从总调查错误框架到人类数字痕迹错误框架:翻译教程

Indira Sen, Fabian Flöck, Katrin Weller, Bernd Weiss, Claudia Wagner
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引用次数: 3

摘要

数亿人的数字痕迹为不同平台上的个人和群体提供了越来越全面的图景,但也可以推断出这些平台之外更广泛的目标人群。研究利用数字痕迹来了解人类和社会现象时可能出现的错误是至关重要的。许多类似的错误也会影响调查估计,这是调查设计者几十年来一直在解决的问题,最著名的是使用总调查误差框架(TSE)。在本教程中,我们首先向读者介绍TSE的概念和指导方针,以及社会科学中的调查从业者如何应用它们。其次,我们引入了自己的概念框架来诊断、理解和避免基于人类数字痕迹的研究中可能出现的错误。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
From the total survey error framework to an error framework for digital traces of humans: translation tutorial
The digital traces of hundreds of millions of people offer increasingly comprehensive pictures of both individuals and groups on different platforms, but also allow inferences about broader target populations beyond those platforms. Studying the errors that can occur when digital traces are used to learn about humans and social phenomena is essential. Many similar errors also affect survey estimates, which survey designers have been addressing for decades, most notably using the Total Survey Error Framework (TSE). In this tutorial, we first introduce the audience to the concepts and guidelines of the TSE and how they are applied by survey practitioners in the social sciences. Second, we introduce our own conceptual framework to diagnose, understand, and avoid errors that may occur in studies that are based on digital traces of humans.
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