Power Analysis Calculator For Confirmatory Factor Analysis

Power analysis in confirmatory factor analysis (CFA) is crucial for ensuring your study has enough statistical power to detect effects. Our calculator helps you determine the necessary sample size for your CFA.

1. Enter your desired significance level (α) and power (1 – β).
2. Input the expected effect size (f2).
3. Click ‘Calculate’.
4. View your results and chart below.

The calculator uses the formula for sample size calculation in CFA, based on the work of MacCallum et al. (1996).

Case Study 1: Expected f2 = 0.15, α = 0.05, Power = 0.8

Sample Size (n)	Result
50	Power = 0.23
100	Power = 0.52
150	Power = 0.77
200	Power = 0.89

Comparison of Power Levels for Different Sample Sizes (α = 0.05, f2 = 0.15)

Sample Size (n)	Power (1 – β)
50	0.23
100	0.52
150	0.77
200	0.89

• Consider using a power of at least 0.8 for your study.
• Be aware that increasing the significance level (α) will decrease the required sample size but also increase the chance of a Type I error.

Q: What is the difference between power and significance level?

A: The significance level (α) is the probability of rejecting the null hypothesis when it is true. Power (1 – β) is the probability of rejecting the null hypothesis when it is false.

MacCallum, R. C., Browne, M. W., & Sugawara, H. (1996). Power analysis for structural equation models: A Monte Carlo simulation study. Structural Equation Modeling, 3(4), 399-424.

Confirmatory Factor Analysis using R