Design of Experiments > Repeated Measures Design

## What is a Repeated Measures Design?

In a repeated measures design, **each group member in an experiment is tested for multiple conditions over time or under different conditions.** For example, a group of people with Type II diabetes might be given medications to see if it helps control their disease, and then they might be given nutritional counseling. A blood test to measure glucose levels is given to the patients after each treatment.

- An
**ordinary repeated measures**is where patients are assigned a single treatment, and the results are measured over time (e.g. at 1, 4 and 8 weeks). - A
**crossover design**is where patients are assigned all treatments, and the results are measured over time.

The most common crossover design is “two-period, two-treatment.” Participants are randomly assigned to receive either A and then B, or B and then A.

One important fact that sets crossover designs apart from the “usual” type of experiment is that **the same patients are in the control group and all of the treatment groups**. A patient is measured by how well they respond to treatments against themselves; the “control” is the original status of the patient. For example, a patient undergoing nutritional counseling is measured before treatment (the control part) and after treatment (the experimental part).

## Advantages and Disadvantages

Advantages include:

**Fewer patients are needed overall**. In a traditional experiment, you would need x patients per experimental group, plus a control; If you had three experimental conditions (e.g. nutrition, exercise, drugs) plus a control, you would need 4x patients. In a repeated measures design, you would only need one group of patients of size x, because all patients are given all conditions.**Fewer patients are needed**to detect an effect size.**Higher statistical power**: controlling for variability between subjects is built in.

Disadvantages include:

**Order effects:**the possibility that the position of the treatment in the order of treatments matters. Randomization or counterbalancing can correct for this.**Carryover Effects**: administration of one part of the experiment can have trickle down effects to subsequent parts.**Subjects can drop out**before the second or third part of the experiment, resulting in sample sizes that are too small to yield meaningful results.

## Analyzing Results with Repeated Measures ANOVA

ANOVA is an acronym for Analysis of Variance. It’s called*Repeated Measures*because the same group of study participants is being measured over and over again. For example, you could be studying the glucose levels of the patients at 1 month, 6 months, and 1 year after receiving nutritional counseling. For more details (including assumptions and how to run a repeated measures ANOVA), see the main article: Repeated Measures ANOVA.

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