Collaborative Research: Time-Sharing Experiments for the Social Sciences (TESS)

社会科学分时实验 (TESS) 是一个针对美国成年人基于概率的样本进行调查实验的平台。 TESS 利用规模经济，使来自社会科学领域的学者能够在竞争的基础上，对具有广泛理论和实践重要性的问题进行突破性研究。 TESS 的智力价值与其成功地使社会科学家及时收集基于人群的原始实验数据密切相关；促进对基本社会、政治和经济问题的更好理解；通过集中其他单独研究的费用来最大限度地提高财务效率；并提供指导和教育资源。 TESS 的影响范围广泛。 TESS 允许众多学者进行调查实验，而这些实验由于单个研究人员的成本而无法实现。 它还允许通过严格的审查过程和必要时的指导来改进这些学者的工作。 此外，在 TESS 框架下收集的所有数据都可以在单个存档中及时提供。TESS 当前的迭代还包括在 M-TURK 上运行实验以及在欧洲基于概率的样本。 与当前使用的基于概率的样本相比，使用 M-TURK 将使学者能够检查该数据收集系统的有用性。 它将允许调查人员检查前者数据收集过程中的偏差，虽然成本低廉，但对于某些目的来说可能存在严重缺陷。 将欧洲基于概率的分析结果与美国进行比较，研究人员将能够研究从美国数据得出的结果有多稳健。 对于处理非政治案件的实验尤其如此。

Time-Sharing Experiments for the Social Sciences (TESS) is a platform for conducting survey experiments fielded on probability-based samples of United States adults. TESS capitalizes on the economies of scale to enable scholars from across the social sciences, on a competitive basis, to conduct ground-breaking research on issues of broad theoretical and practical importance. The intellectual merit of TESS is closely connected to its successes in enabling social scientists collection original population-based experimental data in a timely manner; promoting better understanding of fundamental social, political, and economic questions; maximizing financial efficiency by pooling expenses for otherwise separate studies; and offering mentoring and educational resources. The broader impacts of TESS are wide-ranging. TESS has allowed a wide variety of scholars to undertake survey experiments that because of cost to single researchers would not be possible. It has also allowed for these scholar's work to be improved by a rigorous review process and mentoring where needed. In addition, all the data collected under the TESS framework is made available on a timely basis in a single archive.The current iteration of TESS also includes running experiments on M-TURK as well as probability-based samples in Europe. Using M-TURK will allow scholars to examine the usefulness of that data collection system as compared to the probability-based samples currently used. It will allow investigators to examine for biases in the data collection processes in the former, which while inexpensive may be severely flawed for some purposes. Examining the results of probability-based analysis in Europe as compared to the United States will allow investigators to study how robust results derived from United States data are. This is especially true of experiments dealing with non-political cases.