Sampling Methods

Sampling Methods in Research

Within all facets of applied science, especially psychological research, the selection of participants in a survey or study can be what “makes or breaks” the significance of the results. All data and observations are ultimately sourced from this representative sample of individuals. Therefore, it is crucial to ensure that those participating are a great representation of the required diversity for the population of the study at hand. The selection of these participants through sampling methods is important for both the significance of the findings, and the well-being of the target audience and consumers.

Sampling Methods in Psychology

How do researchers start identifying sampling methods for the particular study being conducted?

First, researchers must assess the content and structure of the study to select the most appropriate type of sampling method. Doing so provides criteria to follow that dictate what type of participants are needed for the research. In psychology, many studies require participants who have certain experiences, symptoms, or disorders that will ultimately pertain to the topic or phenomenon being studied.

In contrast, there is also a requirement for participant selection to be completely randomized and diverse for control groups or other means. For the study to be established as reliable and valid, the sample of participants must be representative of the target population.

Types of Sampling Methods

Now that we have discussed why sampling methods are so important to psychological research, you must be wondering what the different types of sampling methods are, and how researchers select them.

There are four main types of sampling methods used in research. Each technique is dependent on the kind of study and the population being generalized within each study. We will go over when each method is used, and the pros and cons of different sampling methods.

Stratified Random Sampling

Because populations can sometimes be disproportionate, the sample taken must also represent this in its demographics (Jackson, 2016). This means that if a population being generalized has more females than males or more seniors than youth, the sample must also simulate this to be representative. Stratified random sampling helps to ensure this by dividing a population by those subgroups, also known as strata, and then randomly selecting participants from each stratum for the most representative sample for the study.

Pros: Ensures a more exact representation of the population’s dimensions. It is more efficient in terms of size of the sample.

Cons: It can be difficult to analyze the differences between strata. It is not as simple to conduct.

Cluster Sampling

Similar to stratified sampling, cluster sampling uses a smaller group than the population to select a sample. This is mainly due to a population that is too large to select an equally representative sample from. By selecting a cluster from the population, you can get a good variety of participants to use in a sample.

Pros: Uses fewer population sources, saves money and time, and allows for more accurate samples.

Cons: Could potentially result in biased samples. It may also leave out some underrepresented subgroups.

Convenience Sampling

As the name implies, convenience sampling is truly out of convenience to the researchers and parameters of the study. Unlike the previously mentioned sampling methods, this one is not as careful in the selection of participants, and instead uses a sample of participants from whatever source is most convenient. Imagine you are conducting a study for your research methods class. Due to your limited reach and connections, you stand outside the cafeteria and ask any of the students walking by if they would like to participate in your study. As you are only collecting participants who happen to be around at the same place and time, this would be an example of a convenient sampling method.

Pros: Cost and time-efficient, student-friendly, easy to collect participants.

Cons: High risk of underrepresentation, low accuracy and validity, inconsistent results, high risk of sampling errors.

Quota Sampling

In quota sampling, the goal is to ensure the selected sample of participants represents the population in particular qualities. However, they are also chosen through any convenient source. Just like convenience sampling, the participants are selected from wherever they can be found, but are then filtered to ensure they meet the representative criteria.

Pros: Low costs, more effort in representation, time-efficient.

Cons: High risk of selection bias in participants, not random, not generalizable to the population.

To select the one ideal for their study, researchers must assess both the pros and cons of different sampling methods to reach the most representative and valid result.

Error in Sampling Methods

What happens when sampling methods are carried out incorrectly or with unintended bias? This is known as sampling errors and can occur in any given study. These errors are common among novice researchers as well as well-established teams of professionals. However, they can be identified and avoided in future attempts.

Sampling errors may include mistakes in the selection of participants. This occurs when researchers pick out participants themselves or when participants volunteer, which can lead to the potential of biases. Later data extracted from participant results may be affected by this error, and should be avoided using a more randomized selection method.

Non-response errors can also harm the validity of the sampling methods due to the lack of participation altogether. Usually, these non-response errors are seen in sampling methods like convenience sampling because of the limited commitment from the sample.

Sample frame errors pose a threat to studies by simply selecting irrelevant samples of participants, which ultimately impacts the results and data.

Researchers must carefully analyze the features, size, and relevance of the target population they intend to sample from to avoid these common errors. In doing so, these errors are less likely to occur, and the study will result in more relative and meaningful findings.