Practical sampling methods in research with examples
Find practical sampling methods in research and choose the best methodology for your next study
Inside this Article…
- Introduction: Inside every researcher’s toolkit
- Common research sampling questions
- Information you need to choose a sampling method
- Sample Frames and how they help you collect a sample
- Questions to consider during sample frame selection
- Sampling methods used in Online Research
- Sample Size Calculator
- Sampling methods to consider for your study design
- Probability sampling methods: Simple Random, Cluster, Stratified, and Systematic
- Non-probability sampling methods: Convenience, Quota, and Judgemental
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Is the sample qualified?
Do members selected in this sample frame match the qualifications for the study target population? (is this qualified list of members a sample representative of a total population)
How accurate are the results?
With what degree of precision (margin of error) can I forecast or state my conclusions? This sampling error is about the statistical accuracy of estimates from the target population, confidence interval, and sample size.
Use this Sample Size Calculator to Estimate Your Sample Size
- Population total: Give the number of people that your survey results will be representing. This is not your sample size, but the entire population you are trying to represent. Examples would be The State of California (39.51 million) or Pacific Gas & Electric (PG&E) customers (16 million). Enter your target population total.
- Margin of error: The margin of error represents how precise your results will be. The margin of error represents the difference in proportion from the true proportion that is acceptable to the researcher. In the social sciences, five percent is an acceptable margin of error. You can change the margin of error depending on your precision needs. If you have no preference, choose five percent default. Sample size increases as the margin of error decreases.
- Confidence level: Select the reliability level. In statistics, this a measure of the reliability of a result. A confidence level of 95 percent means there is a probability of at least 95 percent the result is within a given range. Increasing the confidence level will increase the sample size.
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