Repeated measures design uses the same subjects with every branch of research, including the control.^{[1]} For instance, repeated measurements are collected in a longitudinal study in which change over time is assessed. Other (nonrepeated measures) 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

Crossover studies 1

Uses of a repeated measures design 2

Order effects 3

Counterbalancing 4

Limitations 5

Repeated measures ANOVA 6

Partitioning of error 6.1

Assumptions 6.2

F test 6.3

Effect size 6.4

Cautions 6.5

See also 7

Notes 8

References 9

Design and analysis of experiments 9.1

Exploration of longitudinal data 9.2

External links 10
Crossover studies
A popular repeatedmeasures 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. Crossover designs are common for experiments in many scientific disciplines, for example psychology, education, pharmaceutical science and healthcare, especially medicine.
Randomized, controlled, crossover experiments are especially important in health care. In a randomized clinical trial, the subjects are randomly assigned treatments. When such a trial is a repeated measures design, the subjects are randomly assigned to a sequence of treatments. A crossover clinical trial is a repeatedmeasures design in which each patient is randomly assigned to a sequence of treatments, including at least two treatments (of which one 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 treatments and that all subjects participate for the same number of periods. In most crossover trials, each subject receives all treatments.
However, many repeatedmeasures designs are not crossovers: 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

Limited number of subjects—The repeated measure design reduces the variance of estimates of treatmenteffects, allowing statistical inference to be made with fewer subjects.^{[2]}

Efficiency—Repeated measure designs allow many experiments to be completed more quickly, as fewer groups need to be trained to complete an entire experiment. For example, experiments in which each condition takes only a few minutes, whereas the training to complete the tasks take as much, if not more time.

Longitudinal analysis—Repeated measure designs allow researchers to monitor how participants change over time, both long and shortterm situations.
Order effects
Order effects may occur when a participant in an experiment is able to perform a task and then perform it again. Examples of order effects include performance improvement or decline in performance, which may be due to learning effects, boredom or fatigue. The impact of order effects may be smaller in longterm longitudinal studies or by counterbalancing using a crossover design.
Counterbalancing
In this technique two groups each perform the same two tasks, but in reverse order. With two branches, four groups are formed.
Counter Balancing

Condition 1

Condition 2

Remarks

Group A

Group A1

Group A2

A1 performs Condition 1 first

Group B

Group B2

Group B1

B2 performs Condition 1 first

Limitations
It may not be possible for each participant to be in all conditions of the experiment (i.e. time constraints, location of experiment, etc.). Severely diseased subjects tend to drop out of longitudinal studies, potentially biasing the results. In these cases mixed effects models would be preferable as they can deal with missing values.
Mean regression may affect conditions with significant repetitions. Maturation may affect studies that extend over time. Events outside the experiment may change the response between repetitions.
Repeated measures ANOVA
Repeated measures analysis of variance (rANOVA) is a commonly used statistical approach to repeated measure designs.^{[3]} With such designs, the repeatedmeasure factor (the qualitative independent variable) is the withinsubjects factor, while the dependent quantitative variable on which each participant is measured is the dependent variable.
Partitioning of error
One of the greatest advantages to rANOVA, as is the case with repeated measures designs in general, is the ability to partition out variability due to individual differences. Consider the general structure of the Fstatistic:
F = MS_{Treatment} / MS_{Error} = (SS_{Treatment}/df_{Treatment})/(SS_{Error}/df_{Error})
In a betweensubjects design there is an element of variance due to individual difference that is combined with the treatment and error terms:
SS_{Total} = SS_{Treatment} + SS_{Error}
df_{Total} = n1
In a repeated measures design it is possible to partition subject variability from the treatment and error terms. In such a case, variability can be broken down into betweentreatments variability (or withinsubjects effects, excluding individual differences) and withintreatments variability. The withintreatments variability can be further partitioned into betweensubjects variability (individual differences) and error (excluding the individual differences) ^{[4]}
SS_{Total} = SS_{Treatment (excluding individual difference)} + SS_{Subjects} + SS_{Error}
df_{Total} = df_{Treatment (within subjects)} + df_{between subjects} + df_{error} = (k1) + (n1) + ((nk)(n1))
In reference to the general structure of the Fstatistic, it is clear that by partitioning out the betweensubjects variability, the Fvalue will increase because the sum of squares error term will be smaller resulting in a smaller MSError. It is noteworthy that partitioning variability reduces degrees of freedom from the Ftest, therefore the betweensubjects variability must be significant enough to offset the loss in degrees of freedom. If betweensubjects variability is small this process may actually reduce the Fvalue.^{[4]}
Assumptions
As with all statistical analyses, specific assumptions should be met to justify the use of this test. Violations can moderately to severely affect results and often lead to an inflation of type 1 error. With the rANOVA, standard univariate and multivariate assumptions apply.^{[5]} The univariate assumptions are:

