Repeated
Repeated Measures Design
A Repeated Measures Design is a type of research design in which the same individuals are observed multiple times under different conditions or over different periods of time. This design is commonly used in psychology, sociology, medicine, and other fields where researchers are interested in studying changes in behavior or physiological responses over time or under varying conditions.
Overview[edit | edit source]
In a Repeated Measures Design, each participant serves as their own control, which allows researchers to control for individual differences and reduce the variability that can occur between subjects. This design is particularly useful when the number of available subjects is limited, or when the variability between subjects is expected to be high.
Advantages and Disadvantages[edit | edit source]
Advantages[edit | edit source]
- Power: Repeated Measures Designs are statistically more powerful than Between-Subjects Designs, as they require fewer participants to achieve the same level of statistical power.
- Control of Individual Differences: Because each participant serves as their own control, this design controls for individual differences that might otherwise add noise to the data.
Disadvantages[edit | edit source]
- Order Effects: Participants may perform differently in later conditions simply because they have participated in earlier conditions. This is known as an Order Effect.
- Carryover Effects: The effects of one condition may carry over into subsequent conditions, potentially confounding the results.
Types of Repeated Measures Designs[edit | edit source]
There are several types of Repeated Measures Designs, including:
- Crossover Design: Each participant is exposed to all conditions in a random order.
- Time-Series Design: Participants are measured multiple times before and after a treatment or intervention.
- Within-Subjects Design: Each participant is exposed to all conditions, but the order of conditions is not randomized.
See Also[edit | edit source]
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