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I am looking for a numerical value that expresses how representative a (non-random in this case) sample from a population is regarding the distribution of a certain attribute.

Take for example, the age distribution in a country and in its elected body of representatives.

I'll want to compare this value across many dimensions, so the value needs to be normalized in some way and not depend on the nature of the dimension. Some of the dimensions will be numerical and continuous (age, income), others discrete with many (county of residence, trained profession) or with few classes (gender).

The goal is to figure out in which of these dimensions the sample is most (or least) representative, i.e. in which ways the non-random selection (the electoral process) is biased.

Notes: There is a question of almost the same title here alreadyhere already, but I think it talks about something else, and this other questionthis other question seems to ask for a very similar thing too, but there is no good answer for me there, either.

I am looking for a numerical value that expresses how representative a (non-random in this case) sample from a population is regarding the distribution of a certain attribute.

Take for example, the age distribution in a country and in its elected body of representatives.

I'll want to compare this value across many dimensions, so the value needs to be normalized in some way and not depend on the nature of the dimension. Some of the dimensions will be numerical and continuous (age, income), others discrete with many (county of residence, trained profession) or with few classes (gender).

The goal is to figure out in which of these dimensions the sample is most (or least) representative, i.e. in which ways the non-random selection (the electoral process) is biased.

Notes: There is a question of almost the same title here already, but I think it talks about something else, and this other question seems to ask for a very similar thing too, but there is no good answer for me there, either.

I am looking for a numerical value that expresses how representative a (non-random in this case) sample from a population is regarding the distribution of a certain attribute.

Take for example, the age distribution in a country and in its elected body of representatives.

I'll want to compare this value across many dimensions, so the value needs to be normalized in some way and not depend on the nature of the dimension. Some of the dimensions will be numerical and continuous (age, income), others discrete with many (county of residence, trained profession) or with few classes (gender).

The goal is to figure out in which of these dimensions the sample is most (or least) representative, i.e. in which ways the non-random selection (the electoral process) is biased.

Notes: There is a question of almost the same title here already, but I think it talks about something else, and this other question seems to ask for a very similar thing too, but there is no good answer for me there, either.

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Thilo
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Measuring representativeness of a (non-random) selection

I am looking for a numerical value that expresses how representative a (non-random in this case) sample from a population is regarding the distribution of a certain attribute.

Take for example, the age distribution in a country and in its elected body of representatives.

I'll want to compare this value across many dimensions, so the value needs to be normalized in some way and not depend on the nature of the dimension. Some of the dimensions will be numerical and continuous (age, income), others discrete with many (county of residence, trained profession) or with few classes (gender).

The goal is to figure out in which of these dimensions the sample is most (or least) representative, i.e. in which ways the non-random selection (the electoral process) is biased.

Notes: There is a question of almost the same title here already, but I think it talks about something else, and this other question seems to ask for a very similar thing too, but there is no good answer for me there, either.