Sample Size Calculator (G*Power)
A-priori power analysis for multiple regression — more defensible than population-only formulas.
1 Set your parameters
* Every independent variable in the model, including controls and dummy codes.
💡 Why power analysis?
Population-based formulas such as Taro Yamane ignore how many relationships your model has to estimate. A power analysis confirms your sample is large enough to actually detect the effects your hypotheses predict — which is what reviewers ask about.
Minimum sample size
For 5 predictors at 80% statistical power
Methodology paragraph
Choosing your parameters
1. Counting predictors
Count every independent variable that enters the model. If your framework has four personal factors and four marketing factors predicting purchase intention, that is eight predictors — not two constructs. Control variables and dummy-coded categories count too.
Weak expected relationships — requires a much larger sample.
The default convention when no prior estimate exists (recommended).
Only when prior evidence supports a strong effect.
! Collect more than the minimum
The computed figure is a statistical minimum. Plan to over-collect by 10–20% to absorb incomplete questionnaires, straight-lined responses, and outliers removed during cleaning.
Reference: Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum. · Faul, F., Erdfelder, E., Buchner, A., & Lang, A.-G. (2009). Statistical power analyses using G*Power 3.1. Behavior Research Methods, 41(4), 1149–1160.
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