## Probability Sampling Methods: Definition & Types

### Question:

Probability Sampling Methods: Definition & Types

### Answer:

Choosing a sample is one of the most important steps in research. But how should you choose? In this lesson, we’ll look at three types of probability sampling: simple random, systematic, and stratified sampling.

Probability Sampling

Laura is a psychologist who is interested in studying whether there is bias against women in the workforce. So, she decides to survey workers to see if they believe that sexism plays a part at their company.

Who should Laura give the survey to? The sample of a study is the group of subjects that are involved in the study. The sample is an important part of the study and can influence the outcome. For example, if Laura only gives the survey to men, her results might underestimate whether workers believe there is a bias against women because men are less likely to notice or admit that sexism is a part of the workplace culture.

Not only that, but if Laura gives the survey only to women who work in fields that are female-dominated, like teachers or nurses, they might report less bias than if she gives the survey to women who run companies or work in sales or science.

From these examples, you can probably guess that sampling, or the process whereby a researcher chooses a sample, is an important part of planning a study.

One type of sampling is probability sampling, which is when the researcher chooses subjects randomly to be part of a sample. Randomly choosing subjects can increase the chance that a sample will reflect the population at large; Laura, for example, is less likely to end up with all men if she randomly chooses subjects.

Let’s look closer at three types of probability sampling: simple random, systematic, and stratified sampling.

Simple Random

Imagine that Laura decides that she just wants to know if there is bias against women who work in sales at large companies. The population that she wants to generalize to is just women who work in sales at large companies, which is a much smaller and simpler population than women who work in any career in any company.

For simple populations where individuals are relatively homogeneous (that is, similar to one another), a simple random sampling method works well. This is when subjects are randomly selected in some way, like flipping a coin or drawing names from a hat. Imagine simple random sampling as picking marbles from a big jar: the probability of being picked is exactly the same for every marble.

There are also a lot of computer programs that allow researchers to easily generate a simple random sample from a population. For example, maybe Laura uses a computer program to randomly select women in sales at the top 50 companies in the United States. She inputs all of their names and the number of people she wants in her sample, and the computer will randomly pick names to be included in the sample.

Systematic

As we mentioned, simple random sampling works well with a small, homogeneous population. But there are other methods of probability sampling that can also work with populations that are larger or slightly more complicated.

One popular method of probability sampling is the systematic sampling method, which involves ordering the population and then choosing every nth person. For example, maybe Laura orders a list of possible women alphabetically or by height.

Stratified Random Sampling

involves splitting subjects into mutually exclusive groups and then using simple random sampling to choose members from groups.

Systematic Sampling

means that you choose every “nth” participant from a complete list. For example, you could choose every 10th person listed.

Cluster Random Sampling

is a way to randomly select participants from a list that is too large for simple random sampling. For example, if you wanted to choose 1000 participants from the entire population of the U.S., it is likely impossible to get a complete list of everyone. Instead, the researcher randomly selects areas (i.e. cities or counties) and randomly selects from within those boundaries.

Multi-Stage Random sampling

uses a combination of techniques.

### Advantages

- Cluster sampling: convenience and ease of use.
- Simple random sampling: creates samples that are highly representative of the population.
- Stratified random sampling: creates strata or layers that are highly representative of strata or layers in the population.
- Systematic sampling: creates samples that are highly representative of the population, without the need for a random number generator.

### Disadvantages

- Cluster sampling: might not work well if unit members are not homogeneous (i.e. if they are different from each other).
- Simple random sampling: tedious and time-consuming, especially when creating larger samples.
- Stratified random sampling: tedious and time-consuming, especially when creating larger samples.
- Systematic sampling: not as random as simple random sampling,

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