Jarmasz Jerzy, Hollands Justin G
Defence Research and Development Canada - Toronto.
Can J Exp Psychol. 2009 Jun;63(2):124-38. doi: 10.1037/a0014164.
Since the publication of Loftus and Masson's (1994) method for computing confidence intervals (CIs) in repeated-measures (RM) designs, there has been uncertainty about how to apply it to particular effects in complex factorial designs. Masson and Loftus (2003) proposed that RM CIs for factorial designs be based on number of observations rather than number of participants. However, determining the correct number of observations for a particular effect can be complicated, given the variety of effects occurring in factorial designs. In this paper the authors define a general "number of observations" principle, explain why it obtains, and provide step-by-step instructions for constructing CIs for various effect types. The authors illustrate these procedures with numerical examples.
自洛夫特斯和马森（1994年）发表了在重复测量（RM）设计中计算置信区间（CIs）的方法以来，对于如何将其应用于复杂析因设计中的特定效应一直存在不确定性。马森和洛夫特斯（2003年）提出，析因设计的RM置信区间应基于观测次数而非参与者数量。然而，鉴于析因设计中出现的各种效应，确定特定效应的正确观测次数可能会很复杂。在本文中，作者定义了一个通用的“观测次数”原则，解释了其成立的原因，并提供了针对各种效应类型构建置信区间的分步说明。作者用数值示例说明了这些程序。
