key term - Repeated Measures

Definition

Repeated measures refer to a statistical technique where the same subjects are measured multiple times under different conditions or over various time points. This approach is often used to compare means within the same group, making it useful for analyzing how certain factors affect the same individuals over time. By measuring the same subjects repeatedly, researchers can control for individual differences, enhancing the reliability of the results.

5 Must Know Facts For Your Next Test

1. Repeated measures designs are particularly advantageous because they reduce variability due to individual differences, leading to more powerful statistical tests.
2. In repeated measures analysis, factors like time or condition can be manipulated, allowing researchers to assess trends or changes over time.
3. Statistical methods like repeated measures ANOVA or paired t-tests are commonly used to analyze data from repeated measures designs.
4. It’s essential to consider potential carryover effects when using repeated measures, as earlier treatments can influence responses in later measurements.
5. This design is widely used in fields like psychology and medicine, where understanding changes in behavior or health over time is crucial.

Review Questions

• How does a repeated measures design help improve the reliability of study results?
  • A repeated measures design improves the reliability of study results by controlling for individual differences among participants. Since the same subjects are measured multiple times under different conditions, it minimizes variability that might occur if different individuals were used in each condition. This helps researchers identify true effects of the independent variable without being influenced by personal characteristics or differences among participants.
• Discuss how carryover effects can impact results in a repeated measures study and how researchers might mitigate these effects.
  • Carryover effects can significantly impact results in a repeated measures study because they can create bias by allowing the influence of one condition to spill over into subsequent conditions. To mitigate these effects, researchers can employ counterbalancing techniques, where the order of treatments is varied among participants, ensuring that no single condition consistently comes first or last. This helps to balance out any potential carryover effects across the different treatments.
• Evaluate the strengths and weaknesses of using repeated measures designs compared to independent groups designs in research.
  • Repeated measures designs have several strengths, such as reduced error variance due to controlling individual differences and increased statistical power since fewer participants are needed. However, they also have weaknesses, including potential carryover effects and increased complexity in analysis due to correlation between measurements. In contrast, independent groups designs eliminate carryover issues but require larger sample sizes and may introduce more variability due to individual differences among groups. Evaluating these aspects helps researchers choose the appropriate design based on their specific research questions and contexts.

Related terms

Within-Subjects Design:

A research design in which the same participants are exposed to all levels of the independent variable, allowing for direct comparisons of their responses.

ANOVA:

Analysis of variance, a statistical method used to compare the means of three or more groups to see if at least one is significantly different from the others.

Carryover Effects: A type of bias that occurs when the effect of one treatment condition persists and influences responses in subsequent conditions.