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Researchers are usually interested in making some kind of an inference from the data obtained from the sample – a ‘generalization’ of some sort. However, practical considerations typically require the researcher to limit his or her data collection to a sample drawn from a larger population of interest. The ability to make a population inference is going to depend in large part on how the sample was obtained, for the method chosen influences how similar the sample is to the population on all dimensions, characteristics, or features that are likely to influence or be related to the measurement of the variables in the study. When population inference is the goal the researcher is well advised to employ some kind of random sampling method.

Random sampling (also called ‘probability’ or ‘probabilistic’ sampling) requires that the process through which members of the population end up in the sample be determined by chance. Furthermore, for each member of the population, it must be possible to derive the probability of inclusion in the sample (even if you never actually calculate that probability). Random sampling is extremely important when the goal of the research is population inference, for it is the random sampling process that will, over the long haul, produce a sample that represents the population. Although it is possible that, just by chance, a specific sample will be unrepresentative of the population as a whole on one or more relevant dimensions, random sampling ensures that no conscious or unconscious biases will influence who ends up included in the sample.

The most basic form of random sampling is simple random sampling. Here, every unit of the population must have an equal probability of being included in the sample. In order to conduct a simple random sample, the researcher must have some means of identifying who or what is in the population in order to implement a method for making sure that each member has an equal chance of being included. Thus, simple random sampling requires that the investigator have some kind of list of the population prior to sampling – the ‘sampling frame.’ However, for many populations that communication researchers would be interested in sampling from, such lists do not exist.

There are reasons not to use simple random sampling even when it is possible. When conducting a stratified random sample, the population is first split into groups (strata) that are homogeneous on the stratification variable. Then a simple random sample of each stratum is taken. The sample will contain as many members of population in each stratum as you desire, with that number being a function of whether the stratified sampling is done proportionally or non-proportionally.

A related method easily confused with stratified sampling is cluster sampling. To conduct a cluster sample, it must be possible for members of the population to be classified into groups (clusters) in some fashion. When you cluster sample, all you need to have available is the universe of clusters. You randomly sample clusters from the universe of clusters, and for those clusters that are randomly selected, you include each and every cluster member in the sample.

The penetration of the telephone into most households has made sampling of people much easier than in the past. By randomly dialing telephone numbers, it is possible to collect random samples of large populations of people who are geographically dispersed. This approach does not require an enumeration of the members of the population in advance of sampling because it relies on the assumption that most people are attached to at least one phone number.

In practice, random sampling plans are often multistage, mixing sampling methods of different types that are conducted at different stages during the sampling process. For example, a researcher who wanted to collect data by doing face-to-face interviews of a random sample of urban city dwellers of an entire country would find it very difficult to collect a simple random or stratified sample of that population. Even if it were possible to enumerate the population, it might be costprohibitive to travel to the residences of, say, 1,000 different people dispersed across an entire country.

It is important to acknowledge that even if the selection of members of the population for inclusion in a sample is governed by a random process, nonrandom processes can adulterate random samples. For instance, an investigator might select a sample of people randomly from a population of interest, but certain people who are approached for inclusion in the study are likely to choose not to participate. The process that drives that choice may not be a random one (‘nonresponse bias’

**Bibliography:**

- Frick, R. W. (1998). Interpreting statistical testing: Process and propensity, not population and random sampling. Behavior Research Methods, Instruments, and Computers, 30, 527–535.
- Hayes, A. F. (2005). Statistical methods for communication science. Mahwah, NJ: Lawrence Erlbaum.
- Lacy, S., Riffe, D., Stoddard, S., Martin, H., & Chang, K.-K. (2001). Sample size for newspaper content analysis multi-year studies. Journalism and Mass Communication Quarterly, 78, 836–845.