Outcomes, Outputs, and More: A Nonprofit's Guide to the Evaluation Words That Sound Scarier Than They Are
There's likely one question quietly sitting in the back of your mind, whether or not you've admitted it to yourself yet: how are you going to convey your results to donors and funders?
Oh, you weren't thinking about that? You were busy running programs, managing staff, and doing about nine other jobs at once? Fair enough. But now that we've brought it to the front of your brain, let's talk about it.
Most evaluation terminology is not complicated, it just sounds complicated. Once you've got the vocabulary, the ideas underneath it are things you already understand instinctively from running your programs day to day.
We're going to walk through the terms that come up most often when talking about Impact Evaluation, in plain language, using examples from real-life organizations so hopefully by the end you'll feel confident enough to go after that grant application and start digging deeper into the world of Impact Evaluation!
Outputs: the easy stuff to count
Outputs are what you did and who you reached. They're the hard numbers, the ones you can pull straight from a spreadsheet. How many people came through your program. For example: How many books your literacy nonprofit handed out, how many meals served, how many workshops held, how many clients on your caseload this quarter.
Outputs are fairly easy to measure: how much, of what, and how consistently. Funders like outputs because they're clean and comparable. But outputs alone only tell half the story, and it's the less interesting half.
Outcomes: what actually changed
Outcomes are blurrier around the edges, and that's exactly why they matter more. An outcome is the change that happened because of what you did, not just the fact that you did it. For example: Did a client's reading level improve? Did a family report feeling more supported?
That's harder to measure than counting attendance, but it's the part a donor actually cares about, because it's the part that answers "so what."
Impact: the outcomes, zoomed out
Impact is where things get confused most often, because people use it interchangeably with outcomes. They're related, but not the same. An outcome is a specific change for a specific person or group, usually something you can see within the life of your program. Impact is the bigger, longer-term shift your organization exists to create, the reason you're doing any of this in the first place.
If your outcome is that program participants report improved food security this year, your impact is the broader reduction in hunger and its downstream effects on health and stability in your community over time. One person's outcome is a data point. A shift in the neighborhood over five years, that's closer to impact.
Theory of Change: your "if this, then that"
A theory of change is simply the logic connecting what you do to why you believe it works. If we provide X, then Y will happen, because of Z. It doesn't need to be a 40-page document. At its core, it's one sentence you should be able to say out loud: if we give kids access to books and reading support, then their literacy will improve, because consistent practice with a supportive adult is what builds reading skill.
Every program has one, whether it's been written down or not. Half the value of putting it into words is finding the part you've been assuming without evidence.
Logic Model: the theory of change, mapped out
If a theory of change is the sentence, a logic model is the diagram. It lays out your inputs (staff, funding, time), your activities (what you actually do), your outputs (what gets produced), and your outcomes (what changes as a result), usually in one visual flow.
People tend to dread building one, but a logic model is less about paperwork and more about catching gaps before a funder does. It forces you to answer: does this activity actually connect to that outcome, or are we assuming it does? Half the value of a logic model shows up in the conversation you have while drafting it, before anyone ever reads the final version.
Baseline: where you started
A baseline is your starting point, the measurement you take before your program does anything, so you have something to compare against later. Without a baseline, "improvement" is just a feeling. With one, it's a number you can defend.
If you want to know whether your workshop improved participants' confidence, you need to know how confident they said they felt walking in the door. Skip that step and you're left guessing at whether change happened at all, which is a hard position to be in when a funder asks for evidence.
Indicator: the specific thing you're tracking
An indicator is the specific, measurable sign that tells you whether an outcome is happening. If your outcome is "improved financial stability," your indicators might be something like the percentage of clients who report having an emergency savings fund, or a drop in the number of clients reporting overdue bills.
Indicators are where a lot of evaluation plans quietly go wrong, either by picking something too vague to measure or something that doesn't actually reflect the outcome it's supposed to represent. A good indicator is specific enough that two different people, looking at the same data, would agree on whether it moved.
Mixed Methods: numbers and stories, together
Mixed methods evaluation means combining quantitative data (numbers, counts, statistics) with qualitative data (interviews, open-ended survey responses, stories). Neither one alone gives you the full picture. Numbers tell you what happened. Stories tell you why, and to whom it mattered.
If your data shows a 20 percent increase in program completion, that's useful. But a participant explaining, in their own words, what changed for them because they finished the program, that's what turns a statistic into something a funder remembers.
A quick reference, so you don't have to scroll back up:
Output: what you did, in countable terms
Outcome: what changed as a result
Impact: the larger, longer-term shift your organization exists to create
Theory of change: the logic connecting your work to the change you expect
Logic model: that logic, mapped out step by step
Baseline: where things stood before your program started
Indicator: the specific thing you measure to know if an outcome happened
Mixed methods: combining numbers with stories for the full picture
None of this is designed to make evaluation feel like a second job on top of your actual work. It's meant to do the opposite: once you know what each term is actually asking for, building a plan around them gets a lot less intimidating, and a lot more useful.
Need a hand connecting your program's data to a strategy for evaluating the impact of your programs? That's the work we do at Mockingbird Analytics!