Samples in Psychology Research

Common Reasons for Using Samples

Verywell / Nusha Ashjaee  

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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 sheer number of people is simply too large. To make inferences about the characteristics of a population, psychology researchers can use a random sample.

This article discusses how samples are used in psychology research. It also covers the different types of samples that researchers use and errors that might occur.

Why Psychology Researchers Use Samples

When researching an aspect of the human mind or ​human behavior, psychology researchers cannot collect data from every single individual in most cases. Instead, they choose a smaller sample of individuals to represent the larger group.

The goal when choosing a sample is to make sure that it represents the entire group accurately. This means that the sample should reflect the characteristics present in the total population. If the sample truly represents the population in question, researchers can generalize their results to the larger group using 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 that every individual in a population stands a chance of being selected. Because probability sampling involves random selection, it ensures that every subset of the population has an equal chance of being represented in the sample.

Probability samples are more representative and researchers are better able to generalize their results to the group as a whole.

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 their sample, often 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, research might divide the population up into subgroups based on race, sex, or age, and then take a simple random sample of each of these groups.

Stratified random sampling often provides greater statistical accuracy than simple random sampling and helps ensure that certain groups are accurately represented in the sample.

Cluster Sampling

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

For example, imagine that you are trying to do a study on school principals in your state. Collecting data from every single school principal would be cost-prohibitive and time-consuming. Using a cluster sampling method, you randomly select five counties from your state and then collect data from every subject in each of those five counties.

Recap

Probability sampling methods allow psychology researchers to get a more representative sample. Techniques that might be used include simple random sampling, stratified random sampling, and cluster sampling.

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 volunteers.

One problem with this type of sample is that volunteers might differ from non-volunteers on certain variables, which might make it difficult to generalize the results to the entire population.

Convenience Sampling

Convenience sampling involves selecting participants for a study because they are convenient and available. 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 that are available to the researcher are also examples of convenience samples.

Purposive Sampling

Purposive sampling involves seeking out individuals that 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. They might conduct telephone interviews that intentionally seek out and interview people that 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. Unlike stratified sampling, researchers use non-random methods to fill the quotas for each subgroup.

Recap

Nonprobability sampling can also be used when selecting a sample in psychology research. Such methods are less representative and include techniques such as convenience sampling, purposive sampling, and quota sampling.

Sampling Errors

Sampling errors are differences between what is present in a population and what is present in a sample. Because sampling naturally 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 are able to 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.

In general, the larger the sample size, the smaller the level of error. This is simply because the closer the sample is to the size of the total population, the more likely it is to accurately capture all of the characteristics of the population.

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 actually conduct research on the entire group.

Different types of samples can be used depending on what researchers are studying and the resources that 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 in order to study specific subsets of a larger group.

Sampling errors can occur, however. Researchers strive to minimize errors by using large, representative samples.

2 Sources
Verywell Mind uses only high-quality sources, including peer-reviewed studies, to support the facts within our articles. Read our editorial process to learn more about how we fact-check and keep our content accurate, reliable, and trustworthy.
  1. Valliant R, Dever J. Estimating propensity adjustments for volunteer web surveys. Sociol Methods Res. 2011;40(1):105-137. doi:10.1177/0049124110392533

  2. Lin L. Bias caused by sampling error in meta-analysis with small sample sizes. PLoS ONE. 2018;13(9):e0204056. doi:10.1371/journal.pone.0204056

Additional Reading
  • Goodwin CJ. Research In Psychology: Methods and Design, 12th ed. John Wiley and Sons.

By Kendra Cherry
Kendra Cherry, MS, is an author and educational consultant focused on helping students learn about psychology.