文章基本信息

标题：Metastudies for robust tests of theory
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作者：Beth Baribault ; Chris Donkin ; Daniel R. Little 等
期刊名称：Proceedings of the National Academy of Sciences
印刷版ISSN：0027-8424
电子版ISSN：1091-6490
出版年度：2018
卷号：115
期号：11
页码：2607-2612
DOI：10.1073/pnas.1708285114
语种：English
出版社：The National Academy of Sciences of the United States of America
摘要：We describe and demonstrate an empirical strategy useful for discovering and replicating empirical effects in psychological science. The method involves the design of a metastudy, in which many independent experimental variables—that may be moderators of an empirical effect—are indiscriminately randomized. Radical randomization yields rich datasets that can be used to test the robustness of an empirical claim to some of the vagaries and idiosyncrasies of experimental protocols and enhances the generalizability of these claims. The strategy is made feasible by advances in hierarchical Bayesian modeling that allow for the pooling of information across unlike experiments and designs and is proposed here as a gold standard for replication research and exploratory research. The practical feasibility of the strategy is demonstrated with a replication of a study on subliminal priming.
关键词：robustness ; generalizability ; metastudy ; radical randomization ; many labs