Revisiting confidence intervals for repeated measures designs.
Loftus and Masson (1994) proposed a method for computing confidence intervals (CIs) in repeated measures (RM) designs and later proposed that RM CIs for factorial designs should be based on number of observations rather than number of participants (Masson & Loftus, 2003). However, determining the correct number of observations for a particular effect can be complicated, given that its value depends on the relation between the effect and the overall design. To address this, we recently defined a general number-of-observations principle, explained why it obtains, and provided step-by-step instructions for constructing CIs for various effect types (Jarmasz & Hollands, 2009). In this note, we provide a brief summary of our approach.
Hollands
Justin G
JG
Defence Research and Development Canada, Ontario, Canada. justin.hollands@drdc-rddc.gc.ca
Jarmasz
Jerzy
J
D016001:Confidence Intervals; D006801:Humans; D008433:Mathematics; D019370:Observation; D012107:Research Design / Q000706:statistics & numerical data*; D018401:Sample Size