Introduction to Sampling

Why Sample?

Populations, Sampling Frames, and Elements

Non-Probability Sampling





Probability Sampling

Probability sampling is any sampling scheme in which the probability of choosing each individual is the same (or at least known, so it can be readjusted mathematically). These are also called random sampling. They require more work, but are much more accurate. They also allow the researcher to calculate the amount of error she can expect, and this is really important.

Simple Random

Example. A company of 680 employees wants to know whether to bother with instituting a program to deal with employee drug-taking. To find out, they will test a sample of employees on an anonymous basis: if a person tests positive, the company will not know who it is and will not try to find out. The objective is solely to estimate what percentage of the company might be doing drugs. If the percentage is high enough, the company will consider instituting a mandatory drug testing program. Given this objective, a simple random sampling design is perfect: the results will generalize to the whole company.

Stratified Sampling

Example. The VP for Human Resources of a large manufacturing is considering creating a stress-management program for employees. To get an idea of what kinds of needs the program would have to fill, she will interview a sample of 50 employees first. If she does a simple random sample, it's possible that her sample will not include any representatives of some of the smaller departments, just by chance. Since she knows that different kinds of jobs within the company produce different kinds of stress, she wants to get separate samples from the workmen (who handle dangerous chemicals), the foremen (who balance the interests of the workmen with management), and the managers (who are responsible to shareholders). So she uses a stratified random sample.

Cluster Sampling

Example.  Once a quarter, a large retail chain sends auditors to randomly chosen stores to check that proper procedures are being carried out. They look at the physical layout, the interactions between staff and customers, backroom procedures, and so on. A simple random sample could have an auditor visiting a California store one day, a New York the next, then another California store, and so on. Using cluster sampling, the auditor might first select a random sample of states, then visit a random sampling of stores with each state, thus reducing travel time.

Sample Size