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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

• Repeated measures: comparable to paired samples t-test

• Used with within-subjects design

• Factorial ANOVA: used when there is more than one predictor variable

• Captures variability between conditions, compared to error

•  MSbetween/MSerror

• MSerror = variability within groups, with variability due to individual idiosyncrasies removed

• Just like in between subjects ANOVA

• = SSbetween/df between

• SSbetween = SStotal – SS within groups

• df between = df total – df within

• 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

• 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

• 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

• 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

• 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

• 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?

• Once you have a significant interaction, you cannot interpret the main effects without taking that interaction into account

• When to use repeated measures ANOVA

• When to use factorial ANOVA

• The general logic of each