Repeated Measures Design
In repeated measures design, the participants in a study are exposed to the experimental conditions and other multiple control conditions. This is different from the Independent group design when participants are exposed to the experimental condition and only one control condition over a specified period. The changes over time are then collected and analyzed. The crossover study is a popular repeated measure where participants are exposed to different treatments. There is often much confusion when using the repeated measures test since the participants are run through experimental and control conditions. In a typical experiment, subjects are either exposed to the experimental condition or the control conditions. The differences in observations are then recorded, which will be used to conclude the matter under investigation. Many researchers prefer experimental setups that involve the subjects being mutually exclusive. Having precise control and experimental samples in a population is vital when investigating a phenomenon about a situation or context. Since repeated measures design contravenes the traditional experimental setup divided into the control group and the experimental group, it is often difficult to generate accurate results. The fact that a subject is a participant in multiple groups makes the process of analysis more difficult.
How Repeated Measures Design Works
As mentioned earlier, and as the name suggests, subjects are exposed to multiple conditions in repeated measures design. In this design, the subject is measured multiple times (repeatedly); this means that the participant experiences all the present and numerous conditions that they are exposed to. The fact that subjects experience various conditions during the experiment makes it possible for them to form their control. Many statisticians often refer to this sample and dependent since a single observation can offer useful insights about another observation. In conventional experiment designs, the impact of controllable factors is usually sought after. However, in repeated measures design, there are possibilities for uncontrolled variation factors, which undermines what can be learned about the aspects that can be controlled. For instance, if a researcher intends to investigate the drug's effectiveness, making accurate conclusions can be challenging since the subject(s) are uncontrolled (exposed to multiple conditions). However, when different subject measurements are taken under various/disparate conditions, useful information can be collected about their reactions to the different sets of conditions. Here, patterns can begin to emerge, and the outcomes are then compared to the general baseline.
Despite the many reservations about the repeated measures design, there are several benefits of using the model. This includes cost-effectiveness and speediness in conducting research; observations are time-contingent, offer greater statistical power when done correctly, and require a small sample size of participants. The design challenges include the difficulty in accurately estimating effects and the order of effects as subjects are exposed to multiple treatments. However, these drawbacks can be overcome by increasing the duration between treatments. In the repeated measures ANOVA, the differences between the related mean are detected under three or more time points or under multiple and disparate conditions. The repeated measures ANOVA mostly deals with associated groups. The t-test is preferred where the groups are not dependent. In both approaches, measurements of variables are taken repeatedly over a given period.
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