-illness-rcna71027.
The New York Times. 2022. Coronavirus (Covid-19) Data in the United States. Retrieved July 12, 2023, from https://github.com/nytimes/covid-19-data.
Aburto, Jose Manuel, Andrea M. Tilstra, Ginevra Floridia, and Jennifer Beam Dowd. 2022. “Significant Impacts of the COVID-19 Pandemic on Race/Ethnic Differences in US Mortality.” Proceedings of the National Academy of Sciences 119(35): e2205813119.
Paglino, Eugenio, Dielle J. Lundberg, Zhenwei Zhou, Joe A. Wasserman, Rafeya Raquib, Anneliese N. Luck, Katherine Hempstead, Jacob Bor, Samuel H. Preston, Irma T. Elo, and Andrew C. Stokes. 2023. “Monthly Excess Mortality across Counties in the United States during the COVID-19 Pandemic, March 2020 to February 2022.” Science Advances 9: eadf9742.
Linke, Magdalena, and Konrad S. Jankowski. 2022. “Religiosity and the Spread of COVID-19: A Multinational Comparison.” Journal of Religion and Health 61: 1641-1656.
Miller, Sarah, Laura R. Wherry, and Bhashkar Mazumder. 2021. “Estimated Mortality Increases During The COVID-19 Pandemic By Socioeconomic Status, Race, And Ethnicity.” Health Affairs 40 (8): 1252-1260.
Druckman, James N. 2022. “Introduction to Threats to Science: Politicization, Misinformation, and Inequalities,” The Annals of the American Academy of Political and Social Science 700: 8-24.
Byrne, Donn. 1997. “An Overview (and Underview) of Research and Theory within the Attraction Paradigm. Journal of Social and Personal Relationships.” 14(3): 417-431.
Cabral, Marika, and Marcus Dillender. 2024. “Gender Differences in Medical Evaluations: Evidence from Randomly Assigned Doctors.” American Economic Review 114(2): 462-469.
Matias, J. Nathan, Neil A. Lewis, and Elan C. Hope. 2022. US Universities are Not Succeeding in Diversifying Faculty. Nature Human Behavior 6: 1606–1608.
Nature. 2018. “Science Benefits from Diversity.” Nature 558: 5-6.
Swartz, Talia H., Ann-Gel S. Palermo, Sandra K. Masur, and Judith A. Aberg. 2019. “The Science and Value of Diversity: Closing the Gaps in Our Understanding of Inclusion and Diversity.” The Journal of Infectious Diseases 220: S33–S41.
Yang Yang, Tanya Y. Tian, Teresa K. Woodruff, Benjamin F. Jones, and Brian Uzzi. 2022. “Gender-diverse Teams Produce More Novel and Higher-impact Scientific Ideas.” Proceedings of the National Academy of Sciences 119: e2200841119.
Graves Jr, Joseph L., Maureen Kearney, Gilda Barabino, and Shirley Malcom. 2022. “Inequality in Science and the Case for a New Agenda.” Proceedings of the National Academy of Sciences 119(10): e2117831119.
Safford, Hugh D., Sarah C. Sawyer, Susan D. Kocher, J. Kevin Hier, and Molly Cross. 2017. Linking knowledge to action: The role of boundary spanners in translating ecology. Frontiers in Ecology and the Environment 15: 560-568.
Bayes, Robin, James N. Druckman, and Alauna C. Safarpour. 2022. “Studying Science Inequities: How to Use Surveys to Study Diverse Populations.” The Annals of the American Academy of Political and Social Science 700: 220-233.
Busch, Carly A., Tala Araghi, Jingyi He, Katelyn M. Cooper, and Sara E. Brownell. 2024. “Beyond Gender and Race: The Representation of Concealable Identities Among College Science Instructors at Research Institutions.” CBE—Life Sciences Education 23: 1–9.
https://en.wikipedia.org/wiki/List_of_black_Nobel_laureates
https://en.wikipedia.org/wiki/List_of_Asian_Nobel_laureates
Okrent, Abigail, and Amy Burke. 2021. “The STEM Labor Force of Today: Scientists, Engineers, and Skilled Technical Workers.” https://ncses.nsf.gov/pubs/nsb20212.
Browne, Alyssa. 2022. “Demographic Characteristics and Work Experiences of Physician Scientists in the U.S.” Association of American Medical Colleges. https://www.aamc.org/data-reports/report/demographic-characteristics-and-work-experiences-physician-scientists-us
