Sampling Terminology

Sampling Terminology

As with anything else in life you have to learn the language of an area if you’re going to ever hope to use it. Here, I want to introduce several different terms for the major groups that are involved in a sampling process and the role that each group plays in the logic of sampling.

The major question that motivates sampling in the first place is: “Who do you want to generalize to?” Or should it be: “To whom do you want to generalize?” In most social research we are interested in more than just the people who directly participate in our study. We would like to be able to talk in general terms and not be confined only to the people who are in our study. Now, there are times when we aren’t very concerned about generalizing. Maybe we’re just evaluating a program in a local agency and we don’t care whether the program would work with other people in other places and at other times. In that case, sampling and generalizing might not be of interest. In other cases, we would really like to be able to generalize almost universally. When psychologists do research, they are often interested in developing theories that would hold for all humans. But in most applied social research, we are interested in generalizing to specific groups. The group you wish to generalize to is often called the population in your study. This is the group you would like to sample from because this is the group you are interested in generalizing to. Let’s imagine that you wish to generalize to urban homeless males between the ages of 30 and 50 in the United States. If that is the population of interest, you are likely to have a very hard time developing a reasonable sampling plan. You are probably not going to find an accurate listing of this population, and even if you did, you would almost certainly not be able to mount a national sample across hundreds of urban areas. So we probably should make a distinction between the population you would like to generalize to, and the population that will be accessible to you. We’ll call the former the theoretical population and the latter the accessible population. In this example, the accessible population might be homeless males between the ages of 30 and 50 in six selected urban areas across the U.S.

Once you’ve identified the theoretical and accessible populations, you have to do one more thing before you can actually draw a sample – you have to get a list of the members of the accessible population. (Or, you have to spell out in detail how you will contact them to assure representativeness). The listing of the accessible population from which you’ll draw your sample is called the sampling frame. If you were doing a phone survey and selecting names from the telephone book, the book would be your sampling frame. That wouldn’t be a great way to sample because significant subportions of the population either don’t have a phone or have moved in or out of the area since the last book was printed. Notice that in this case, you might identify the area code and all three-digit prefixes within that area code and draw a sample simply by randomly dialing numbers (cleverly known as random-digit-dialing). In this case, the sampling frame is not a list per se, but is rather a procedure that you follow as the actual basis for sampling. Finally, you actually draw your sample (using one of the many sampling procedures). The sample is the group of people who you select to be in your study. Notice that I didn’t say that the sample was the group of people who are actually in your study. You may not be able to contact or recruit all of the people you actually sample, or some could drop out over the course of the study. The group that actually completes your study is a subsample of the sample – it doesn’t include nonrespondents or dropouts. The problem of nonresponse and its effects on a study will be addressed when discussing “mortality” threats to internal validity.

People often confuse what is meant by random selection with the idea of random assignment. You should make sure that you understand the distinction between random selection and random assignment.

At this point, you should appreciate that sampling is a difficult multi-step process and that there are lots of places you can go wrong. In fact, as we move from each step to the next in identifying a sample, there is the possibility of introducing systematic error or bias. For instance, even if you are able to identify perfectly the population of interest, you may not have access to all of them. And even if you do, you may not have a complete and accurate enumeration or sampling frame from which to select. And, even if you do, you may not draw the sample correctly or accurately. And, even if you do, they may not all come and they may not all stay. Depressed yet? This is a very difficult business indeed. At times like this I’m reminded of what Donald Campbell used to say (I’ll paraphrase here): “Cousins to the amoeba, it’s amazing that we know anything at all!”

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Knowledge Base written by Prof William M.K. Trochim. Changes and additions by This page was last modified on 10 Mar 2020.

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