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• W1593489106 abstract "Abstract Examples of repeated cross‐sections (RCS) include daily tracking polls of political opinions during campaigns, monthly Current Population Surveys of unemployment, yearly national health interview surveys, and quadrennial election studies of presidential voting. Each iteration is a distinct sample, as opposed to panels in which the same people are interviewed two or more times. By asking the same questions on repeated survey samples from the same population, RCS studies allow us to track trends and to establish causal inferences. One analytic challenge is to maintain both the representativeness and the comparability of samples as fieldwork methods or sources change. The longer the span covered by an RCS, the likelier it is that the universe will change. For an RCS spanning decades, populations can change in fundamental ways. The universe of content also changes, as issues of one period are redefined or even rendered irrelevant in another. Extracting trends from RCS data typically requires smoothing to separate signal from noise, especially where samples or subsamples are small, but this can lead to bias due to excessive smoothing or to mistaking noise for signal because of sampling variability when there is not enough smoothing. By deploying time the RCS design enables certain kinds of causal inference, but many alternative micro‐processes are observationally equivalent, and so the RCS benefits from being combined with the panel design." @default.
• W1593489106 created "2016-06-24" @default.
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• W1593489106 date "2015-05-15" @default.
• W1593489106 modified "2023-09-27" @default.
• W1593489106 title "Repeated Cross‐Sections in Survey Data" @default.
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• W1593489106 doi "https://doi.org/10.1002/9781118900772.etrds0284" @default.
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