Food Systems Data Integrity Considerations

This project team encourages users to consider the information outlined below to help ensure the accuracy, completeness, and consistency of data as they use, analyze, interpret and share their work. These Data Integrity Considerations* are meant for anyone who plans to utilize the data shared on this website, collect additional data for their own projects or provide funding for others to collect data.

Funding Sources for Data Collection

  • Collecting data can be an expensive process. Many researchers must seek outside funding to support their research. Funding for this kind of work can come from federal, state, local or tribal governments, non-profit foundations, private companies, individual donors, universities, etc.
  • Each funder, no matter their affiliation, has some sort of motivation for funding a particular research project. It is important for one to consider the potential motivations and priorities of the funder and also determine the level of influence that particular funder has on the research that is being conducted.
  • Work your way through the next 6 sections of this framework (motivation; project design; data collection and usage; analysis; conveying your findings; and, communication and distribution) with the following consideration – is this designed in a fair and transparent manner with the only motivation being accurate and thorough data collection or could the funding source have influenced the development of the project to best fit their desired needs?

Motivation

  • Be clear about your motivation for engaging in data analysis. Are you applying for a grant? Providing a progress report? Providing information to your community? Advocating for a topic of interest? Identify your “why”.
  • Clarity around your motivation for data analysis also helps manage the complexity inherent in food systems. Many of us are examining multifaceted problems so it is important to keep individual data analysis projects focused on your specific “why”.
  • Seek to develop shared project goals in partnership with community members. Shared motivations can help to facilitate authentic community engagement.

Project Design

  • Make sure you are using a food systems lens when designing your project—that is, how might current food systems challenges or successes impact the way in which you design your project or research?
  • Consider the programs, policies or groups of people you might seek for the study and tailor your project and data collection strategies appropriately:
    • If researching a specific program or policy, when developing your data collection strategy or determining your target population, consider: Who has used this program successfully?  Who might have been excluded from this program in the past? How might specific policies have discouraged individuals from participating in certain programs or have impacted their ability to access resources (i.e. farm loans, insurance, access to farmable land)?
    • If researching a specific group of people: What are their time constraints/availability? Would they feel most comfortable speaking with someone by phone, through email, in-person, as part of a focus group? Would it be better for a trusted community member to interview this population instead of a researcher?
    • Use community advisors who can help to ensure that community input is gathered at all stages of the project and in a way where the community feels actively engaged and heard through the process.

Data Collection and Usage

Our research briefs offer recommendations on which additional data measures would be most useful to collect/construct to further inform the food landscape.

Primary Data Collection

  • Seek to include community members in the design of your instruments and in your data collection process. Consider partnerships with community organizations who are well-positioned to assist with recruitment and training.
  • Take steps to use plain language and be concise and clear with the wording of your survey questions. Be sure not to make any assumptions about the sample population when developing your questions.
  • Seek out data collection procedures that respect cultural traditions. This might include the use of interviews or focused groups, which allow for oral history and storytelling, rather than a survey.
  • Take steps to reduce community participant burden. This includes reducing the time required for participation and providing adequate compensation for time involved.
  • Engage with community members or organizations you are wishing to research. Tell them about the goals of your research and learn about their goals and needs. Design a mutually beneficial project and once the research has been completed, return to the survey participants with the research or tools that could help the community further their goals.
  • Only include questions in your survey that are relevant to your work or project. For example, do not ask about income level or level of education if it is not a data point that is pertinent to your research question.

Secondary Data Usage

  • Take time to acknowledge which populations are represented in the existing data, which populations might be underrepresented, and which populations were left out entirely.
  • Consider supplementing your secondary data with the collection of new primary data (see above).
  • Be sure to consider and adhere to the laws of data sovereignty by understanding who owns the data, the intentions of the original project, and how the data is meant to be used. 

Analysis

  • Depending on your project, quantitative data analysis may range from calculating averages to developing complex models. Regardless of the type of analysis you conduct it is important to be clear about exactly what you did to the data.
  • If you utilize a complex model your work may require simplifying assumptions. Strive to describe how you selected your analytical approach and developed these assumptions.
  • Take steps to disaggregate data for different populations and sub-groups where possible. Aggregation of data can mask important differences that might be relevant for understanding needs and crafting adequate program and policy solutions.
  • Clarify how decisions you make regarding analysis impact your results and describe the analytical processes in plain language.
  • When using and analyzing data sets, try to ground truth it by looking at other research previously conducted in this area or reviewing related peer-reviewed journal articles. Is it consistent with other research? If not, is there an explanation for the inconsistencies?

Conveying Your Findings

  • Be transparent about the limitations of your findings and where there are data gaps.
  • Consider supplementing the data analysis with narratives from community members (using data and stories) that illustrate experience and impact where possible. An example of this can be found at Stories from the Field.
  • Consider the effects of root causes or the core issues that are contributing to the observational data trends and conditions. Be sure to include them in your narrative. Throughout this data warehouse there are “Narrative Matters” sections within our research briefs for each of the data categories that provides additional guidance on this topic.

Communication and Distribution

  • Consider multiple report formats and communication channels to ensure that your work can be seen by all audiences.
  • If you collected primary data, return to the people you surveyed to share the data and analysis. Consider providing the opportunity for survey participants to provide feedback on the analysis, it is possible that they could add another viewpoint that could enhance your interpretation of the data.  
  • Describe your standards, how your results came to be (e.g., data analysis, summarization, qualitative analysis), and how you’ve incorporated Data Integrity Considerations into your work.

Additional Considerations for Funders of Research Projects

  • Look to simplify the application process for researchers seeking funding for their work by using plain language and low-tech options.
  • Consider using application criteria that includes the participation of community members as a requirement and that also weights equity criteria appropriately during the review process, so that “equity points” on an application can effectively tip the scale, rather than simply check a box.
  • Distribute funds in a manner that respects the value of community partners’ knowledge and time commitments. (Often times the community partners receive very little compensation in comparison to research partners.)

*The categories outlined below are based on the 7 categories identified by We All Count

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