ISSN 0439-755X
CN 11-1911/B
主办:中国心理学会
   中国科学院心理研究所
出版:科学出版社

心理学报 ›› 2010, Vol. 42 ›› Issue (01): 88-98.

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决策与判断研究中的个体分析

Jonathan Baron   

  1. Department of Psychology, University of Pennsylvania, USA
  • 收稿日期:2008-10-06 修回日期:1900-01-01 出版日期:2010-01-30 发布日期:2010-01-30
  • 通讯作者: Jonathan Baron

Looking at Individual Subjects in Research on Judgment and Decision Making (or anything)

Jonathan Baron   

  1. Department of Psychology, University of Pennsylvania, USA
  • Received:2008-10-06 Revised:1900-01-01 Published:2010-01-30 Online:2010-01-30
  • Contact: Jonathan Baron

摘要: 决策与判断研究中(甚至是实验心理学研究中)的许多问题关注某效应是否真实存在, 及其背后的解释是什么。这些问题不关注该效应在某一特殊群体中是否显著。因此, 可以通过分析单个被试来检验效应的显著性。如果有一个被试表现出了该效应, 那么, 这个效应就是存在的。根据这一观点, 有时也可通过跨案例或者轮次(across cases or rounds)分析来验证效应的显著性, 而不需要进行跨被试分析(across subjects )。这一观点也暗示在一些实验中可能存在反方向的效应。本文建议通过进行基于被试个体的统计分析来检验这样的效应, 并介绍了一些不同形式的方法:PP 概率图(probability probability plots); P 值分布检验(tests of the distribution of p-values); 分层取样多重检验的矫正(correction for multiple testing with step-down resampling)。这些方法都可以用于处理在对同样假设进行多重检验时无法避免的问题。另外, 本文也列举了一些例子, 其中有一部分例子存在反方向的效应, 另一部分例子不存在。

关键词: 多重检验, 单尾检验, 实验方法, 冗余偏差

Abstract: Many questions in judgment and decision-making research, and, indeed, in experimental psychology generally, concern the existence of effects, and the explanation of effects shown to exist. These questions do not concern the prevalence of effects in any particular population. It is thus appropriate to look for effects in single subjects. If one person shows the effect, then it exists. This argument implies that it is sometimes appropriate to test effects across cases or rounds, without testing across subjects. It also implies that, in some experiments, effects in opposite directions may exist. I recommend looking for such effects by carrying out statistical tests on individual subjects. I describe a few methods, varying in formality, that can be used to deal with the inevitable problem of doing multiple tests of the same hypothesis: probability-probability plots; tests of the distribution of p-values; and correction for multiple testing with step-down resampling. I also present a few examples, some of which show effects in both directions and some of which do not.

Key words: multiple testing, one-tailed tests, experimental methods, omission bias