Two-Way RM ANOVA Flashcards
Two-Way RM ANOVA
An analysis of variance with two nominal IVs, one interval ratio DV, and the IV levels are related.
Main effect
Direct influence of IV on DV
Interaction
Effect of IV1 in the presence of IV2
Sources
- IV1
- IV1-error
- IV2
- IV2-error
- IV1 x IV2
- IV2 x IV2-error
- Ss
- Ss-error
- Total
df(IV1)
k(IV1) - 1
df(IV1-e)
df(IV1) * df(Ss-e)
df(IV2)
k(IV2) - 1
df(IV2-e)
df(IV2) * df(Ss-e)
df(IV1xIV2)
df(IV1) * df(IV2)
df(IV1xIV2-e)
df(IV1xIV2) * df(Ss-e)
df(Ss)
of IVs - 1
df(Ss-e)
N - 1
df(Total)
(k[IV1] * k[IV2] * N) - 1
MS(IV1)
SS(IV1)/df(IV1)
MS(IV1-e)
SS(IV1-e)/df(IV1-e)
MS(IV2)
SS(IV2)/df(IV2)
MS(IV2-e)
SS(IV2-e)/df(IV2-e)
MS(IV1xIV2)
SS(IV1xIV2)/df(IV1xIV2)
MS(IV1xIV2-e)
SS(IV1xIV2-e)/df(IV1xIV2-e)
F(IV1)
MS(IV1)/MS(IV1-e)
F(IV2)
MS(IV2)/MS(IV2-e)
F(IV1xIV2)
MS(IV1xIV2)/MS(IV1xIV2-e)
Two-Way RM ANOVA Hypothesis Test Steps
- Calculate F-ratios (will have 3)
- Set criteria for decisions
- Make decisions regarding significance
- Interpret (w/ comparisons if needed)
Set criteria for decisions
df(IV1) = df between (numerator), dfIV1-e = df w/in (denominator) for locating F[crit] of IV1 dfIV2 = df between (numerator), dfIV2-e = df w/in (denominator) for locating F[crit] of IV2 dfIV1xIV2 = df between (numerator), dfIV1xIV2-e = df w/in (denominator) for locating F[crit] of IV1xIV2
Make decisions regarding significance
If F > F[crit] (falls within critical boundary), reject H0 and conclude that there is a significant effect (or interaction).
If F < F[crit] (falls outside critical boundary), retain H0 and conclude that there is not a significant effect (or interaction). Draw table to plot F-ratio and F[crit] to help you determine whether the F-ratio falls within the critical boundary F[crit].
Interpret (w/ comparisons if needed)
Go back to question and ask: what is my IV, and what is my DV? Look at the # of levels to determine if you need to do a comparison test (3+ levels) of if you can compare the means directly to each other (2 levels)
Significant main effect
- k = 2: Interpret significant mean differences in context of problem
- k = 3+: Run a Tukey or a priori comparison test, then interpret significant mean differences in context of problem
Significant interaction
- Conduct simple effects
- k = 2 in simple effects ANOVAs: interpret significant mean differences in context of problem
- k = 3+ in simple effects ANOVAs: Run a Tukey or a priori comparison test, then interpret significant mean differences in context of problem
No significant result
Interpret in context of problem
Calculate F-ratio steps
- Calculate SS (will be provided)
- Calculate dfs (9)
- Calculate MSs (6)
- Calculate F-ratios (3)