Unfortunately, there's no single "best" sample size for quantitative research. It depends on various factors specific to your study:

1. Population size:

• Small populations (less than 500): A larger sample size is generally recommended, aiming for at least 50% of the population.
• Large populations (greater than 5000): Smaller percentages suffice, typically between 17% and 27%.
• Very large populations (over 250,000): The required sample size increases only slightly, typically falling within a range of 1060 to 1840.

2. Desired level of precision:

• Higher precision (narrower margin of error): Requires a larger sample size.
• Lower precision (wider margin of error): Allows for a smaller sample size.

3. Expected effect size:

• Larger expected effect size (stronger anticipated relationship): Allows for a smaller sample size.
• Smaller expected effect size (weaker anticipated relationship): Requires a larger sample size to detect it confidently.

4. Statistical power:

• Higher statistical power (lower chance of a Type II error - missing a true effect): Requires a larger sample size.
• Lower statistical power: Allows for a smaller sample size but increases the risk of missing a true effect.

5. Available resources:

• Limited resources: Might necessitate a smaller sample size despite the ideal size based on other factors.

While these points provide an overview, it's crucial to use statistical power analysis to determine the appropriate sample size for your specific research question and desired level of precision. This analysis considers the factors mentioned above and utilizes specific formulas to calculate the minimum sample size necessary to achieve your desired statistical power.