The intent was to communicate one piece of information without confusion: where on the measurement spectrum the item fits relative to others in its group. As opposed to delivering as much information as possible, for which there are more nuanced systems.
Most things I am rating do not have a uniform distribution, I tried to follow a normal distribution because it would fit the greater majority of cases. We lose information and make assumptions when we measure data on the wrong distribution, did you fit to uniform by volume or by value? It was another source of confusion.
As mentioned, this method did fail. I changed my methods to saying ‘better than 90% of the items in its grouping’ and had moderate success. While solving the uniform/normal/Chi-squared distribution problem it is still too long winded for my tastes.
Most things I am rating do not have a uniform distribution
The distribution of your ratings does not need to follow the distribution of what you are rating. For maximum information your (integer) rating should point to a quantile—e.g. if you’re rating on a 1-10 scale your rating should match the decile into which the thing being rated falls. And if your ratings correspond to quantiles, the ratings themselves are uniformly distributed.
We have different goals. I want to my rating to reflect the items relative position in its group, you want a rating to reflect the items value independent of the group.
Doesn’t seem so. If you rate by quintiles your rating effectively indicates the rank of the bucket to which the thing-being-rated belongs. This reflects “the item’s relative position in its group”.
If you want your rating to reflect not a rank but something external, you can set up a variety of systems, but I would expect that for max information your rating would have to point a quintile of that external measure of the “value independent of the group”.
Trying to stab at the heart of the issue: I want the distribution of the ratings to follow the distribution of the rated because when looking at the group this provides an additional piece of information.
Well, at this point the issue becomes who’s looking at your rating. This “additional piece of information” exists only for people who have a sufficiently large sample of your previous ratings so they understand where the latest rating fits in the overall shape of all your ratings.
Consider this example: I come up to you and ask “So, how was the movie?”. You answer “I give it a 6 out of 10″. Fine. I have some vague idea of what you mean. Now we wave a magic wand and bifurcate reality.
In branch 1 you then add “The distribution of my ratings follows the distribution of movie quality, savvy?” and let’s say I’m sufficiently statistically savvy to understand that. But… does it help me? I don’t know the distribution of movie quality. it’s probably bell-shaped, maybe, but not quite normal if only because it has to be bounded, I have no idea if its skewed, etc.
In branch 2 you then add “The rating of 6 means I rate the movie to be in the sixth decile”. Ah, that’s much better. I now know that out of 10 movies that you’ve seen five were probably worse and three were probably better. That, to me, is a more useful piece of information.
The intent was to communicate one piece of information without confusion: where on the measurement spectrum the item fits relative to others in its group. As opposed to delivering as much information as possible, for which there are more nuanced systems.
Most things I am rating do not have a uniform distribution, I tried to follow a normal distribution because it would fit the greater majority of cases. We lose information and make assumptions when we measure data on the wrong distribution, did you fit to uniform by volume or by value? It was another source of confusion.
As mentioned, this method did fail. I changed my methods to saying ‘better than 90% of the items in its grouping’ and had moderate success. While solving the uniform/normal/Chi-squared distribution problem it is still too long winded for my tastes.
The distribution of your ratings does not need to follow the distribution of what you are rating. For maximum information your (integer) rating should point to a quantile—e.g. if you’re rating on a 1-10 scale your rating should match the decile into which the thing being rated falls. And if your ratings correspond to quantiles, the ratings themselves are uniformly distributed.
We have different goals. I want to my rating to reflect the items relative position in its group, you want a rating to reflect the items value independent of the group.
Is this accurate?
Doesn’t seem so. If you rate by quintiles your rating effectively indicates the rank of the bucket to which the thing-being-rated belongs. This reflects “the item’s relative position in its group”.
If you want your rating to reflect not a rank but something external, you can set up a variety of systems, but I would expect that for max information your rating would have to point a quintile of that external measure of the “value independent of the group”.
Trying to stab at the heart of the issue: I want the distribution of the ratings to follow the distribution of the rated because when looking at the group this provides an additional piece of information.
Well, at this point the issue becomes who’s looking at your rating. This “additional piece of information” exists only for people who have a sufficiently large sample of your previous ratings so they understand where the latest rating fits in the overall shape of all your ratings.
Consider this example: I come up to you and ask “So, how was the movie?”. You answer “I give it a 6 out of 10″. Fine. I have some vague idea of what you mean. Now we wave a magic wand and bifurcate reality.
In branch 1 you then add “The distribution of my ratings follows the distribution of movie quality, savvy?” and let’s say I’m sufficiently statistically savvy to understand that. But… does it help me? I don’t know the distribution of movie quality. it’s probably bell-shaped, maybe, but not quite normal if only because it has to be bounded, I have no idea if its skewed, etc.
In branch 2 you then add “The rating of 6 means I rate the movie to be in the sixth decile”. Ah, that’s much better. I now know that out of 10 movies that you’ve seen five were probably worse and three were probably better. That, to me, is a more useful piece of information.
I understand and concede to the better logic. This provides greater insight on why the original attempt to use these ratings failed.