Studying Science Inequities: How to Use Surveys to Study Diverse Populations

Abstract

Scholars have long documented unequal access to the benefits of science among different groups in the United States. Particular populations, such as low-income, non–white people, and Indigenous people, fare worse when it comes to health care, infectious diseases, climate change, and access to technology. These types of inequities can be partially addressed with targeted interventions aimed at facilitating access to scientific information. Doing so requires knowledge about what different groups think when it comes to relevant scientific topics. Yet data collection efforts for the study of most science-based issues do not include enough respondents from these populations. We discuss this gap and offer an overview of pertinent sampling and administrative considerations in studying underserved populations. A sustained effort to study diverse populations, including through community partnerships, can help to address extant inequities.

Inequality and Misperceptions of Group Concerns Threaten the Integrity and Societal Impact of Science

“…And might we expect similar findings in other countries with high levels of inequality, where the perspectives of disproportionately affected groups are often similarly lacking in science and policymaking (Viswanath et al, this volume)? Future research might shed light on these and other questions by incorporating new survey methods that enable researchers to reach more diverse populations than what general population surveys and other traditional survey methods typically afford (Bayes, Druckman, and Safarpour, this volume).…”

Measuring What Matters: Data Absenteeism, Science Communication, and the Perpetuation of Inequities

“…This requires a shift in the way we think of those who are included in our research as “participants” and not just “subjects.” Similarly, if our science is expected to have an impact, then the act of “doing” science is not about thrusting science on the community, as much as it is about engaging in science that is participatory. Institutionalization of efforts to address data absenteeism, such as in public use data collections (see Bayes, Druckman, and Safarpour, this volume) are sorely needed. Along the way, questions about ownership of data and how (and how long) the data are used could be topics of negotiation and navigation, rather than a unilateral decision of the scientists.…”