Be a Data Champion! Introducing CDF-Ohio’s Data Resource Toolkit
February 2, 2022
By Morghan Hyatt, Data Policy Associate
When utilized responsibly, data can be a powerful contributor to social and systems change, stre
ngthening the foundations of our democracy by bolstering more equitable access to opportunity and information, reinforcing greater accountability, and driving decision-making for the greater good of all children, families, and communities.
The importance of data may seem clear to those who use it on a regular basis, but for those who may not be as comfortable with data, the information it holds may seem elusive and hard to understand or even interpret. Wherever you fall on the spectrum of data utilization experience and knowledge, CDF-Ohio’s new online tool, our Data Resource Toolkit, can provide the appropriate resources needed to not only use data but to present it in a meaningful way that tells a compelling story.
In the child advocacy space, data is used to measure all aspects of child wellbeing and gain greater understanding of how children are faring over a given period of time. The patterns, correlations, and connections that data reveals to be statistically significant help advocates push for ideas and proposals that are data-informed and based on concrete evidence of what our communities need. They serve as a roadmap toward greater equity for children and families.
Our new toolkit is intended to be an introduction on how to use data, be a responsible consumer of it, and use data in a way that builds greater equity in our communications, research, policy development, and advocacy day to day. It also serves as an online repository of reliable data sources for novice users to begin their research journey. The resource serves to easily direct users to public sources applicable to nearly any area of a child’s wellbeing, from child nutrition, to education, health, and more.
Depending on a policy goal or research purpose, analyses can be conducted in a variety of ways. For example, a user may be interested in geographical comparisons or comparing indictors across various demographic groups through data disaggregation. Once a user establishes the purpose of research, they’ll be ready to implement best practices for data utilization.
Here are five helpful techniques we recommend as starting points for working with data and navigating our data toolkit:
- Prepare a research question or an issue area that will filter the data
- Having a precise notion of where you want to look for your data narrows down your resources.
- Use credible and reliable resources that ensure the validity of the data
- A trustworthy source should display accurate data (meaning, bottom line, it should be able to be reproduced and thoroughly vetted).
- Create parameters to help define the data collection
- Define a period in time (finite), an amount of time (days, months, years), or a group/category (gender, race/ethnicity, age groups, etc.) to measure.
- A comparative analysis is successful when it distinguishes the differences amongst groups (disaggregated data) and provides communication towards the “why” (evidence toward the variations)
- Disparities persist amongst racial, ethnic, income, geography, linguistics, and other data.
- The “why” should aim heavily toward equity solutions.
- Know your target audience and contextualize the data for effective framing of the message
- Data alone can’t achieve political or social change, effective storytelling and contextualization gives meaning to the data.
- Data visualizations, infographics, issue briefs or blog posts can be useful ways to convey a message.
For example, let’s say a user is interested in learning more about Ohio’s rates of infant mortality:
- Craft a research question that states: How did infant mortality rates in Ohio’s 88 different counties compare or contrast in 2019?
- Determine the most appropriate and credible data sources needed to answer this question. The Data Resource Toolkit has multiple resources under ‘Health’ that can assist with finding that information.
- Create parameters for the data to capture a certain set of information. We are interested in infant mortality rates by county, let’s say our curiosity leads us to want to view this information in 2019. Now we have a category (mortality rates), a group (children under the age of one) a timeframe (2019), and an area of geography to compare (counties in Ohio). These parameters help us to filter our resources. The Ohio Department of Health and the KIDS COUNT County Profiles both have infant mortality datasets for children in Ohio. Referencing back to our parameters we are interested in the rate of infant mortalities by county, which can be found in the KIDS COUNT County Profiles
- Begin comparative analysis. This analysis could center on viewing the various counties in Ohio that have higher or lower infant mortality rates in 2019. Franklin County had an infant mortality rate of 6.9 compared to Hamilton County, 9.3.
- Dig into to the Context. Geographical differences and disparities in infant mortality rates could be contributed to several things and contextualizing the findings from your data will help to tell the full picture of potential issues that contribute to higher infant mortality rates in some counties versus others. (However, please note that correlation does not mean causation when giving analyses to findings within the data; therefore, the message should focus on ways to push for adequate solutions to build more equity within communities in Ohio.)
It is also critical to note that every message can be delivered differently depending on the audience, and in some cases, a data visualization could be more impactful than a blog post. Reference the Applying Racial Equity Awareness in Data Visualization or other resources compiled in the data toolkit to explore responsible data visualization and the most appropriate vehicle for conveying your findings.
Bottom line, data that is democratized, irrespective of technical capabilities, enables everyone to know how to work with data and feel comfortable with using it. With easy access to the Data Resource Toolkit, anyone can access resources to help better understand data usage, where to seek those resources, and provide tools for equity driven messaging. CDF-Ohio’s goal is that this toolkit serves as a valuable resource for anyone interested in data and research, promoting users to responsibly consume and use data- regardless if you’re a beginner or advanced.