Sampling Methods and Sample Size for Quantitative Research

Probability Sampling: Simple Random, Stratified, and Cluster

Probability sampling means every member of the population has a known, non-zero chance of being selected, which is what allows you to generalise your results back to that population with a defensible confidence level.

• Simple random sampling: every individual has an equal chance of selection, drawn from a complete list of the population.
• Stratified sampling: the population is divided into subgroups (strata) first, then randomly sampled within each, ensuring smaller subgroups aren’t missed by chance.
• Cluster sampling: naturally occurring groups (schools, clinics, neighbourhoods) are randomly selected first, then everyone within the chosen clusters is included.

Non-Probability Sampling: Convenience and Purposive

Non-probability sampling doesn’t give every population member a known chance of selection, which limits how confidently you can generalise findings, but it’s often the only realistic option for student research with limited time and access.

• Convenience sampling: whoever is easiest to reach (students in your own class, patients at one clinic).
• Purposive sampling: participants are deliberately selected because they have specific characteristics relevant to the research question.

Most undergraduate and master’s-level projects use convenience or purposive sampling out of practical necessity; this is a legitimate limitation to name explicitly in your write-up, not something to disguise.

How Sample Size and Statistical Power Connect

Sampling method determines who ends up in your study; sample size determines whether your study has a realistic chance of detecting the effect you’re looking for. A properly randomised sample that’s too small still risks a Type II error, failing to detect a real effect simply because there wasn’t enough data to find it. This is exactly what a priori power analysis calculates: given your expected effect size, alpha level, and desired power, how large a sample do you actually need. See the full power analysis and sample size guide for the step-by-step G*Power walkthrough, and the Chapter 3 statistical analysis plan guide for how sampling and power analysis fit together in a dissertation proposal.

Need help justifying your sampling approach or calculating your required sample size? Get SPSS assignment help.