Probability Sampling Techniques
Random Sampling Reduces Researcher Bias
Researchers must choose probability sampling methods over the ease of non-probability sampling so they can generalize their study results and reduce the risk of bias.
Why Use Probability Sampling?
If school administrators wished to conduct a survey assessing the popularity of pizza on the cafeteria menu, they could stop students on the way to the library and ask them the survey questions. Although this non-probability sampling type is a convenient way to conduct a survey, it’s not as accurate or rigorous as some probability sampling modalities.
In any field of scholarly research, researchers must set up a process that assures that the different members of a population have an equal chance of selection. This allows researchers to draw some general conclusions beyond those people included in the study. Another reason for probability sampling is the need to eliminate any possible researcher bias. Returning to the pizza survey example, the survey administrator might not be inclined to stop the troublemaker who threw water balloons in the cafeteria last week.
Researchers can choose from several types of probability sampling. This explanation uses the simple pizza survey as an example.
Simple Random Sampling
Simple random sampling is akin to pulling a number out of a hat. However, in a large population, it can be time-consuming to write down 3000 names on slips of paper to draw from a hat. An easier way to draw the sample for the pizza survey is to utilize a random number table to choose students. Administrators could use the last two digits of the students’ social security number to identify the table column and the first two digits to identify the row. However, just using luck of the draw may not provide the administrators with a good representation of subgroups in the student population.
Stratified Random Sampling
This sampling method involves dividing the population into subgroups based on variables known about those subgroups, and then taking a simple random sample of each subgroup. This would assure the administrator that he was accurately representing not only the overall population, but also key subgroups, such as students with low attendance or minority groups. This method can be tricky for the uninitiated, as the researcher must decide what weights to assign to each stratification variable.
Cluster Sampling
This stepwise process is useful for those who know little about the population they’re studying. First, the researcher would divide the population into clusters (usually geographic boundaries). Then, the researcher randomly samples the clusters. Finally, the researcher must measure all units within the sampled clusters. Researchers use this method when economy of administration is important. Because a school population is confined to a three-block area, the school administrators wouldn’t need to get so elaborate.
Multistage Sampling
This is the most complex sampling strategy. The researcher combines simpler sampling methods to address sampling needs in the most effective way possible. For example, the administrator might begin with a cluster sample of all schools in the district. Then he might set up a stratified sampling process within clusters. Within schools, the administrator could conduct a simple random sample of classes or grades. By combining various methods, researchers achieve a rich variety of results useful in different contexts.
Learn more about using sound research methods in social science research.
Sources:
Reinard, J. (1998). Introduction to Communication Research, 2nd Ed. McGraw-Hill: Boston.
Verdugo, E.D. (1998). Practical Problems in Research Methods. Pyrczak Publishing: Los Angeles.