O'Neal L., and A. Perkins. 2021. “Rural Exclusion from Science and Academia.” Trends in Microbiology 29(11): 953-956.
Schultz, Robert, and Anna Stansbury. 2022. “Socioeconomic Diversity of Economics PhDs.” Peterson Institute for International Economics Working Paper N. 22-4. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4068831.
https://www.nsf.gov/statistics/2016/nsf16300/digest/theme5.cfm
Pew Research Center 2009. “Religion and Science in the United States.” November 5. https://www.pewresearch.org/religion/2009/11/05/an-overview-of-religion-and-science-in-the-united-states/.
Larson, Edward J., and Larry Witham. 1997. “Scientists Are Still Keeping the Faith.” Nature 386: 435-436.
Ecklund, Elaine Howard. 2010. Science Vs. Religion: What Scientists Really Think. New York, NY: Oxford University Press.
Leuba, James H. 1916. “The Belief in God and Immortality: A Psychological, Anthropological and Statistical Study.” Boston: Sherman, French & Co.
Mehta, Sharan Kaur, Rogert A. Thomson Jr, and Elaine Howard Ecklund. 2021. “Polarized Scientists? Exploring Political Differences about Religion and Science among US Biologists and Physicists.” Sociological Forum 36 (1): 5-28.
https://www.census.gov/programs-surveys/decennial-census/decade/2020/2020-census-main.html
Enns, Peter, and Jacob Rothschild. 2021. “Revisiting the ‘Gold Standard’ of Polling.” 3Streams, April 12. https://medium.com/3streams/revisiting-the-gold-standard-of-polling-new-methods-outperformed-traditional-ones-in-2020-451650a9ba5b
Santillana, Muricio, Ata A. Uslu, Tamanna Urmi, Alexi Quintana, Katherine Ognyanova, Matthew A. Baum, James N. Druckman, Roy H. Perlis, and David Lazer. N.d. “Survey Data Yields Improved Estimates of Test-Confirmed COVID-19 Cases When Rapid At-Home Tests Were Massively Distributed in the United States,”JAMA Network Open, Forthcoming.
Lehdonvirta, Vili, Atte Oksanen, Pekka Räsänen, and Grant Blank. 2021. “Social Media, Web, and Panel Surveys. Policy & Internet13: 134-155.
https://www.cdc.gov/nchs/data_access/urban_rural.htm
The New York Times. 2022. Coronavirus (Covid-19) Data in the United States. Retrieved July 12, 2023, from https://github.com/nytimes/covid-19-data.
Jones, Jeffrey. 2017. “GOP Maintains Edge in State Party Affiliation in 2016” January 30. Gallup. Accessed: March 14, 2024.https://news.gallup.com/poll/203117/gop-maintains-edge-state-party-affiliation-2016.aspx
NASHP. 2023. “State Efforts to Limit or Enforce COVID-19 Vaccine Mandates” Accessed: July 1, 2023. National Academy for State Health Policy (NASHP). https://nashp.org/state-tracker/state-efforts-to-ban-or-enforce-covid-19-vaccine-mandates-and-passports/
Bansak, Kirk, Jens Hainmueller, Daniel J. Hopkins, and Teppei Yamamoto. 2021. “Conjoint Survey Experiments.” In James N. Druckman, and Donald P. Green, eds., Advances in Experimental Political Science. New York: Cambridge University Press.
Druckman, James N. 2022. Experimental Thinking: A Primer on Social Science Experiments, New York: Cambridge University Press.
Schuessler, Julian, and Markus Freitag. 2023. “Power Analysis for Conjoint Experiments.” Unpublished paper. https://doi.org/10.31235/osf.io/9yuhp.
Clayton, Katherine, Yusaku Horiuchi, Aaron R. Kaufman, Gary King, and Mayya Komisarchik. 2023. “Correcting Measurement Error Bias in Conjoint Survey Experiments.” Unpublished paper. http://tinyurl.com/24btw3dq

There is NO Competing Interest.

SupportingInformation.docx

Download PDF

Version 1

posted

You are reading this latest preprint version

Representation in Science and Trust in Scientists in the United States

Status:

Version 1

Abstract

Figures

Introduction

Trust in Scientists and COVID-19 Outcomes

Building Trust in Scientists

Conclusion

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1