A repeated measures design is a longitudinal study, usually a controlled experiment but sometimes an observational study (often referred to as a longitudinal or panel study).

Contents 1 Crossover studies, an example of a repeated measures design 2 Uses of a repeated measures design 3 Practice effects 4 Advantages and disadvantages 4.1 Advantages 4.2 Disadvantages 5 Notes 6 References 7 See also

[edit] Crossover studies, an example of a repeated measures design

A popular repeated-measures design is the crossover study. A crossover study is a longitudinal study in which subjects receive a sequence of different treatments (or exposures). While crossover studies can be observational studies, many important crossover studies are controlled experiments, which are discussed in this article. Crossover designs are common for experiments in many scientific disciplines, for example psychology, education, pharmaceutical science, and health-care, especially medicine.

Randomized, controlled, crossover experiments are especially important in health-care. In a randomized clinical trial, the subjects are randomly assigned treatments. When the randomized clinical trial is a repeated measures design, the subjects are randomly assigned to a sequence of treatments. A crossover clinical trial is a repeated-measures design in which each patient is randomly assigned to a sequence of treatments, including at least two treatments (of which one "treatment" may be a standard treatment or a placebo): Thus each patient crosses over from one treatment to another.

Nearly all crossover designs have "balance", which means that all subjects should receive the same number of treaments and that all subjects participate for the same number of periods. In most crossover trials, each subject receives all treatments.

However, many repeated-measures designs are not crossover studies: The longitudinal study of the sequential effects of repeated treatments need not use any "crossover", for example (Vonesh & Chinchilli; Jones & Kenward).

[edit] Uses of a repeated measures design

• Conduct an experiment when few participants are available: The repeated measure design reduces the variance of estimates of treatment-effects, allowing statistical inference to be made with fewer subjects.

• Conduct experiment more efficiently: Repeated measures designs allow many experiments to be completed more quickly, as only a few groups need to be trained to complete an entire experiment. For example, there are many experiments where each condition takes only a few minutes, whereas the training to complete the tasks take as much, if not more time.

• Study changes in participants’ behavior over time: Repeated measures designs allow researchers to monitor how the participants change over the passage of time, both in the case of long-term situations like longitudinal studies and in the much shorter-term case of practice effects.

[edit] Practice effects

Practice effects occur when a subject in an experiment is able to perform a task and then perform it again at some later time. Generally, they either have a positive (subjects become better at performing the task) or negative (subjects become worse at performing the task) effect. Repeated measures designs are almost always affected by practice effects; the primary exception to this rule is in the case of a longitudinal study. How well these are measured is controlled by the exact type of repeated measure design that is used.

Both types, however, have the goal of controlling for practice effects.

[edit] Advantages and disadvantages

[edit] Advantages

The primary strengths of the repeated measures design is that it makes an experiment more efficient and helps keep the variability low. This helps to keep the validity of the results higher, while still allowing for smaller than usual subject groups. ^[1]

[edit] Disadvantages

A disadvantage to the repeated measure design is that it may not be possible for each participant to be in all conditions of the experiment (i.e. time constraints, location of experiment, etc.).

[edit] Notes

[edit] References

• Fitzmaurice, Garrett, Davidian, Marie, Verbeke, Geert and Molenberghs, Geert, ed (2008). Longitudinal Data Analysis. Boca Raton, FL: Chapman and Hall/CRC. ISBN 1-584-88658-7. 
• Vonesh, Edward F. and Chinchilli, Vernon G. (1997). Linear and Nonlinear Models for the Analysis of Repeated Measurements. London: Chapman and Hall.  (Comprehensive treatment of theory and practice)
• Jones, Byron; Kenward, Michael G. (2003). Design and Analysis of Cross-Over Trials (Second ed.). London: Chapman and Hall. 

• Conaway, M. (1999, October 11). Repeated Measures Design. Retrieved February 18, 2008, from http://biostat.mc.vanderbilt.edu/twiki/pub/Main/ClinStat/repmeas.PDF
• Minke, A. (1997, January). Conducting Repeated Measures Analyses: Experimental Design Considerations. Retrieved February 18, 2008, from Ericae.net: http://ericae.net/ft/tamu/Rm.htm
• Shaughnessy, J. J. (2006). Research Methods in Psychology. New York: McGraw-Hill.

[edit] See also

Analysis of variance Clinical trial protocol Crossover study Design of experiments Glossary of experimental design Longitudinal study Missing data

Multivariate analysis Observational study Optimal design Panel analysis Panel data Panel study

Randomization Randomized controlled trial Repeated measures design Sequence Statistical inference Treatment Treatment effect

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