Let’s get straight to the point.
When logic models are viewed as a valuable planning and learning tool, rather than solely as a grant making reporting requirement, it increases the likelihood that your organization will use them. When logic models are viewed in connection to other tools you have at your disposal, that also increases a logic model’s utility.
The key to making actionable logic models –logic models that you will actually use — is to have a plan for how this logic model will be used.
Within this key are macro and micro considerations. Let’s start with the micro considerations:
Logic models from the micro perspective
When we zoom in on a logic model from a micro perspective, we see its components:
- Inputs (resources put into the program)
- Outputs (program activities and the direct result of the activities)
- Intended outcomes (divided into short-term , intermediate, and long-term)
One key to making a program logic model actionable, at the micro level, is understanding and identifying appropriate outcomes. When an activity’s purpose and its outputs are clearly defined, you can identify appropriate outcomes. Also, measurable outcomes are easier to create when we focus our outcomes on the program participants.
A simple way I teach this with my client organizations involves walking your program participants through the outcomes:
Short term outcomes: Your participant is enrolled in the program. They’re highly engaged and they participate in all activities. As a result of being highly engaged in the program, this participant is able to _______. Fill in the blank. To figure this out, you have to be observant. What are YOU noticing in this participant? Are you noticing any behavioral changes? Are you noticing any increases in their level of understanding? Was the participant unsure of themselves, but now is experiencing an increase in their confidence level? Did the participant believe one thing, but through the program their understanding changed? Short term outcomes are outcomes that are immediate. Not only are you noticing these outcomes, the participants are noticing them too.
Intermediate outcomes: The program is over, and the participant is no longer in the safety of the program. They’re no longer surrounded by other participants. There’s no more role playing, group discussions, videos to watch and react to, or whatever activities they participated in with your program. They now have to apply what they’ve learned to real world conditions. If you were to reach out to them in 6 months time and ask how are they applying what they’ve learned, what do you expect to hear? How are they navigating barriers? What aspects of the program have they found more helpful to implement? Intermediate outcomes are outcomes that stick with your participants the most. Out of everything you want them to experience, what do you want them to remember the most about your program, and how are they implementing this in real time?
Long-term outcomes: One of the biggest barriers to making logic models actionable is not acknowledging program and audience complexities. Despite understanding that there are many causes and effects within a program, we typically view logic models in a linear – and literal- sense. Logic models often convey that the program – and only the program – will lead to systemic change, which is unfair to the program and to the nonprofit. As you develop long term outcomes for your program, consider all the aspects that work together to get your participants to experience long-lasting change. That means, in addition to the actual program, the people and other programs your participants engage are working together to boost the effectiveness of what participants experience as a result of participating in your actual program. As a result of participating in your program, your participant are inspired to seek out other opportunities to learn more, to engage with others, to correct their behaviors, etc. What would these long term outcomes be for your program, their lasting impact? If the participant keeps implementing what they’ve learned, what are they expected to experience in a year’s time?
Another key to making logic models actionable at the micro level is to ask: How can we measure this outcome?
Each activity has an intended outcome. Your outcome should be something that can be realistically measured. As you’re creating your logic model outcomes, ask yourself, “How can we measure this? What indicators will we look for?” as these are questions an evaluator will ask.
Logic models from the macro perspective
Zoom out and view your logic model within the context of your other organizational tools. Ideally, these tools are working together to create a holistic, comprehensive approach to program development and implementation.
For example, a nonprofit may pair a logic model with a work plan. A work plan is a detailed roadmap that outlines the specific tasks, timelines, responsibilities, and resources required to implement the activities identified in the logic model.
The logic model ensures that the program’s conceptual design aligns with the organization’s mission, while the work plan provides the practical guidance necessary to bring the program to life. This approach enhances the chances of achieving the desired outcomes and maximizing the positive impact of your work.
Ultimately, the most important aspect is that your organization is deliberate and intentional in your program planning and implementation. Whether you prefer work plans, logic models, or a combination of both, or something else, the key is to have a clear understanding of your goals, how you plan to achieve them, and how you’ll measure learning and success.
Key takeaway
We increase the likelihood of using logic models when we view them as a valuable planning and learning tool. Understanding logic models from a micro level and in connection to other organizational learning and planning tools, we recognize logic models’ value in supporting your program’s development and implementation.
Raise Your Voice: How do you make your logic models actionable? Share your thoughts below in the comments section.
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