عنوان

Three Statistical Methods for the Social Sciences

شماره

TL58c65467

زبان متن نوشتاري يا گفتاري و مانند آن

انگلیسی

عنوان اصلي

Three Statistical Methods for the Social Sciences

نام عام مواد

[Thesis]

نام نخستين پديدآور

Miratrix, Luke Weisman

نام ساير پديدآوران

Yu, Bin; Sekhon, Jasjeet S

نام ناشر، پخش کننده و غيره

UC Berkeley

تاریخ نشرو بخش و غیره

2012

کسي که مدرک را اعطا کرده

UC Berkeley

امتياز متن

2012

متن يادداشت

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

مستند نام اشخاص تاييد نشده

Miratrix, Luke Weisman

مستند نام اشخاص تاييد نشده

Yu, Bin; Sekhon, Jasjeet S

مستند نام تنالگان تاييد نشده

UC Berkeley

نام الکترونيکي

فرمت انتشار

p

نوع ماده

[Thesis]

کد کاربرگه

276903

سطح دسترسي

a

تكميل شده

Y