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.
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.
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.
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.
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.
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. 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.
