Revisiting confidence intervals for repeated measures designs

• Published in: Psychonomic bulletin & review Vol. 17; no. 1; pp. 135 - 138
• Main Authors: Hollands, Justin G., Jarmasz, Jerzy
• Format: Journal Article
• Language: English
• Published: New York Springer-Verlag 01.02.2010; Springer; Springer Nature B.V
• Subjects: Behavioral Science and Psychology; Biological and medical sciences; Cognitive Psychology; Confidence Intervals; Fundamental and applied biological sciences. Psychology; Humans; Mathematics; Notes and Comment; Observation; Psychology; Psychology. Psychoanalysis. Psychiatry; Psychology. Psychophysiology; Psychometrics. Statistics. Methodology; Research Design - statistics & numerical data; Sample Size; Statistics. Mathematics; Null Hypothesis Significance Testing; Repeat Measure; Repeat Measure Design; Repeat Measure Factor; Mixed Model Approach; Statistical method; Repeated measurement; Confidence interval
• ISSN: 1069-9384; 1531-5320
• DOI: 10.3758/PBR.17.1.135

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.