Assumptions: What is going on that causes you to want to intervene? The point of assumptions is to establish a baseline—a measurable general perspective, view, and logical starting point of the collaborating body developing the OLM. Assumptions are not directly tied to each row, but they are about the evaluation you conduct at the conclusion of your intervention. They provide points of comparison in the context of the outcomes and impacts you present in the evaluation.
Assumptions can be facts compiled from research. Most people think they need the facts or other verifiable information. They think these are the only currency of value in building the outcome logic model. Opinions are important as well. These are the thoughts that are based on the facts. We recognize that facts are one thing, but how you interpret those facts impacts your course of action and the interventions you construct. Again, we are talking about what made you want to do something. Your impetus involves more than just facts.
Beliefs or feelings are germane to the process of developing assumptions. It may not be a real fact that extreme poverty exists on the Southside of Los Angeles. But something you observed caught your attention, caused the formation of an assumptions, and motivated you to address it. It could be as simple as driving through. You notice a couple of tent cities and squalor. You say to yourself, “I would like to brighten up this place with something.” The validity is not as important as tracing where it originated. When establishing a baseline, you want to explain where your beliefs are coming from. Is it based on evidence that is researched or observe or, is it just a belief you have? If no evidence exists, we need to drop the assumption. But opinion based in evidence is enough.
Some mention statistics as if they are a separate category of assumptions. They are typically going to be facts, but they are selective and often interpreted (read biased) in how they are presented. A proper baseline will report the statistic along with the methods that were implemented to arrive at the answer. Without these, statistics without context are beliefs without evidence.
The reason for facts, opinions, and statistics is to establish the baseline. For example, if we are exploring a plan to address childhood hunger, we want to establish the baseline of childhood hunger prior to our intervention. After our program has been operational for year, we want to say that hunger is decreased by a certain percentage. We want to be able to quantify our assertions. We compare our outcomes with the evidence we cited at the beginning.