As u/Probablynotabadguy says, these can be context dependent. In my area of study I would translate the terms as “research” and “explanatory” variable.
The Research, or Output variable, is what you’re looking to understanding – say, the grades of students. The Explanatory, or input variable, would be what you’re trying to show is correlated to the output, say for example time spent on Social Media.
Output might the average height of a population, input might be diet.
Output might be rates of home ownership, input might be average income.
The next step in your lesson might be causation vs. correlation. For example, the rates of drowning in the US is correlated to the release of movies starting Nicholas Cage, but clearly the movies aren’t *causing* the drownings.
IPO Model (Input Process Output).
Well first we need a topic of research. Something we don’t know, that we want to try to work out.
In this case, I want to peel bananas 15% faster. Because bananas are cool. And this is important.
Firstly, we need some input for the research. In this case, it will be bananas. How many bananas? Who knows. Lets use a common metric of “lots”. Lots of bananas. Do we have any other important banana related information to take into consideration? Throw it in here if we do. (Feedback can become input too)
Right, now we do something with the bananas. Whatever we do with them, that’s the process. Do we want a control group that is only allowed to peel them from the top? What about only from the side? Will we get in trouble if don’t let Kelly have any of the bananas? (Damn ethics. And damn Kelly)
Now that the process is over, we review what was done, and we check to see if we have our desired output: a 15% increase in banana peeling speed. If not, take the feedback from the process… start again.
I realise the question is to “define” the variable, but really they can almost anything, tangible or intangible. And an output is effectively a result.
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