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<blockquote data-quote="Chaosmancer" data-source="post: 9130554" data-attributes="member: 6801228">I don't fully understand it, again, I'm not a survey expert.I know that a synthetic population model allows them to take multiple population polls, compare biases and data between those populations, to fill in gaps within a combined data set. I know that iterative proportional fitting is an incredibly common tool where weights are adjusted based on the population, to more accurately reflect the data. Do I understand exactly how it works? No, again, this isn't something I have a degree in, but it is commonly used by researchers to adjust the numbers based on known values. Propensity weighting is similar, but tends to work mainly on known biases within a population. Essentially knowing that a population will generally lean one way or the other, and adjusting the data numbers to account for that lean. Not to change the values, but to account for things like how people in a certain demographic might use different phrases to express the same information. And before you accuse my surface level understanding of meaning that all this is impossible, I also only have a surface level understanding of rocket science, brain surgery, and architecture. My personal lack of detailed knowledge in a subject does not mean that experts in the field don't know this stuff or can't do this stuff.</blockquote>

[QUOTE="Chaosmancer, post: 9130554, member: 6801228"] I don't fully understand it, again, I'm not a survey expert. I know that a synthetic population model allows them to take multiple population polls, compare biases and data between those populations, to fill in gaps within a combined data set. I know that iterative proportional fitting is an incredibly common tool where weights are adjusted based on the population, to more accurately reflect the data. Do I understand exactly how it works? No, again, this isn't something I have a degree in, but it is commonly used by researchers to adjust the numbers based on known values. Propensity weighting is similar, but tends to work mainly on known biases within a population. Essentially knowing that a population will generally lean one way or the other, and adjusting the data numbers to account for that lean. Not to change the values, but to account for things like how people in a certain demographic might use different phrases to express the same information. And before you accuse my surface level understanding of meaning that all this is impossible, I also only have a surface level understanding of rocket science, brain surgery, and architecture. My personal lack of detailed knowledge in a subject does not mean that experts in the field don't know this stuff or can't do this stuff. [/QUOTE]

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