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Topic 3.4 of Data Collection and Analysis Methods

Sampling Techniques

Population, sampling frame, and sample -- and why choosing a stratified sample over a simple random one is often the more honest design when a characteristic is expected to vary sharply across subgroups.

~25 min total·4 quadrants of structured content

By the end of this topic, you will

Distinguish a population, a sampling frame, and a sample
Compare probability sampling designs: simple random, systematic, stratified, and cluster
Compare non-probability sampling designs: convenience, purposive, quota, and snowball
Justify choosing a stratified sample over a simple random sample for a given question

Quadrant 1 · e-Tutorial

Video lectures and walkthroughs

Quadrant 2 · e-Content

Articles and case studies

research-methodology

Sampling Techniques

Almost no study can examine an entire population. How the sample is chosen decides how far the findings can be trusted to represent the whole -- and a convenience sample is not automatically a bad choice, just one with a cost you must disclose.

11 min read

Quadrant 3 · Web Resources

Downloadable material and curated external links

Web resources coming soon.

Quadrant 4 · Self-Assessment

Test your knowledge — earn a certificate on first pass

Assessment coming soon.