How and Why Sampling Is Used in Psychology Research

In statistics, a sample is a subset of a population that is used to represent the entire group as a whole. When doing psychology research, it is often impractical to survey every member of a particular population because the number of people is simply too large. To make inferences about the characteristics of a population, psychology researchers use a random sample.

Keep reading to learn about how samples are used in psychology research, the different types of samples, and the errors that may occur when using samples.

Why Psychology Researchers Use Samples

When researching an aspect of the human mind or ​human behavior, psychology researchers can rarely collect data from every single affected individual. Instead, they use a smaller sample of individuals to represent the larger group.

The goal when choosing a sample is to make sure it represents the entire group accurately. This means that the sample should reflect the diverse characteristics present in the total population. The sample must accurately represent the population in question so researchers can generalize their results to the larger group with statistical analysis.

In psychological research and other types of social science research, experimenters typically rely on a few different sampling methods. These can be grouped into probability and nonprobability samples.

Types of Probability Samples

Probability sampling means every individual in a population stands a chance of being selected. Because probability sampling uses random selection, every subset of the population has an equal chance of being represented in the sample.

Simple Random Sampling

Simple random sampling is, as the name suggests, the simplest type of probability sampling. Psychology researchers take every individual in a population and randomly select individuals to compose their sample, often by using some type of computer program or random number generator.

Stratified Random Sampling

Stratified random sampling involves separating the population into subgroups and then taking a simple random sample from each of these subgroups. For example, researchers may divide the population into subgroups based on race, sex, or age, and then take a simple random sample of each of these groups.

Cluster Sampling

Cluster sampling involves dividing a population into smaller clusters, often based on geographic location. A random sample of these clusters is then selected, and all of the subjects within the cluster are measured.

For example, imagine you are doing a study on school principals in your state. Collecting data from every single school principal would be cost-prohibitive and time-consuming. But, if you were to use a cluster sampling method, you would randomly select five counties from your state and then collect data from every subject in each of those five counties to create a representative sample.

Types of Nonprobability Samples

Nonprobability sampling involves selecting participants using methods that do not give every subset of a population an equal chance of being represented. For example, a study may recruit participants from an already established group of volunteers.

Convenience Sampling

Convenience sampling involves selecting participants for a study based on what is most convenient–people who are easily accessible and have the time. If you have ever volunteered for a psychology study conducted through your university's psychology department, then you have participated in a study that relied on a convenience sample.

Studies that rely on asking for volunteers or using clinical samples available to the researcher are also examples of convenience samples.

Purposive Sampling

Purposive sampling involves seeking out individuals who meet certain criteria. For example, a researcher might be interested in learning how college graduates between the ages of 20 and 35 feel about a topic. In purposive sampling, the researcher might conduct telephone interviews to intentionally seek out people who meet their criteria.

Quota Sampling

Quota sampling involves intentionally sampling specific proportions of each subgroup within a population. For example, political pollsters might be interested in researching the opinions of a population on a certain political issue. If they use simple random sampling, they might miss certain subsets of the population by chance.

Instead, they establish criteria to assign each subgroup a certain percentage of the sample. This differs from stratified sampling because, to find individuals within each subgroup, researchers use non-random methods to fill the quotas for each subgroup.

Sampling Errors

Sampling errors are differences between what is present in a population and what is present in a sample. Because sampling cannot include every single individual in a population, errors can occur. This can ultimately have an impact on the results of psychology research.

While it is impossible to know exactly how great the difference between the population and sample may be, researchers can statistically estimate the size of the sampling errors. In political polls, for example, you might often hear of the margin of errors expressed by certain confidence levels.

The only way to completely eliminate sampling error is to collect data from the entire population, which is often simply too costly and time-consuming. Sampling errors can be minimized, however, by using randomized probability testing and large sample size.

Summary

Samples are important in psychology research because they allow scientists to study what is happening in a larger population without having to reach every individual in the entire group.

Different types of samples can be used depending on what researchers are studying and the resources they have available to collect data. Probability samples tend to be more representative of the larger group. Nonprobability samples, on the other hand, tend to involve selecting participants based on availability and studying specific subsets of a larger group, which is less representative of the larger group.

Sampling errors can occur, however, with any type of sampling. To minimize errors, researchers strive to use large, representative samples.