Abstract
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.
Article PDF
Similar content being viewed by others
References
Blouin, D. C., & Riopelle, A. J. (2005). On confidence intervals for within-subjects designs. Psychological Methods, 10, 397–412.
Cumming, G., & Finch, S. (2005). Inference by eye: Confidence intervals and how to read pictures of data. American Psychologist, 60, 170–180.
Estes, W. K. (1997). On the communication of information by displays of standard errors and confidence intervals. Psychonomic Bulletin & Review, 4, 330–341.
Jarmasz, J., & Hollands, J. G. (2009). Confidence intervals in repeated measures designs: The number of observations principle. Canadian Journal of Experimental Psychology, 63, 124–138.
Kirk, R. E. (1982). Experimental design: Procedures for the behavioral sciences (2nd ed.). Monterey, CA: Brooks/Cole.
Lee, P. M. (1997). Bayesian statistics: An introduction (2nd ed.). London: Arnold.
Loftus, G. R., & Loftus, E. F. (1988). Essence of statistics (2nd ed.). New York: Knopf.
Loftus, G. R., & Masson, M. E. J. (1994). Using confidence intervals in within-subject designs. Psychonomic Bulletin & Review, 1, 476–490.
Masson, M. E. J. (2004). Correction to Masson and Loftus (2003). Canadian Journal of Experimental Psychology, 58, 289.
Masson, M. E. J., & Loftus, G. R. (2003). Using confidence intervals for graphically based data interpretation. Canadian Journal of Experimental Psychology, 57, 203–220.
SAS Institute (1999). SAS/STAT user’s guide (Version 8, Vol. 2). Cary, NC: SAS Institute.
Thissen, D., Steinberg, L., & Kuang, D. (2002). Quick and easy implementation of the Benjamini-Hochberg procedure for controlling the false positive rate in multiple comparisons. Journal of Educational & Behavioral Statistics, 27, 77–83.
Tryon, W. W. (2001). Evaluating statistical difference, equivalence, and indeterminacy using inferential confidence intervals: An integrated alternative method of conducting null hypothesis statistical tests. Psychological Methods, 6, 371–386.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Hollands, J.G., Jarmasz, J. Revisiting confidence intervals for repeated measures designs. Psychonomic Bulletin & Review 17, 135–138 (2010). https://doi.org/10.3758/PBR.17.1.135
Received:
Accepted:
Issue Date:
DOI: https://doi.org/10.3758/PBR.17.1.135