* Desired power: The probability of finding a statistically significant result if a true effect exists. Higher power requires a larger sample size. A typical target is 80% power.
* Significance level (alpha): The probability of rejecting the null hypothesis (no difference in spatial ability) when it is actually true. A common significance level is 0.05.
* Effect size: The magnitude of the difference in spatial ability between the two menstrual cycle points. A larger effect size requires a smaller sample size to detect. Iyesha might use the effect size reported in the original study as an estimate, or she might conduct a power analysis using a smaller effect size to be more conservative.
* Type of test: The statistical test used to analyze the data (e.g., a paired t-test if the same women are tested at both time points). The choice of test influences the sample size calculation.
* Variability in the data: Greater variability in spatial ability scores requires a larger sample size. Iyesha may need to estimate this based on the original study or pilot data.
How Iyesha would determine n:
Iyesha would ideally use statistical power analysis software (G*Power, PASS, etc.) or a statistical package (R, SPSS) to calculate the necessary sample size. She would input the desired power, significance level, effect size, and the type of statistical test she plans to use. The software will then output the minimum sample size (n) needed for her study.
Without knowing the specifics of the original study (effect size, variability, etc.), it's impossible to give a specific value for 'n'. However, given that the original study used a *small* sample, Iyesha should expect that her study will also require a larger sample size than the original, to achieve sufficient power.