Living with fuzziness
Variables like “my son’s mood” and “the findings of the report” are real, fully-fledged Variables just like Germany’s GDP. They are not waiting to become Variables when some scientists get around to inventing a way of measuring them objectively. Theorymaker recognises that, beyond a few islands where hard, valid and reliable numbers are available, most of the time people, including evaluators, have to make judgements on the basis of vague, fuzzy and poorly defined data which may be nothing more than information like “most stakeholders thought the project was more successful than last year”.
If you want to evaluate a project, you have to be able to formulate ideas about how it works, and usually that means getting down and dirty with fuzzy Variables and fuzzy Rules. Using Theorymaker helps evaluators write down and deal with Variables which are hard to even define, let alone measure. Theorymaker is flexible and generous enough to be useful in actual evaluation contexts with this kind of data.
Example of a fuzzy Rule between two fuzzy Variables: The child copies the teacher’s movements.
child's movements ((fuzzy)) !Rule:fuzzy !Rule copies teacher's movements ((fuzzy))
Variables can be fuzzy in different and often overlapping ways. For example, sometimes we imagine there is a genuine numerical Variable hidden in all that fuzziness somewhere, but we are not sure how to get it. In other cases we think the reality is just essentially fuzzy; we might or might not be able to capture it using one or more numerical Variables.
You can get robust inter-subjective agreement about my son’s mood, i.e. if you gave a few people who know him, a 1-5 scale with 1 being “like the worst few hours in his typical week” and 5 being “like the best few hours in his typical week”. In this case, we could say that this 1-5 scale is a non-fuzzy Variable because it is assigned a fixed set of Levels (1 to 5), whereas the original mood is an fuzzy Variable because it isn’t.
Often, the best way to get a grip on what a Variable is, is to show it being used in an explanation.