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?

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