Repeated measures anova

Definition

Repeated measures ANOVA is a statistical technique used to analyze data where the same subjects are measured multiple times under different conditions or over time. This method allows researchers to assess whether there are significant differences between the means of three or more related groups while taking into account the fact that measurements from the same subject are correlated. It is especially useful for experiments involving pre-test and post-test designs, longitudinal studies, and any situation where responses are gathered from the same participants across various conditions.

5 Must Know Facts For Your Next Test

1. In repeated measures ANOVA, the analysis accounts for the fact that repeated measurements can lead to correlated error terms, which violates assumptions made by traditional ANOVA.
2. The F-ratio in repeated measures ANOVA compares the variance explained by the treatment conditions to the variance due to error, helping identify if differences among means are statistically significant.
3. If sphericity is violated, corrections such as Greenhouse-Geisser or Huynh-Feldt can be applied to adjust the degrees of freedom for accurate significance testing.
4. Repeated measures ANOVA can be used in various fields including psychology, medicine, and education where interventions or changes are assessed over time with the same subjects.
5. Results from repeated measures ANOVA provide insight into not only whether there are significant differences among group means but also how those differences change across different conditions.

Review Questions

• How does repeated measures ANOVA differ from traditional ANOVA in terms of design and analysis?
  • Repeated measures ANOVA differs from traditional ANOVA primarily in its design, as it involves measuring the same subjects multiple times under different conditions. This allows for controlling individual variability because each participant serves as their own control. In analysis, repeated measures considers the correlations between repeated observations, thus providing a more accurate estimate of variance and significance compared to traditional ANOVA, which assumes independent observations.
• What impact does the assumption of sphericity have on the results of repeated measures ANOVA, and how can researchers address violations of this assumption?
  • Sphericity is a key assumption in repeated measures ANOVA that requires equal variances among the differences of all possible pairs of groups. If this assumption is violated, it can lead to inaccurate F-ratios and inflated Type I error rates. Researchers can address violations by applying corrections like Greenhouse-Geisser or Huynh-Feldt, which adjust the degrees of freedom for significance testing to account for the lack of sphericity.
• Evaluate the advantages and disadvantages of using repeated measures ANOVA in experimental research settings.
  • Using repeated measures ANOVA offers several advantages, including increased statistical power since variability between subjects is controlled and fewer participants are needed compared to independent measures. However, there are disadvantages such as potential carryover effects where previous treatments influence subsequent measurements, leading to confounding results. Additionally, careful consideration must be given to assumptions like sphericity, and if violated without correction, can lead to misleading conclusions about treatment effects.

Related terms

Within-subjects design: A research design in which the same subjects are exposed to all levels of the independent variable, allowing for direct comparison within each participant.

Sphericity:

A condition in repeated measures ANOVA that assumes equal variances of the differences between all possible pairs of within-subject conditions.

Post-hoc tests: Follow-up analyses conducted after an ANOVA to determine which specific group means are significantly different from each other.