Normality—For each level of the withinsubjects factor, the dependent variable must have a normal distribution.

Sphericity—Difference scores computed between two levels of a withinsubjects factor must have the same variance for the comparison of any two levels. (This assumption only applies if there are more than 2 levels of the independent variable.)

Randomness—Cases should be derived from a random sample, and scores from different participants should be independent of each other.
The rANOVA also requires that certain multivariate assumptions be met, because a multivariate test is conducted on difference scores. These assumptions include:

Multivariate normality—The difference scores are multivariately normally distributed in the population.

Randomness—Individual cases should be derived from a random sample, and the difference scores for each participant are independent from those of another participant.
F test
As with other analysis of variance tests, the rANOVA makes use of an F statistic to determine significance. Depending on the number of withinsubjects factors and assumption violations, it is necessary to select the most appropriate of three tests:^{[5]}

Standard Univariate ANOVA F test—This test is commonly used given only two levels of the withinsubjects factor (i.e. time point 1 and time point 2). This test is not recommended given more than 2 levels of the withinsubjects factor because the assumption of sphericity is commonly violated in such cases.

Alternative Univariate test^{[6]}—These tests account for violations to the assumption of sphericity, and can be used when the withinsubjects factor exceeds 2 levels. The F statistic is the same as in the Standard Univariate ANOVA F test, but is associated with a more accurate pvalue. This correction is done by adjusting the degrees of freedom downward for determining the critical F value. Two corrections are commonly used—The GreenhouseGeisser correction and the HuynhFeldt correction. The GreenhouseGeisser correction is more conservative, but addresses a common issue of increasing variability over time in a repeatedmeasures design.^{[7]} The HuynhFeldt correction is less conservative, but does not address issues of increasing variability. It has been suggested that lower HuynhFeldt be used with smaller departures from sphericity, while GreenhouseGeisser be used when the departures are large.

Multivariate Test—This test does not assume sphericity, but is also highly conservative.
Effect size
One of the most commonly reported effect size statistics for rANOVA is partial etasquared (η_{p}^{2}). It is also common to use the multivariate η^{2} when the assumption of sphericity has been violated, and the multivariate test statistic is reported. A third effect size statistic that is reported is the generalized η^{2}, which is comparable to η_{p}^{2} in a oneway repeated measures ANOVA. It has been shown to be a better estimate of effect size with other withinsubjects tests.^{[8]}^{[9]}
Cautions
rANOVA is not always the best statistical analyses for repeated measure designs. The rANOVA is vulnerable to effects from missing values, imputation, unequivalent time points between subjects and violations of sphericity.^{[10]} These issues can result in sampling bias and inflated rates of Type I error.^{[11]} In such cases it may be better to consider use of a linear mixed model.^{[12]}
See also
Notes

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References
Design and analysis of experiments
Exploration of longitudinal data










(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: McGrawHill.
External links

Examples of all ANOVA and ANCOVA models with up to three treatment factors, including randomized block, split plot, repeated measures, and Latin squares, and their analysis in R
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