7 Common Mistakes When Writing a Logframe (and How to Avoid Them)

The logical framework—commonly known as a logframe—is a foundational tool for designing, managing, and evaluating programs. It’s widely used by development agencies, NGOs, and funders because it helps map out exactly how a project’s activities lead to its intended impact. When a logframe is done well, it offers clarity, accountability, and a strong backbone for both implementation and reporting. But too often, people rush through the process or misunderstand key principles, leading to vague goals, unrealistic expectations, or mismatched indicators. These mistakes don’t just make the logframe hard to read—they can also undermine the success and credibility of the entire program.

To help you avoid those pitfalls, here are seven of the most common mistakes people make when developing a logframe, explained in depth using the example of a primary school reading program aimed at improving literacy for students in Grades 5 and 6.

1. Not using a SMART goal or outcome

One of the most fundamental errors in a logframe is failing to ensure that your goal and outcomes are SMART—that is, Specific, Measurable, Achievable, Relevant, and Time-bound. Without these criteria, your goal becomes an aspirational statement rather than a clear target.

For example, stating a goal like “Increase in primary students graduating to high school” lacks the precision and accountability that donors and implementers need. It doesn’t specify which students, how many, or by when. Compare this to a SMART goal: “Achieve a 10% increase in Grade 5 and 6 students progressing to high school within three years.” This version outlines exactly who is targeted, what level of change is expected, and when the change should occur. It provides a measurable outcome that can be tracked and evaluated over time.

When a logframe lacks SMART goals, teams often struggle to measure success, communicate clearly with stakeholders, or make data-informed decisions. Making your goals SMART is the first step to creating a logframe that actually drives results.

2. Links between levels are not logical

Another major issue arises when the relationships between the different levels of the logframe—activities, outputs, outcomes, and the goal—don’t make logical sense. Every level should naturally lead to the next. If your activities are fully implemented, they should logically result in the stated outputs. Similarly, your outputs should realistically produce the outcomes, and the outcomes should lead to your overarching goal.

For example, if your activity is to run five summer reading camps with 30 students each, then listing an output of 500 students completing a summer camp is mathematically impossible. That’s a total of only 150 students—not 500. This kind of mismatch undermines confidence in your program logic and suggests poor planning. A more appropriate approach would be to either increase the number of camps or the number of participants per camp to ensure the numbers line up. These logical connections are what make a logframe more than a checklist—they form a chain of reasoning that demonstrates your program is both intentional and feasible.

3. Expected outcomes or goal are not realistic based on the outputs

A third and very common mistake is setting goals or outcomes that are unrealistic given the scale or nature of your outputs. For instance, expecting a 100% improvement in reading proficiency from a short-term intervention like a summer camp—combined with minimal follow-up at home—is not just ambitious, it’s impractical. Without evidence or historical data to back up such claims, these kinds of targets can set your program up for failure.

When results fall short, it’s often not because the program was poorly run, but because expectations were never grounded in reality. Instead, your outcomes and goals should be informed by your theory of change, relevant research literature, and evaluations of similar programs. For example, a more realistic target might be a 20% increase in reading proficiency over a year, supported by data showing that this level of impact is typical for similar programs. Overstating what your project can achieve may help in the short term—like getting funding—but it often backfires in the long run when those promises can’t be kept.

4. Activities do not have corresponding outputs or outcomes

As a program grows in complexity, it’s easy to lose track of whether every activity in your logframe is connected to a meaningful output or outcome. In larger tables that span multiple pages, it’s common to see activities that float on their own—listed without any direct result attached. For example, you might include an activity like “Train five Grade 5 and 6 teachers on reading strategies”, but if there is no corresponding output such as “Five teachers implement new reading techniques in the classroom”, then that activity is effectively invisible when it comes time to evaluate results. This is a problem because unlinked activities can’t be measured or managed, and it raises questions about whether they’re truly necessary.

Every activity should contribute directly to a change in behavior, knowledge, or conditions—and that change should be reflected in the next column of your logframe. Creating those connections ensures that every component of your program contributes to its overall success.

5. Risks and assumptions are not identified at every level (except the goal)

Many logframes omit or downplay the risks and assumptions that underlie each level of the results chain. While it’s true that the goal level doesn’t require risks and assumptions—since there’s no level above it to depend on—every other level absolutely does. These assumptions spell out the conditions necessary for one level to lead to the next.

For example, if your outcome is “Improved reading proficiency”, an important assumption might be that “Improved proficiency leads to greater self-confidence and retention in school.” If that assumption turns out to be false, the outcome may not lead to the goal as expected.

Identifying assumptions helps you recognize areas of uncertainty and plan accordingly. Drawing on your theory of change, prior experience, and research can help you surface the assumptions you’re unconsciously making. When they’re left unstated, those assumptions can quietly derail your program—especially if funders or stakeholders are relying on a chain of logic that only exists in your head.

6. Indicators do not match the goal, outcome, output, or activity

Even when your logframe’s logic is solid, problems can arise when the indicators you use don’t accurately measure what they’re attached to. This happens often when indicators are chosen quickly or pulled from templates without careful thought.

Consider this example: the output is “500 parents support home reading”, but the indicator is “Number of books in the home.” While book availability is helpful, it doesn’t actually tell you whether parents are supporting reading. A better indicator would be “Number of parents who report reading with their children at least three times per week.” This directly reflects the behavior you’re aiming to influence.

Indicators should be carefully selected to measure exactly what each level of the logframe is meant to achieve. Misaligned indicators won’t just confuse your team—they’ll make it difficult to monitor progress or demonstrate success to funders.

7. Indicators do not match what is written in the M&E framework

Lastly, a technical but serious issue: misalignment between the logframe and your Monitoring and Evaluation (M&E) framework. Even if your logframe indicators are well-written and relevant, they can still cause problems if they don’t match the ones listed in your M&E framework. For example, your logframe might use an indicator like “Percentage of students improving literacy scores”, while your M&E framework lists “Number of students attending reading workshops.” These are not interchangeable—they measure different things, using different methods.

This kind of misalignment often happens when different teams write the logframe and M&E plan, or when updates are made to one document but not the other. The result is confusion during reporting, missed data points, and a weakened ability to analyze results effectively. To avoid this, always ensure that your logframe and M&E framework are developed together, and that all indicators use the same definitions, data sources, and reporting timelines.