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Logic models are the strategic backbone of effective philanthropy—they're how funders and nonprofits map the journey from investment to impact. When you're designing a grant strategy, evaluating a program, or defending a funding decision, you need to understand how each component connects to the next. You're being tested on your ability to think causally: if we invest X resources and do Y activities, we should see Z changes in the community.
The real skill isn't memorizing definitions—it's understanding the theory of change that logic models represent. Can you identify where a program's logic breaks down? Can you distinguish between what an organization does versus what changes because of that work? Don't just know what inputs and outcomes are; know how they connect, where assumptions hide, and what external forces can derail even the best-designed initiative.
Every program starts with what's available. These components represent the raw materials and underlying beliefs that shape everything else in your logic model.
Compare: Inputs vs. Assumptions—both exist before activities begin, but inputs are tangible and countable while assumptions are beliefs that may prove false. Strong logic models make assumptions explicit so they can be tested and revised.
This is where strategy becomes execution. Activities and outputs describe the work itself and its immediate, countable products.
Compare: Activities vs. Outputs—activities describe what you do while outputs count how much you did. A common mistake is confusing outputs with outcomes: "500 people attended our workshop" is an output; "participants increased their financial literacy scores" is an outcome.
Outcomes are where impact lives—but change happens in stages. Understanding this progression is critical for realistic evaluation design and honest reporting.
Compare: Short-term vs. Long-term Outcomes—short-term outcomes (knowledge, attitudes) are necessary precursors to long-term outcomes (systemic change), but they don't guarantee them. A program can successfully change attitudes without changing behavior or community conditions. Strong logic models show the pathway connecting these stages.
No program operates in a vacuum. These components acknowledge that external context shapes what's possible—and what you should realistically expect.
Compare: External Factors vs. Assumptions—both represent uncertainty, but external factors are observable conditions outside your control while assumptions are internal beliefs you can test and revise. Smart philanthropists monitor external factors and update assumptions accordingly.
| Concept | Best Examples |
|---|---|
| Tangible resources | Inputs (funding, staff, data) |
| Underlying beliefs | Assumptions (causation, engagement, sustainability) |
| Program execution | Activities (interventions, training, outreach) |
| Countable products | Outputs (attendance, services delivered, reports) |
| Immediate participant change | Short-term outcomes (knowledge, attitudes, satisfaction) |
| Behavioral and relational change | Intermediate outcomes (new practices, partnerships, conditions) |
| Lasting impact | Long-term outcomes (systemic change, population indicators) |
| Contextual forces | External factors (economy, policy, demographics) |
A nonprofit reports that 200 families attended their financial literacy workshop. Is this an output or an outcome? What would need to change to make it an outcome?
Which two logic model components represent uncertainty—one that's internal and testable and one that's external and observable? How should a funder respond to each?
Compare activities and intermediate outcomes: Why is it a red flag when a grant proposal focuses heavily on activities but provides vague outcome language?
A program successfully increases participants' knowledge about healthy eating (short-term outcome) but sees no change in actual eating behaviors (intermediate outcome). Where in the logic model did the theory of change break down, and what assumptions might need revision?
If an evaluation question asks you to assess whether a program is "working," which components would you examine first—and why might focusing only on outputs give a misleading answer?