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" Three Statistical Methods for the Social Sciences "
Miratrix, Luke Weisman
Yu, Bin; Sekhon, Jasjeet S
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Latin Dissertation
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English
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| Record Number
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905487
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TL58c65467
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| Main Entry
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Miratrix, Luke Weisman
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Three Statistical Methods for the Social Sciences\ Miratrix, Luke WeismanYu, Bin; Sekhon, Jasjeet S
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| College
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UC Berkeley
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| Date
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2012
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| student score
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2012
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| Abstract
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Social sciences offer particular challenges to statistics due to difficulties such as conducting randomized experiments in this domain, the large variation in humans, the difficulty in collecting complete datasets, and the typically unstructured nature of data at the human scale. New technology allows for increased computation and data recording, which has in turn brought forth new innovations for analysis.Because of these challenges and innovations, statistics in the social sciences is currently thriving and vibrant.This dissertation is an argument for evaluating statistical methodology in the social sciences along four major axes: \emph{validity}, \emph{interpretability}, \emph{transparency}, and \emph{employability}. We illustrate how one might develop methods that achieve these four goals with three case studies.The first is an analysis of post-stratification, a form of covariate adjustment to evaluate treatment effect. In contrast to recent results showing that regression adjustment can be problematic under the Neyman-Rubin model, we show post-stratification, something that can easily done in, e.g., natural experiments, has a similar precision to a randomized block trail as long as there are not too many strata. The difference is
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Yu, Bin; Sekhon, Jasjeet S
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UC Berkeley
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