Sampling is used in some research projects and it is important to get this critical part of a research project done as well as possible. Some basic concepts of sampling will be covered here.
The collection of all the objects in the target group is called the population. With a small universe it is often possible to survey every member of the group. However we can seldom collect data from every member of a large universe. Still we can make some reliable statements about a large universe if we observe the characteristics of a carefully selected sub-group of that universe. It's like evaluating a whole pot of soup by sipping a spoonful after stirring the pot. The process of picking that sub-group is called sampling.
The steps in sampling are:
If we want to know the opinion of the American public on an issue where do we get the names? The universe is all people in the USA but who has the list? Probably the researcher will have to accept a close substitute for the universal list, maybe the names in all the phone books in the USA or the list of everybody with Social Security numbers.
The size of the sub-group depends on the degree of accuracy you want in your results: the bigger the sample size, the more accurate your estimates will be; also the more expensive the study will be. The method of selecting the sample is also critical to the accuracy of your results. Below is a table which indicates how many respondents or observations would be needed to obtain the various error ranges given that 95 out of 100 samples will reflect the total population within that error range.
(This table is from Mildren Parten, Surveys, Polls, and Samples. Practical Procedures (New York: Harper, 1950), pp. 305-19. Quoted in James Engel, How Can I Get Them to Listen (Grand Rapids: Zondervan, 1980), p 55.)
The number of respondents needed (and therefore the cost of the survey) goes up exponentially as one pushes for a tighter error range. For this reason, you will seldom see survey results with an error range less than three percent. The method of selecting the actual members of a sample is a highly developed science but the basics are fairly simple. First the members have to come from the universe under study (they need to be representative) and second the members need to be chosen in such a way that every member has an equal likelihood of being selected. Picking the members at random accomplishes this.
There are several types of random sampling. Here are some examples:
Sometimes it will simply be impossible to obtain a sample that is really random. And without the quality of randomness in the sample the researcher technically cannot make statements about the total population based on the sample. In the real world a second best course of action is possible. First, do your best to get a random sample. Then, try and discover any reason why there would be a bias in the sample. If there is no reason to believe that the sample is biased, then just assume it is random. If there is reason to believe that the sample is biased, use logic to figure out what kind of influence this bias might have on your sample.
Here is a simple exercise:
See How Can I Get Them to Listen? by James Engel for a discussion of non-random sampling methods. The Survey Research Handbook by Alreck and Stettle has an excellent discussion of sampling error and bias and the implications.