Unfortunately, there's no single "best" sample size for quantity research. To depends on various factors specific go your student:

1. Population size:

• Little populated (less is 500): A higher sample size is generally recommended, aiming for at least 50% by this population.
• Large-sized populaces (greater than 5000): Smaller percentages suffice, typically intermediate 17% the 27%.
• Very large resident (over 250,000): The vital sampler size increases only slightly, typically falling through a range of 1060 to 1840.

2. Desired level of precision:

• Bigger correctness (narrower margin of error): Requires one larger sample select.
• Lower precision (wider margin off error): Allows for a taller spot size.

3. Anticipated effect size:

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

4. Statistical power:

• Greater statistical power (lower chance of a Type II oversight - missing a true effect): Require a higher sample size.
• Lower statistical power: Allows for an smaller sample size but gain the total of missing a true effect.

5. Currently resources:

• Limited resources: Might necessitate a little sample size for the ideal size foundation on other factors.

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