Start With the Data You Need
Collecting and understanding data are essential aspects of evaluation, regardless of the stage of your grant cycle (e.g., first, second, or third year). Your evaluation plan, questions, and logic model provide a blueprint for the types of data that need to be collected and from whom. Not all evaluation data comes from a new survey or complicated data collection effort. Depending on your evaluation questions, useful information may already exist within your project or institution.

Where Can Your Data Come From?

- Faculty and staff
- Industry partners
- Students and alumni
- K-12 schools
- Community, state, or national datasets
- Local non-profits
Working with Institutional Research Offices
If you’re looking to use data on student enrollment, persistence, or graduation, one of the most important relationships to begin building early is with your institutional research office. They can often provide student and program data that is relevant to your project and its evaluation. This guide on requesting data from institutional research offices provides an overview of information to ask, including whom to ask if you do not have an institutional research office.
Types of evaluation data
Your evaluation questions should guide the type of data you collect. There are different types of data that can be collected from these sources.
Data collection can involve both qualitative and quantitative questions, even for the same event or instrument, like a survey.
Data may be collected through methods such as:
Surveys
Site visits
Observations
Interviews
Focus groups
Secondary data
You may work closely with your evaluator to not only identify what data to collect to answer your evaluation questions, but also to build data collection instruments, as this blog discusses the benefits of doing so. This could look like co-creating a survey to capture complex constructs, like students’ attitudes or beliefs, or refining focus group questions together.
Making sense of what you collected
It’s important that evaluations not only collect data but also support the interpretation of that data. We also call this process sense-making. This step turns information into learning, recommendations, and decisions.
A few ideas for making sense of data include:
Compare results to your goals
Using project goals, objectives, or performance targets to understand progress
Use evaluation rubrics
Apply clear criteria to assess the quality or progress of your work
Make meaningful comparisons
Compare results with relevant benchmarks, previous results, or other meaningful points of reference
Make sense of data together
Engage project team members, partners, participants, or other interest holders in interpreting findings and considering what they mean for the project.




EvaluATE is supported by the National Science Foundation under grant number 2332143. Any opinions, findings, and conclusions or recommendations expressed on this site are those of the authors and do not necessarily reflect the views of the National Science Foundation.