Calculate sample size for ten different statistical tests using G*Power
The best choice for most researchers is to seek out published papers in the area of empirical interest that answer theoretically, conceptually, or physiologically similar research questions and use the reported values associated with the statistical results. This is known as using an evidence-based measure of effect size to plan an a priori sample size calculation. Sample size calculations using evidence-based measures of effect show more empirical rigor on the researchers' part and adds internal validity to the study.
Researchers should seek out the highest level of evidence at their disposal. Systematic reviews and synopses of syntheses produce the most precise and accurate evidence-based measures of effect size. Randomized controlled trials should be considered if no systematic reviews or syntheses exist in the empirical area. Observational studies should only be considered if higher levels of evidence do not exist in the current literature.
Inclusion and exclusion criteria and sample size
Sampling methods and sample size
G*Power and sample size calculations
Click here to download G*Power: http://www.gpower.hhu.de/en.html
The Sample Size decision tree provides the methods for conducting and interpreting 10 sample size calculations using G*Power. Click on a button below to continue.
Methods for calculating sample size in G*Power
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*Faul, F., Erdfelder, E., Buchner, A., & Lang, A,-G. (2009). Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, 41, 1149-1160.
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