Power Analysis: Determining Sample Size for Quantitative Studies
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In this webinar, we go over how to determine the appropriate sample size for a quantitative study by using power analysis. The presentation includes an explanation of what a power analysis is and examples of how to conduct power analyses for common statistical tests. The presentation focuses on power analysis using G*Power and Intellectus Statistics software programs. Sample size calculations for more advanced analyses are briefly discussed.
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Power Analysis: Determining Sample Size for Quantitative Studies
- 1. Power Analysis: Determining Sample Size for Quantitative Studies October 28, 2020
- 2. Presentation Overview Why is sample size important? What is statistical power? What is a power analysis? Basics of G*Power Power analysis examples Sample size calculation for advanced analyses Questions
- 3. Why is sample size important? The ability to find statistically significant results depends on sample size. Higher sample size = greater chance that results will be significant Lower sample size = lower chance that results will be significant Increasing your sample size will increase the “sensitivity” of your statistical test. If you do not have a large enough sample, important effects/differences may go undetected.
- 4. Why is sample size important? Can your sample size be too large?
- 5. Why is sample size important?
- 6. What is statistical power? Statistical power is the probability of finding a statistically significant effect in the sample if the effect actually exists in the population. Power = 1 – Type II error rate Type II error: Failing to detect an effect that actually exists (false negative) Standard for statistical power is .80 We want to have at least an 80% chance of finding statistical significance if the effect/difference does, indeed, exist.
- 7. What is a power analysis? A power analysis is an objective way to determine the appropriate sample size for a statistical analysis. The appropriate sample size is calculated based on: Type of analysis being conducted Expected/hypothesized effect size Significance (alpha) level (e.g., .05) Power level (e.g., .80) A priori vs. post hoc power analysis Software packages are available for conducting power analysis (e.g., G*Power).
- 8. Power Analysis Software: G*Power https://www.psychologie.hhu.de/arbeitsgruppen/allgemeine-psychologie- und-arbeitspsychologie/gpower.html
- 9. Power Analysis Software: Intellectus Statistics https://analyze.intellectusstatistics.com
- 10. Example: t-tests
- 11. Example: Analysis of Variance (ANOVA)
- 13. Advanced Analyses What if the type of analysis I am doing is not in G*Power? Factor analysis Structural equation modeling “Rules of thumb” (participant to variable ratio) Simulation studies (Monte Carlo analysis) Wolf, E. J., Harrington, K. M., Clark, S. L., & Miller, M. W. (2013). Sample size requirements for structural equation models: An evaluation of power, bias, and solution propriety. Educational and psychological measurement, 73(6), 913-934.
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