Melody S. Goodman, @goodmanthebrain, is the associate dean for research and a professor of biostatistics at New York University School of Global Public Health.

Janice Johnson Dias, @DrJaniceJ, is an associate professor of sociology at John Jay College of Criminal Justice and the president of the GrassROOTS Community Foundation.

Data is one of the world’s most valuable resources and an important tool for improving community health. Therefore, knowledge of how to understand, interpret, and share data is necessary for personal and professional success, most importantly for the work of changemaking and improving well-being of low-income and/or predominately minority communities.

In many instances, racialized minority and/or low-income communities are systematically left out of data training and key data collection initiatives, resulting in their underrepresentation both in the field of data science and in many data sources used to make policy decisions. This absence from the data learning and gathering processes has far-reaching public health, political, social, and economic consequences, because we know that even in cases in which there is no or limited available data, there are still important health issues to solve. Inclusion of these populations in data training, collection, and use for social action are pivotal to community well-being.

Understanding these realities, we have spent the last decade working collaboratively with community members to train and find innovative, time bound, relevant solutions to some of the most intractable health issues facing communities. More specifically, though much of the general public health practices rely on evidence compiled by research scientists and other formal health care professionals, we have been laying the foundation for new forms of knowledge generation.

We have been training and equipping community members with the language and skills to understand, interpret, design, collect, and share data. We are working hard to reframe the current perspectives that suggest racialized minority or low-income groups are “hard to reach populations”; we instead suggest these groups and communities be understood as those “typically excluded using existing approaches.”

By focusing on increasing research and data literacy, allowing those most affected by health problems to be part of the development and testing of potential solutions, designing studies that fit in real-world settings with high potential for sustainability, developing meaningful measures for key outcomes, and interpreting research findings in appropriate community context, we are being intentional with the application of our skills. For us, as Black individuals with doctorates who have been educated and raised in largely immigrant, low-income, and urban communities, we are intimately aware of the ways in which knowledge and skill gaps can affect data interpretation, use of services, advocacy, and—most importantly—well-being.

The foundations of this work began with the Community Alliance for Research Empowering Social Change program in Long Island in 2009 and extended to the Community Research Fellows Training program in St. Louis, Missouri, (2012–) and Jackson, Mississippi (2014–).

Goodman has gathered an interdisciplinary team of researchers, community-based organizations, and community members to examine the health issues that disproportionately affect minority populations; developed and implemented culturally competent evidence-based interventions; and disseminated those findings to stakeholders—with the goal of improving minority health and eliminating racial/ethnic health disparities.

In 2018, we adapted the Community Research Fellows Training (for adults) to create the Youth Research Fellows Training (for middle and high school). This was a collaborative effort between New York University School of Global Public Health and the GrassROOTS Community Foundation—a public health and social justice organization that works primarily with Black women and girls.

We trained Black middle- and high-school girls who were participants in the GrassROOTS summer leadership program to become research and data literate partners with social scientists and policymakers so they can advocate for the needs of their communities and create social action campaigns around menstrual equity, green space, and mental health. Participants learned how to develop research questions and were introduced to basic research terminologies, methods, and design.

We were generally satisfied with these efforts, but things changed in the spring of 2020. The COVID-19 pandemic and racial reckoning in the United States underscored the importance of this work and pushed us to deepen our commitment to increasing these proficiencies. The result was the Quantitative Public Health Data Literacy Training (2020–).

The goal of this course is to increase data literacy and improve public health knowledge for youth and adults so they can make sense of the daily bombardment of quantitative information being shared by media sources and government entities. Additionally, we wanted to equip students with the ability to clearly articulate to their families and friends the meaning of the data.

We were seeking to enhance the data literacy of the populations likely to be experiencing the negative effects of the public health crisis and the racial onslaught. We wanted desperately to help these community members become data literate—have the ability to understand the contexts that produced the data they were seeing, be able to read and communicate data as information, and have the knowledge to gather and collect data.

Data literacy, therefore, involves understanding what data means, including how to read charts appropriately, draw correct conclusions from data, and recognize when data is being used in misleading or inappropriate ways. Data literacy requires an understanding of constructs, context, data sources, analytical methods, and techniques applied.

The number of applications demonstrates the clear need for data literacy skills. Over the past three years, more than 2,000 individuals have applied to the Quantitative Public Health Data Literacy Training and more than 500 have been trained. Qualitative findings show that these students are excited to learn the information, but are also eager to learn from Black women scholars. Moreover, many report they are interested in filling gaps in their understanding of data.

Too often, community members feel ill-equipped to challenge data findings when presented in the form of tables, graphs, figures, and maps. There are public health consequences for feeling un or under-prepared to challenge the stories told about their communities. We provide a space for these students to feel a sense of relatedness, which creates an environment for enhanced learning.

Educational research tells us students learn best when they feel their instructors understand their stories and are committed to their success. This approach to teaching data literacy has helped us communicate the value of being able to read, write, interpret, and present with numbers.

Allowing community members to tell their own stories is key for advocacy and equity. We pay particular attention to quantitative data; however, there remains a need for understanding the people behind the data. Having stories (qualitative data) that support quantitative findings humanizes the numbers and often provides greater impact.

We know data is and has been used to obfuscate and distort the realities of these communities. Data aggregation and summarization represent individuals’ lives. We therefore need community members to know their stories are embedded in these data findings and that exclusion of this interpretation is an ethical issue. Community members need to understand how to investigate why data is absent because, in so doing, they can understand whether exclusion occurred by chance or through systematic practices and how this may potentially bias estimates.

For this reason, the data and research literacy courses pay keen attention to research and data ethics. We emphasize the importance of being ethical in the stories you choose to tell and how you choose to tell them. Ethical issues around community data as it relates to ownership, sharing, privacy, identification, and commercialization have real-life implications.

New forms and types of data collection have created many ethical questions that remain unaddressed. The American Statistical Association supports the ethical use of statistics and data science to ensure policies and ideals do not harm, marginalize, and otherwise divide people and groups within our society.

Further Reading

The World’s Most Valuable Resource Is No Longer Oil, but Data

Training Community Members in Public Health Research: Development and Implementation of Community Participatory Research Pilot Project

Data Literacy

A Data and Analytics Leader’s Guide to Data Literacy

Determining Data Information Literacy Needs: A Study of Students and Research Faculty

The Role of Relatedness in Student Learning Experiences

Ethical Guidelines for Statistical Practice