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 samples and cluster samples. You can find a run down on the different types of sampling techniques here. It can be overwhelming to learn all these new terms and to try and remember what each one of the different sampling methods means. Luckily, their names give away their meaning and (unlike many things in statistics!), it’s a straightforward process to figure out what kind of sample you have.
Different Sampling Methods: What’s the Difference: Steps
Step 1: Find out if the study sampled from individuals (for example, picked from a pool of people). You’ll find 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 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.