Method

Table 6.1 Overview of Sampling Techniques

Technique Characteristics Example Advantages Disadvantages

Simple random

Each member of the population has an equal probability of being selected using random sampling.

Students are chosen randomly from a list of all students at a university.

Reduces sampling error by choosing from all members of the population to best represent the population.

Difficult to ensure that each member of a large population can be chosen in a sample.

Cluster Clusters of individuals are identified and then a subset of clusters is randomly chosen to sample from.

Doctors who work at hospitals are chosen for a sample by identifying all hospitals in different areas of the United States and then randomly choosing 10 hospitals in each area of the United States to sample from.

Makes it easier to choose members randomly from smaller clusters to better represent the population.

Can ignore segments of the population that are not in the clusters chosen for the sample.

Stratified random

Members of a population are selected such that the proportion of a group in the sample is equal to the proportion of that group in the population using random sampling.

Registered voters are randomly selected from lists of Democrats and Republicans to equal the proportion of registered Democrats and Republicans in the United States.

Reduces bias due to an identified characteristic of the population by equating proportions in the sample and the population for that characteristic to better represent the population.

Similar to simple random—can be difficult to ensure equal probability of being chosen from a large population.

Convenience Members of population are chosen based on convenience and on who volunteers.

Sample is chosen from students who volunteer to complete an extra credit assignment in their psychology course.

Easier to obtain than probability samples.

May not represent the population properly due to selection bias because random sampling is not used.

PROBABILITY SAMPLES Probability samples can reduce the amount of sampling error that exists in a study. Thus, it is important to use a probability sample when sampling error is likely to be large. Sampling error will increase whenever observations differ greatly from participant to participant in a sample or when a sample is chosen such that a segment of the population is not represented in the sample. For example, in small samples, it is more likely that data will differ from participant to participant. In this section we will consider three different types of probability samples (see Table 6.1) that researchers can consider when sampling error is likely to be high: simple random samples, cluster samples, and stratified random samples. The following section will describe different types of convenience samples.

Simple Random Samples