Week 7 - Lecture 7 Flashcards
What is Repeated Measures ANOVA?
Used when the same participants are tested in all conditions (within-subjects design) to see if there’s a statistically significant difference across multiple measurements.
Example: Measuring anxiety before, during, and after therapy in the same group.
Why is RM ANOVA more powerful than between-subjects ANOVA?
Reduces error due to individual differences because the same people are tested multiple times and each person serves as their own control.
How is total variability partitioned in RM ANOVA?
Total variability is split into:
* Treatment effect
* Participant effect
* Error
What is the formula for Total SS in RM ANOVA?
Total SS = SS_Treatment + SS_Participants + SS_Error.
What does the RM ANOVA Summary Table include?
Includes the following sources:
* Treatment: SS_Treatment, df: k - 1, MS: SS / df
* Participants: SS_Participants, df: n - 1
* Error: SS_Error, df: (k - 1)(n - 1)
* Total: SS_Total, df: kn - 1
k = number of conditions, n = number of participants.
What are the assumptions of RM ANOVA?
- Normality
- Sphericity
What does Sphericity mean in RM ANOVA?
Variances of the differences between all condition pairs are equal.
What should be used if Sphericity is violated?
Corrections like Greenhouse-Geisser.
How is the F-statistic calculated in RM ANOVA?
F = MS_Treatment / MS_Error.
What indicates a significant effect of the condition in RM ANOVA?
If F is large and p < .05.
What was the example from the lecture regarding RM ANOVA?
Participants rated their self-esteem after being primed with:
* Achievement-related words
* Social connection words
* Neutral words
What is Omega Squared (ω²) used for in RM ANOVA?
Measures how much of the total variance is explained by the effect.
What is the formula for calculating ω² in RM ANOVA?
ω² = (SS_Treatment - df_Treatment × MS_Error) / (SS_Total + MS_Error).
How is ω² interpreted in terms of effect size?
Small ≈ 0.01, Medium ≈ 0.06, Large ≈ 0.14.
When to use RM ANOVA vs Independent ANOVA?
Feature Comparison:
* RM ANOVA: Same participants in all conditions
* Independent ANOVA: Different participants in each condition
What are the advantages of RM ANOVA?
- Controls for individual differences
- Requires fewer participants
- Increases statistical power
What are the disadvantages of RM ANOVA?
- Risk of carryover or order effects
- Assumes sphericity, which can be hard to meet.
