Repeated measures design

The repeated measures design uses the same subjects with every condition of the research, including the control. ^[1] For instance, repeated measures are collected in a longitudinal study in which change over time is assessed. Other studies compare the same measure under two or more different conditions. For instance, to test the effects of caffeine on cognitive function, a subject's math ability might be tested once after they consume caffeine and another time when they consume a placebo.

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 6.1 Design and analysis of experiments 6.2 Exploration of longitudinal data 7 See also

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).

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.

Practice effects

Practice effects occur when a participant 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.

Advantages and disadvantages

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. ^[2]

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.).

There are also several threats to the internal validity of this design, namely a regression threat (when subjects are tested several times, their scores tend to regress towards the mean), a maturation threat (subjects may change during the course of the experiment) and an history threat (events outside the experiment that may change the response of subjects between the repeated measures).

Notes

References

Design and analysis of experiments

• Jones, Byron; Kenward, Michael G. (2003). Design and Analysis of Cross-Over Trials (Second ed.). London: Chapman and Hall. 
• Vonesh, Edward F. and Chinchilli, Vernon G. (1997). Linear and Nonlinear Models for the Analysis of Repeated Measurements. London: Chapman and Hall. 

Exploration of longitudinal data

• Davidian, Marie; David M. Giltinan (1995). Nonlinear Models for Repeated Measurement Data. Chapman & Hall/CRC Monographs on Statistics & Applied Probability. ISBN 9780412983412. 
• 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. 
• Jones, Byron; Kenward, Michael G. (2003). Design and Analysis of Cross-Over Trials (Second ed.). London: Chapman and Hall. 
• Kim, Kevin and Timm, Neil (2007). ""Restricted MGLM and growth curve model" (Chapter 7)". Univariate and multivariate general linear models: Theory and applications with SAS (with 1 CD-ROM for Windows and UNIX).. Statistics: Textbooks and Monographs (Second ed.). Boca Raton, FL: Chapman & Hall/CRC. ISBN 1-58488-634-X. 
• Kollo, Tõnu and von Rosen, Dietrich (2005). ""Multivariate linear models" (chapter 4), especially "The Growth curve model and extensions" (Chapter 4.1)". Advanced multivariate statistics with matrices. Mathematics and its applications. 579. Dordrecht: Springer. ISBN 1-4020-3418-0. 
• Kshirsagar, Anant M. and Smith, William Boyce (1995). Growth curves. Statistics: Textbooks and Monographs. 145. New York: Marcel Dekker, Inc.. ISBN 0-8247-9341-2. 
• Pan, Jian-Xin and Fang, Kai-Tai (2002). Growth curve models and statistical diagnostics. Springer Series in Statistics. New York: Springer-Verlag. ISBN 0-387-95053-2. 
• Seber, G. A. F. and Wild, C. J. (1989). ""Growth models (Chapter 7)"". Nonlinear regression. Wiley Series in Probability and Mathematical Statistics: Probability and Mathematical Statistics. New York: John Wiley & Sons, Inc.. pp. 325–367. ISBN 0-471-61760-1. 
• Timm, Neil H. (2002). ""The general MANOVA model (GMANOVA)" (Chapter 3.6.d)". Applied multivariate analysis. Springer Texts in Statistics. New York: Springer-Verlag. ISBN 0-387-95347-7. 
• 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)
• 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.

See also

Analysis of variance Clinical trial protocol Crossover study Design of experiments Glossary of experimental design Longitudinal study Growth curve

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 effect

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