Different Sampling Methods: What’s the Difference: Overview
You’ll come across many terms in statistics that define different sampling methods: simple random sampling, systematic sampling, stratified random sampling and cluster sampling. How to tell the difference between the different sampling methods can be a challenge.
Different Sampling Methods: How to Tell the Difference: Steps
Step 1: Find out if the study sampled from individuals (for example, picked from a pool of people). You’ll find simple random sampling in a school lottery, where individual names are picked out of a hat. But a more “systematic” way of choosing people can be found in “systematic sampling,” where every nth individual is chosen from a population. For example, every 100th customer at a certain store might receive a “doorbuster” gift.
Step 2: Find out if the study picked groups of participants. For large numbers of people (like the number of potential draftees in the Vietnam war), it’s much simpler to pick people by groups (simple random sampling). In the case of the draft, draftees were chosen by birth date, “simplifying” the procedure.
Step 3: Determine if your study contained data from more than one carefully defined group (“strata” or “cluster”). Some examples of strata could be: Democrats and Republics, Renters and Homeowners, Country Folk vs. City Dwellers, Jacksonville Jaguars fans and San Francisco 49ers fans. If there are two or more very distinct, clear groups, you have a stratified sample or a “cluster sample.” If you have data about the individuals in the groups, that’s a stratified sample. In order to perform stratified sampling on this sample, you could perform random sampling of each strata independently. If you only have data about the groups themselves (you may only know the location of the individuals), then that’s a cluster sample.
Step 4: Find out if the sample was easy to get. Convenience samples are like convenience stores: why go out of your way to get samples, when you can nip out to the corner store? A classic example of convenience sampling is standing at a shopping mall, asking passers by for their opinion.
The above steps walk you through the most common types of different sampling methods you’ll find in statistics. For a detailed list of all the types you’re likely to come across, see: Sampling techniques.
If you prefer an online interactive environment to learn R and statistics, this free R Tutorial by Datacamp is a great way to get started. If you're are somewhat comfortable with R and are interested in going deeper into Statistics, try this Statistics with R track.Comments are now closed for this post. Need help or want to post a correction? Please post a comment on our Facebook page and I'll do my best to help!