Repeated measures and two-factor ANOVA

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# repeated measures and two-factor anova - PowerPoint PPT Presentation

Repeated measures and two-factor ANOVA. Chapter 14. Two extensions of ANOVA. Repeated measures: comparable to paired samples t-test Used with within-subjects design Factorial ANOVA: used when there is more than one predictor variable. Repeated measures ANOVA.

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### Repeated measures and two-factor ANOVA

Chapter 14

Two extensions of ANOVA
• Repeated measures: comparable to paired samples t-test
• Used with within-subjects design
• Factorial ANOVA: used when there is more than one predictor variable
Repeated measures ANOVA
• Captures variability between conditions, compared to error
•  MSbetween/MSerror
• MSerror = variability within groups, with variability due to individual idiosyncrasies removed
Calculating MSbetween
• Just like in between subjects ANOVA
• = SSbetween/df between
• SSbetween = SStotal – SS within groups
• df between = df total – df within
Calculating MSerror
• Variability within groups, minus variability due to individual people
• SS within (calculated just like in between subjects ANOVA) minus…
• SS between people (calculate mean for each person, across all treatments, and then calculate SS for those means)
• SS error = SS within – SS between
• df error = df within – df between participants
• df within = sum of df within each condition
• df between participants = number of participants - 1
So, MS error =…
• SS error/df error
• Bottom line: captures how much variability there is in scores that’s not just due to participants being unique weird people
•  MS error < MS within
• F = MS between/MS error
•  repeated measures ANOVAs will have a better chance at detecting variability that’s due to condition
• Still measured by h2
• Calculated by SS between conditions/(SS total – SS between participants)
• Sometimes called partial h2, since individual differences are removed
• Still needed
• Can use Tukey and Scheffe, just using MS error instead of MS within
Bottom line
• Repeated measures ANOVA captures the same idea as between subjects ANOVA
• However, since the same participants are in each condition, individual differences can be removed from the equation
•  more ability to detect differences due to condition
The power of interactions
• Sometimes one variable isn’t enough to capture what’s going on
• Sometimes the role of one variable may differ, depending on the value of another variable
•  interaction
Types of interactions
• Especially if: an effect is especially pronounced in some circumstances
• Only if: an effect is only present in some circumstances
• But if: the direction of an effect changes, depending on circumstances
Three things to look for
• Main effect: role of one variable in the dependent variable
• Main effect (2): role of the other variable in the dependent variable
• Interaction: does the role of one variable depend on the value of the other variable?
To keep in mind
• Once you have a significant interaction, you cannot interpret the main effects without taking that interaction into account
Be sure you know
• When to use repeated measures ANOVA
• When to use factorial ANOVA
• The general logic of each