What are effective strategies for testing assumptions in a logic model?

1 Define your assumptions

The first step is to identify and articulate the assumptions that underlie your logic model. These are the beliefs, expectations, or hypotheses that you have about how your program will work and what results it will achieve. For example, you might assume that your target audience will be interested in your message, that your activities will be accessible and relevant, that your outputs will lead to desired outcomes, and that your outcomes will contribute to your impact. You can use a simple format to write down your assumptions, such as "If we do X, then Y will happen because Z."

2 Collect and analyze data

The next step is to collect and analyze data that can help you test your assumptions. Data can come from various sources, such as surveys, interviews, focus groups, observations, documents, or records. You should choose the data collection methods that are appropriate for your program stage, budget, and scope. You should also define the indicators that will measure your outputs, outcomes, and impact. Indicators are specific, measurable, achievable, relevant, and time-bound statements that show the progress and results of your program. For example, an indicator for an output could be "the number of people who attended our workshop", while an indicator for an outcome could be "the percentage of participants who increased their knowledge of our topic".

3 Compare and contrast data

The third step is to compare and contrast the data that you collected with the assumptions that you made. This will help you see if your logic model is accurate, realistic, and valid. You can use different techniques to compare and contrast data, such as graphs, tables, charts, or matrices. You should look for patterns, trends, gaps, or discrepancies that can confirm or challenge your assumptions. For example, you might find that your output data matches your assumption, but your outcome data does not. This could mean that your activities are effective in reaching your audience, but not in changing their behavior.

4 Adjust and refine your logic model

The final step is to adjust and refine your logic model based on the data that you analyzed. You should use the evidence that you gathered to revise or modify your assumptions, inputs, activities, outputs, outcomes, or impact. You should also document the changes that you made and the reasons behind them. This will help you improve your logic model and make it more relevant, reliable, and robust. For example, you might change your activities to make them more engaging, or add new outcomes to reflect your broader impact.

5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?