What’s an indicator? For Theorymaker native speakers, it is just another Variable.
The fake science of logframes has led many to believe that every item in a Theory needs to have additional things called “indicators” - which have to be easily operationalised and measured. But:
- any such indicator is also a Variable. So if every Variable also needs indicators, do the indicators need indicators, which in turn need indicators? Of course not. That would be turtles all the way down.
- any ordinary Variable may well also be clearly measurable; additional information may not be necessary. There are plenty of examples such as the passing of a particular law.
- not every Variable needs measuring numerically; or at least, reducing it to numbers may not give it its best expression.
When we do find it difficult to record a Variable with sufficient accuracy, we may consider using a proxy Variable is used which is easier to measure and gives some information, but not perfect information, about the Variable of interest: and “Indicator”.
Average score on appropriate questionnaire ('indicator') Tolerant attitudes to other ethnic groups amongst young people (not measured directly)
In most cases we won’t have space to display these indicators as separate Variables, so we can list them under the Variable of interest. But doing so doesn’t change their status and doesn’t mean that every Variable needs to have indicators.
Tolerant attitudes to other ethnic groups amongst young people (not measured directly), Indicator=average score on appropriate questionnaire
Downstream proxy Variables as “indicators”
Nevertheless it can of course happen that we have an important Variable which is very hard to capture or assess. Empirical social scientists have long recommended trying to identify proxy Variables which are easier to measure and which are just downstream of the Variable in question.
We won’t here go into the (mostly very good) reasons such as accountability and transparency why we desire to have easily and cleanly accessible (“SMART”) Variables, but see xx.
With any use of proxy Variables, we can use Bayes’ formula xx to estimate the Variable of interest. Assuming we know how the proxy depends causally on the Variable of interest, this formula allows us to work backwards and do the reverse calculation.
Often, for reasons which are partly statistical, a bunch of related but distinct indicators are suggested, for example by selected different subsets of items from a questionnaire to form related subscales, e.g. “general tolerance”, “lack of threat perception”, “interest in others”.
Average score on sub-scale 1 of appropriate questionnaire ('indicator');Average score on sub-scale 2 of appropriate questionnaire ('indicator');Average score on sub-scale 3 of appropriate questionnaire ('indicator') Tolerant attitudes to other ethnic groups amongst young people (not measured directly)
Other kinds of measure other than questionnaires may of course also be used - any other Variable in fact, if it eases empirical verification.