Confidence intervals in repeated-measures designs: The number of observations principle.

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.

Abstract

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.