Nonrandom Sampling Methods

Systematic Sampling

Sometimes it is more expeditious to collect a sample of survey participants systematically. This is frequently done, for instance, in exit polling of voters or store customers. It is a nonrandom sampling technique, but is used primarily for its ease and speed of identifying participants.

To use the systematic approach, simply choose every K^th member in the population where K is equal to the population size divided by the required sample size. If this quotient has a remainder, ignore it (round down). For example, if you need 100 members in your sample and the population consists of 1000 people, you need to sample every 1000/100 (or 10^th) member of the population. When using this method, some suggest you should choose your starting point at random by choosing a random number from 1 to K.

If you recall the characteristic requirements for a random sample discussed above (equality and independence), Can you see that systematic sampling methods lack both the equality and independence characteristics of a random sampling method? Every member from the population does not have a equal chance of being selected, and the selection of members for the sample depends on the initial selection. Regardless of how you select your starting point, once selected, every subsequent member of the sample is automatically determined. This method is clearly nonrandom.

Some suggest that by mixing the population well you can turn this into a random sampling technique. They are wrong. Regardless of how much you mix the population before selecting a starting point, the fact remains that once the first subject is chosen, selection of every other member of the sample is set.

Recognize the limitation of this type of sampling. Since it is nonrandom, the resulting sample will not necessarily be representative of the population from which it was drawn. This will affect your ability to confidently generalize results of the survey since you may not be sure to which segment of the population the results will apply. As a word of advice, unless you have experience in systematic sampling techniques, and have full knowledge of the population to be sampled, you should avoid using this method.

Judgment or Purposive Sampling

This method of sampling is based on asking an expert on the issue being investigated to define the members that should comprise the sample. The representativeness of the sample is determined solely by the judgment of the researcher. Since each member in the population does not have an equal chance of being chosen, a judgment sample is also a nonrandom sampling method.

There are situations when a variation of the judgment sampling method can be argued to be appropriate. In such situations, it goes by the name of purposive sampling. As the name implies, members from the population are selected into the sample to meet some purpose. This type of sampling is used primarily in causal-comparative (also called ex post facto) studies where the researcher is interested in finding a possible cause-and-effect link between two variables, one of which has already occurred. The researcher intentionally selects the samples in such a way that one possesses the causal (independent) variable and one does not. The purpose of the research governs the selection of the sample and, thus, excludes members of the population who do not contribute to that purpose.

Convenience or Incidental Sampling

Some samples are collected because they are convenient for the researcher. Someone from ABC college wants to do a study of college students and uses students from, where else, the ABC college. Obviously, the results of the study may not be generalizable to any other college. Even though the subjects may be selected randomly from the ABC college, the results of the study will not necessarily apply to any other college. In other words, generalization will be limited to the ABC college. When convenience samples (or incidental samples as they are sometimes called) are used in a research study, the reviewer must exercise caution in generalizing results beyond the sample studied.

Cluster Sampling

Cluster sampling is frequently used in educational research, and is used because random sampling is not possible. Consider this simple example. A researcher wants to do a study to determine if the color of a room affects one's test-taking performance. The reesarcher convinces the principal of a school to paint several classrooms -- some bright red and orange and some pastel green and blue. Then, the researcher requests permission to randomly assign students to one or another of these rooms. The principal says no, because students have already been assigned to particular teachers at the beginning of the school year and will not be reassigned. The researcher is told she may assign a whole class to a particular room, but not individual students. So the researcher puts the names of all the teachers in to a hat and randomly pulls out a name (Ms. Jones) to move into the red room, then pulls out Mr. Thompson's name to move into the green room, and so on. The researcher must assign students to different colored rooms by clusters (classes).

This sampling method is nonrandom in terms of individual students. That is, once Ms. Jones' name is selected from the hat, every student in her class is assigned to the red room (they do not individually have an equal chance of being selected for any other room); so equality is violated. Since it is the students' test scores that the researcher is interested in measuring, we have to conclude that this sampling method is nonrandom in terms of the subjects of this experiment.