## Repeated Measures Designs

### Uses

• Minimize participants: a repeated measure design reduces the variance of estimates of treatment-effects, allowing statistical inference to be made with fewer subjects
• Increase efficiency: 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: 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

### Designs

• Repeated Measures Design:
• Crossover Design: subjects are randomly assigned to a sequence of treatments. Each subject 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): each patient crosses over from one treatment to another.

### How this is done

• Multiple measures obtained for one individual are susceptible to a variety of issues - pseudoreplication, order effects (the nth measure is different because n measures have come before which have a particular cumulative impact), carry-over effects (when a specific preceding measure exerts a particular effect on all subsequent measures)
• Fixed effects are differences or changes in the dependent variable that can be attributed to a predictor (independent) variable. This predictor is constant across a number of instances, e.g. when designating the sex of an individual, on assumes that maleness for one is an equal effect as maleness for another
• Random effects exist when items represent a random sample of a population. If we were to repeated the study, we could have a different sample of subjects, each with different values drawn randomly from the population.

### Example

 To perform a repeated measures analysis in R you can use a variety of helpful tools. There are a number of additions to deducer that help in this > install.packages("DeducerRichOutput", repos="http://R-Forge.R-project.org", type="source") > install.packages("DeducerReshape", repos="http://R-Forge.R-project.org", type="source") > install.packages('ez') > install.packages('Hmisc') > library(DeducerANOVA) > library(DeducerReshape) > install.packages(c("DeducerExtras","DeducerPlugInExample","DeducerPlugInScaling","DeducerSpatial","DeducerSurvival","DeducerText","deducorrect"))