AI Article Synopsis
- The paper addresses challenges in applying confidence intervals to repeated-measures designs in complex factorial experiments.
- Masson and Loftus suggest that confidence intervals should be calculated using the number of observations instead of the number of participants in factorial designs.
- The authors outline a general principle for counting observations, provide a rationale for its use, and offer detailed instructions with examples for constructing confidence intervals for different effect types.
Article 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.
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