There are four things to think about here:
- the meaning of the scale,
- direction of the scale,
- end points of the scale,
- resolution of the scale.
Now, it is really exciting how one affects the other:
The meaning of the scale
There is one common challenge when applying a quantitative measure to a qualitative characteristic of a thing: it is impossible to be exact so it is always a matter of some convention.
The direction of the scale
Some characteristics are positive, and some are negative but even telling the opposite things, they tend to use a positive scale.
My favourite (yet, easily quantifiable) combo is "opacity" and "transparency":
- transparency: 0% - 100%
- opacity: 0% - 100%
You can say easily tell "less transparent" (lower value) from "more transparent" (higher value) and "less opaque" (lower value) from "more opaque" (higher value). But should you put both of these side by side, in an attempt to translate one values to another, you would need to reverse one of these, for example:
100% = 0%
80% = 20%
60% = 40%
40% = 60%
20% = 80%
0% = 100%
For qualitative characteristics, this can be, again, a matter of a convention used. Should the characteristic be "quality", as an example, I can see two possible conventions:
- "1" means the highest quality (this is the "first quality", so something superior)
- "max" that you have on the scale means the highest quality.
The end points on the scale
This brings us to the next topic, which are the end points.
Within a scale, at the lowest level, there is either a zero or the lowest positive value. It is sometimes hard to say, for qualitative characteristics, what a zero could be. Using "quality" as an example again, what would it mean in both cases:
- if "1st" is the best quality, 0 does not make sense at all,
- if "max" is the best quality, a question arises if it is possible for a "no quality" state to exist.
Again, it is a matter of a convention; we could assign 0 to something like "of a quality that would remove the value from it completely" (e.g. no one would want to buy it, like a rotten apple).
Another example is asking "How satisfied you are?" In this case, it is hard to use anything else but zero for an answer "Not at all satisfied." - because the satisfaction does not exist.
So, the clue when setting the scale is answering the question if it is possible to imagine a situation when the characteristic would not exist at all. Should it be possible, use 0. Otherwise, use the least positive value (e.g. 1) on the scale.
The resolution of the scale
This depends on three factors:
- what is perceivable by the User,
- how elaborate the model behind setting the score is,
- is the scale closed at the top.
Regarding the perception, it refers mostly to situations when the range would go from "not at all" to "extremely". In these cases, I would recommend a scale of 5 levels.
- 0 Not at all
- 1 ...
- 2 Medium
- 3 ...
- 4 Extremely
(Or 1 to 5, if there is no "zero point" for this characteristic).
This scale is enough for someone to say that something is perceptually good, bad, medium, and somewhere between these levels. You can increase it to more points, but it will become harder and harder for the User to e.g. decide if his/her satisfaction is at the level of 61 62 or 63 in a 100 points scale for example. And there should always be a middle point so that "medium" can be easily reflected.
However, if there is an elaborate mechanism behind setting the score, this may be not enough. This may be used if it is crucial for the User to compare the scores between multiple items.
A good example of these two approaches playing together is a seller rating mechanism, where as a buyer you can assign 0 to 5 stars for example, but the resultant score of all the ratings are shown in factors of the stars, so you can see that one has 4 and about 1/3 of star, whereas the other one has 4 and a half - which may help you to choose the one you prefer.
Another imaginable situation is when there scale is not closed at the top, e.g. there is a possible value (probably to appear in the future) that would be higher than the current max.
In your case:
- I would treat "Condition" as a positive characteristic, so better condition would be a higher value,
- I feel that "0" does not fit this characteristic, so I would start from 1.
- regarding the resolution of the scale, you need to decide yourself, but it is quite probable that 1 to 5 will be ok.