1. What is Sample Size Calculation for Quantitative Research?

Sample size calculation for quantitative research determines the number of participants needed to achieve statistical significance in a study. It ensures that research findings are reliable, valid, and generalizable to the target population.

2. How Does the Calculator Work?

The calculator uses the sample size formula for mean estimation:

Where:

• \( n \) — Required sample size (dimensionless)
• \( Z \) — Z-score corresponding to desired confidence level
• \( \sigma \) — Population standard deviation
• \( E \) — Margin of error (desired precision)

Explanation: This formula calculates the minimum number of participants needed to estimate a population mean with specified confidence and precision.

3. Importance of Sample Size Calculation

Details: Proper sample size calculation prevents underpowered studies (too few participants) and wasteful oversampling (too many participants), ensuring research validity and ethical use of resources.

4. Using the Calculator

Tips: Enter Z-score based on confidence level (1.96 for 95%, 2.58 for 99%), estimated population standard deviation from pilot studies or literature, and desired margin of error. All values must be positive.

5. Frequently Asked Questions (FAQ)

Q1: What Z-score should I use?
A: Common Z-scores are 1.645 (90% confidence), 1.96 (95% confidence), and 2.576 (99% confidence).

Q2: How do I estimate standard deviation?
A: Use data from pilot studies, previous research, or literature reviews. If unavailable, use 0.5 for maximum variability.

Q3: What is an appropriate margin of error?
A: Typically 5% (0.05) for social sciences, 1-3% for medical research, depending on study requirements.

Q4: Does this work for all study designs?
A: This formula is for estimating means. Different formulas exist for proportions, correlations, and other statistical tests.

Q5: Should I adjust for expected dropout?
A: Yes, increase calculated sample size by 10-20% to account for potential participant attrition.