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Weighting survey responses so that the findings better represent underlying demographics

Surveyors sometimes weight their data to make the findings more representative of some other set of information. This point comes through in an article in the New York Times, July 23, 2015 at 83 regarding political polls. Pollsters may get too few responses from some demographic slice, such as farmers, and want to correct for that imbalance when they present conclusions respecting the entire population. The polling company weights the few farmer respondents more heavily to make up for the imbalance and represent the locations of residents more in line with reality.


How does this transformation of data apply in surveys for the legal industry? Let’s assume that we know roughly how many companies in the United States there are that have revenue over $100 million by each major industry. Let’s also assume that a benchmark survey of law departments has gathered compensation data regarding the lawyers in the responding law departments.


If the participants in the law department survey materially under-represent some industry — the proportions in each industry don’t match the proportions that we know to be true – it is not hard to adjust the compensation data. One way would be to replicated representatives in industries that have been insufficient number to be proportional by enough to make up the difference. This is what is happens when a surveyor weights survey data to present more proportional data.


To summarize, you need to have some basis for an underlying distribution of data, such as numbers of companies above a certain size or industry. Secondly, you need a survey data set that you can adjust so that it reflects the proportions of the first data set.

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