As you’ve seen, logic models have a lot of value, but they do have limitations. It’s important not to think of them as a panacea or a cookie cutter to apply wholesale.
A logic model only represents reality; it is not reality.
- It is only as good as our understanding of the situation, the environment, the theory we are expressing, and our assumptions.
- Programs are rarely neat and orderly and the unexpected happens.
- Programs are not linear and rarely follow a sequential order.
- It does help articulate causal linkages, builds consensus, and identifies what and when to evaluate.
A logic model focuses on expected outcomes.
- We need to pay attention to unintended or unexpected outcomes: positive, negative, or neutral.
- We should think about alternative pathways of change, alternative outcomes, and the unexpected.
A logic model faces the challenge of causal attribution.
- A logic model depicts assumed causal connections, not direct cause-and-effect relationships. It does not “prove” that the program caused the effect.
- The program is likely to be just one of many factors influencing outcomes.
- Other factors that may affect observed outcomes should be considered.
A logic model doesn’t address the question: “Are we doing the right thing?”
- We need to consider whether what we are doing is the right thing separately from the logic model. Is what we are doing worth doing?