The Magic Eye of Data

Editor’s note: Last year, in partnership with the Consumer Health Foundation, the Meyer Foundation commissioned a report and digital feature produced by the Urban Institute on the state of equity in DC. A Vision for an Equitable DC was only the beginning of an in-depth look at equity. The report used data from the U.S. Census Bureau’s American Community Survey to provide insight into the circumstances of DC residents. Inconsistent ward-level data and the low number of residents from some racial and ethnic groups prevented the digital feature from fully conveying the current state of equity for all DC residents.

A Vision for an Equitable DC focused on the city’s sizable black and Hispanic populations. Such research limitations are not uncommon in studies of populations, and they unfortunately only tell part of a larger story. Six percent of DC’s population (more than 35,000 people)—including Asian, Pacific Islander, American Indian, and residents identifying as other or multiple races—were not reflected in our data, reinforcing a false perception that poverty in DC is experienced exclusively by black and Hispanic residents.

Neel Saxena, executive director of Asian American LEAD, offers his thoughts below on how studies based on race often exclude groups—many of whom experience considerable inequity that should be part of the conversation.

---

GUEST POST
BY: NEEL SAXENA

---

In the early 1990s “Magic Eye” books swept the country and kept people’s noses glued to the page as they attempted to view the three-dimensional images that emerged from complex two-dimensional patterns. The pages were filled with diverse colors and forms. If you followed the directions and could diverge your eyes, an image appeared to jump out at you. But what of the rest of the colors on the page? After people saw the image, did they go back and see if they missed anything or consider how those other colors and patterns shaped the 3-D image they saw?

This example of directing an individual to an image is the challenge a reader faces today, where presentation of significant data often leaves out other, equally relevant information. Creating a narrative through one lens using data plays on our thirst for bite-sized morsels of information to guide our perspectives. It is important to expand the depth and breadth of the images we create through data by understanding the limits, finding alternatives to the selected data source, and properly communicating a full narrative. The challenges with data and communicating about data are exacerbated when exploring small populations, but the work remains a critical aspect to accurately inform racial equity efforts.

While I applaud initiatives like A Vision for an Equitable DC, I find that this study and similar studies examining populations must look beyond limitations to be more inclusive of diverse communities and the barriers they face.

Understand the limits

The challenges around gathering data for small populations include the methodology of data collection and the statistical significance of the data. For example, 2,197 languages are spoken in Asia. If even one percent of these languages are found in the DC metro area, that is still 22 different languages that would require translation and interpretation of data collection tools. Language barriers are not the only hindrance to participation in typically English language data collection tools. Sociocultural barriers impact participation in surveys, focus groups, and other data collection mechanisms, too. In collecting data on smaller populations, one should take a hyperlocal approach to get an accurate reflection of the community. Corporations have taken this approach around marketing to small communities. However, research and policy data collectors are often stymied by costs, time constraints, and lack of interest or understanding among sponsoring entities.

In the report-writing process—particularly when looking at racial equity—it is important to consider where and how data is collected; examine the definitions around race; and determine if there are large inconsistencies between nationally-collected and locally-collected data. The social construct of race that divides our population can make it challenging to even know who a report is discussing. Providing this clarity is critical. Sometimes, whether the data source even seems feasible is debatable. For example, if you live in a community where you see a large Eritrean population living and working, but they do not pop up in your national data report, there is likely a disconnect somewhere.

Far too often, the needs of smaller communities who do not have the numbers get overlooked, and stereotypical portraits are painted by popular media or biased historical context.

Set the scene

Just as important as having reliable and complete data is how one communicates and presents the data. In the conversation around racial equity, a goal should focus on needs beyond the numbers. Far too often, the needs of smaller communities who do not have the numbers get overlooked, and stereotypical portraits are painted by popular media or biased historical context. The reader has no sense of any of these data challenges; they are looking for instant data gratification and not a “choose your own adventure” footnote mission to learn more.

In talking about race, the delicacy of word choice is the most challenging aspect of reporting. How will the reader interpret the message? Authors must take care in presenting data to not paint a broad stroke, but to focus reporting on the available data and acknowledge data limitations. When using national data to report on local-level issues, supplementing the national data with data collected on the local level can provide context. School data, local government services data, data from foundation grantees, and other local administrative data can provide information on small populations missed in national efforts.

Converge perspectives

While the “Magic Eye” approach focused on diverging one’s perspective to see the prominent image within the larger pattern, a convergence approach is one where a broad narrative includes specifics that highlight small populations and considers the impacts of leaving these populations out. When it comes to data collection and racial equity work, the details matter. Consider all the data-driven reporting and research around racial equity over the past 30 years and ask yourself how much of it included a once thriving Chinese and Chinese American population in DC. Were their needs communicated? Did policy decisions consider this population, or were they lumped into “other?” Our society thrives on data, and thus, there is an increased responsibility to present it in a way that is precise and provides the community information to make informed decisions about their collective future.

Reflect

So, what can Meyer and other organizations do differently? First they must look internally: is diversity just talked about or is it truly embedded within the organization? Is it reflected in the initiatives they support and the activities they participate in? Are there diverse voices at the decision-making table? Including more diverse voices at the decision-making table for A Vision for an Equitable DC could have kept the realities of small populations in the forefront rather than the periphery.

Second, they must look to innovate when it comes to reporting on data. Funders can look to increase both financial and technical support to their grantees around data collection, analysis, and reporting (simply shifting responsibility to the grantee would not be innovative). Organizations that analyze data can innovate by incorporating multiple data sets into their analysis. A typical response to these suggestions is, “what are the costs to the organization?” But the question should be, “what are the costs to the communities who are left out and overlooked?” Hopefully, these actions will take the country by storm like the “Magic Eye” books.