Sampling Strategies in Research Design
Learn the main sampling strategies in research design, including random, stratified, and cluster sampling methods, with practical examples and tips.
Your sampling strategy is a critical component of your overall research design. It determines who you will collect data from.
Sampling is the process of selecting a subset of individuals from a larger population to estimate the characteristics of the whole population.
A well-chosen sample is the key to achieving findings that are both accurate (valid) and generalizable.
This guide covers the primary sampling strategies to help you choose the right one for your research.
Probability Sampling
In probability sampling, every member of the population has a known, non-zero probability of being selected. This is essential for creating a truly representative sample.
Every individual in the population has an equal chance of being selected. Like drawing names from a hat.
The population is divided into subgroups (strata), and random samples are taken from each subgroup.
Individuals are chosen at regular intervals from a list of the population.
The population is divided into clusters (like geographic areas), and a random sample of entire clusters is selected.
Non-Probability Sampling
In non-probability sampling, individuals are selected based on non-random criteria. The results are not statistically generalizable but can be useful for qualitative or exploratory studies.
Participants are selected based on ease of access. It's fast and cheap but highly prone to bias.
The researcher sets quotas for specific subgroups to ensure they are represented, but fills these quotas via convenience.
The researcher uses their expertise to select participants who are most relevant to the study's purpose.
Existing participants are asked to refer or recruit future subjects from among their acquaintances.
Choosing the Right Approach
Your choice depends on a trade-off between cost, speed, and the need for generalizable accuracy.
Do you need to generalize your findings to a whole population? If yes, you must use probability sampling.
Probability sampling is typically more expensive and resource-intensive than non-probability methods.
Non-probability methods like convenience sampling are much faster to execute.
If your decision has high stakes and requires a high degree of accuracy, the investment in probability sampling is justified.
The Sampling Tree
A visual overview of the main sampling categories.
Population
Probability
Simple Random
Stratified
Non-Probability
Convenience
Quota
Common Sampling Errors
A flawed sample leads to flawed conclusions. Be aware of these common mistakes.
When the sample is not representative of the population, leading to skewed results.
Solution: Use probability sampling methods whenever possible.
An insufficient sample size leads to a high margin of error and low confidence in the results.
Solution: Use a sample size calculator to determine the appropriate size for your desired confidence level.
When individuals who respond to a survey are different from those who do not.
Solution: Aim for high response rates and follow up with non-respondents if possible.
When some members of the population are inadequately represented in the sample.
Solution: Ensure your sampling frame is complete and up-to-date.
Practical Example: Customer Product Testing
A company wants to test a new feature with its user base of 100,000 users, composed of 60% standard users and 40% power users.
Poor Approach (Convenience Sampling)
Emailing the first 500 users on their mailing list. This is likely to be biased towards early adopters.
Better Approach (Stratified Sampling)
Randomly select 300 standard users and 200 power users to ensure the sample reflects the population structure. This provides more accurate and representative feedback.
Sampling FAQs
Plan Your Sample with Confidence
Download our free Sampling Planner Template to help you define your population, choose a method, and determine your sample size.