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// Mixed ANOVA - calculate required sample size with G*Power //

The mixed ANOVA (analysis of variance) is a parametric statistical method that is used to test the means of groups over time for differences.

For example, I want to test a new drug for efficacy. I will divide my sample into two groups - an intervention group and a control group. This controls for the between-subject effect. Furthermore, I want to see the results over time, there I'll establish a baseline measurement and two follow-up measurements. This will take care of the within-subject effect.

Next to the within-subject and between-subject an interaction between those two effects is plausible and will be tested as well - it is not mandatory in study designs though.

In the run-up to an empirical study or data collection for the mixed ANOVA, the necessary sample size must be determined, for example using G*Power. The minimum sample size depends on the assumed effect size (f or Eta²), the alpha level, the statistical power, the number of groups and the number of measurements.

At the end, I show examples of different minumum sample size for mixed ANOVA with different characteristics of the input parameters mentioned using G*Power


Download link:
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https://www.psychologie.hhu.de/arbeit...


Introduction to G*Power:
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🎥    • G*Power: A (short) Beginner's Guide  


⏰ Timestamps:
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0:00 Introduction
0:07 Selecting the mxied ANOVA
0:16 Select type of power analysis
0:23 Input parameter I: Effect size f
0:58 Input parameter II: Alpha error probability
1:13 Input parameter III: power (1-beta error)
1:38 Input parameter IV: number of groups
1:46 Input parameter V: number of measurements
1:58 Input parameter VI: correlation among repeated measurements
2:06 Input parameter VII: Nonsphericity correction
2:18 Calculation and overview


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