If you’ve ever gathered data for quantitative research, then you must have come across probability sampling.

This research technique allows you to randomly select a sample population that closely represents the target audience in a systematic investigation. It reduces bias by giving all variables an equal chance to participate in a study.

Looking to implement probability sampling in your research? Read this article to learn more about the types, advantages, and disadvantages of this researcher sampling technique.

Probability sampling is a sampling technique that involves choosing a population for a systematic study based on probability theory. Here, the researcher selects a sample from the population for which they want to estimate characteristics.

Probability sampling is based on the randomization principle which means that all members of the research population have an equal chance of being a part of the sample population. For example, if the population size is 1000, it means that every member of the population has a 1/1000 chance of making it into the research sample.

The basic idea behind this approach is that if you can randomly select a representative sample, then your estimates will be accurate. When the sample population is large enough, you can use statistical techniques to make inferences about the entire population based on the sample.

Read:Survey Errors To Avoid: Types, Sources, Examples, Mitigation

There are four types of probability sampling that you can use in systematic investigations namely: simple random sampling, systematic sampling, stratified sampling, and cluster sampling.

Let’s discuss them in detail.

As the name suggests, simple random sampling is the most basic form of probability sampling and involves spontaneously selecting members of the research sample from the population of interest. Here, the researcher assigns numbers to the research population and chooses random numbers to select variables for the systematic investigation.

The procedure for selecting a random sample requires two steps. First, make a list of all members of the population. Second, randomly select a specific number of cases from the total list.

Simple random sampling works best when there’s enough time and resources for research, or if you are studying a small research population that can easily be sampled.

The American Community Survey (ACS) uses simple random sampling to gather data about American life.

Officials from the United States Census Bureau collect detailed information from a random selection of individuals in the United States and use this to draw conclusions about the entire population.

- It is easy to implement and doesn’t require any special skills.
- Because it uses randomization, any research performed on a simple random sample has high internal and external validity.

- It isn’t well suited for a large research population.
- It requires lots of time and resources for data collection.

Systematic sampling is a probability sampling method where the researcher uses a random starting point and fixed intervals to determine members of the research population. Here’s how this pans out in real-time.

Let’s say your population of interest consists of 500 people. You can choose the 5th person as your random starting point, with 10 as your random sampling interval. This means every 10th element in succession will be part of the research population.

- It’s easy to understand and execute systematic sampling.
- It has the lowest probability of contaminating the data samples.

- It is subject to the researcher’s biases.
- It requires a natural degree of randomness in the data sets.

Stratified sampling is based on the principle of stratification. Stratification occurs when the study population is divided into subgroups (strata) according to gender, age, income levels, and other similar characteristics. Each stratum is given a weight proportional to its size. Then, a sample is drawn by assigning a random starting point within each stratum.

For example, suppose you want to select 50 participants from a population of 1,000 people comprising 700 men and 300 women. To make sure all variables are equally represented, you can create subgroups in your population based on age, gender, and other similar characteristics. Then you use random sampling on each group to arrive at the desired sample size.

- It limits researcher bias.
- It ensures that all the subgroups in the population are equally represented.

- It is time-consuming.
- It doesn’t reflect all the differences between the subgroups in the population.
- It is difficult to determine the appropriate strata for the sample.

Read: What is Stratified Sampling? Definition, Examples, Types

Also known as multi-stage sampling, cluster sampling is a method of probability sampling used for selecting research samples from a large population. Here, the researcher divides the population based on pre-existing units such as geographical markers like neighborhoods and cities.

To cluster a research sample, the researcher divides the sample into naturally-occurring subgroups with distinct characteristics. Next, they randomly select clusters to use as the sample and collect the required data.

- It reduces variability in research.

- It is easier to create biased data within-cluster sampling.
- Clusters may have overlapping data points which affect the validity of the research.

Probability sampling is best used in quantitative research, especially when you’re dealing with a large population of interest. Quantitative research is a research method that focuses on gathering and analyzing numerical datasets, that is, data that can be counted.

In this case, adopting probability sampling helps you arrive at results that represent the entire research population.

- Probability sampling reduces the chances of systematic errors and sampling bias affecting your research population.
- Because the sample population has a close resemblance to the research population, researchers can use their results to make valid inferences about the population of interest.
- Probability sampling produces highly-reliable data.
- It is a cost-effective method of data collection and it helps the researcher to save time.
- Probability sampling is easy to implement and doesn’t require any technical or complex skills.

When is probability sampling a bad idea? To answer this, let’s highlight some limitations of probability sampling in research.

- It can be time-consuming when you’re dealing with a large population size.
- Despite randomization, the researcher can end up choosing similar research variables which affects the data quality.

In probability sampling, all the members of the population of interest have the same chance of being a part of the research sample. On the other hand, non-probability sampling doesn’t provide a leveled playing ground for selection. Rather, it is based on the researcher’s subjective judgment.

Probability sampling relies on tested hypotheses which leads to conclusive and unbiased research results. Non-probability sampling uses generated hypotheses that lead to biased research findings. Other differences between both research methods include:

- Probability sampling makes use of statistical inferences while non-probability sampling uses analytical inferences
- In probability sampling, the opportunity for selection is fixed and known while in non-probability sampling, the opportunity for selection is unspecified.

Probability sampling uses randomization as a criterion for selecting a sample size. It’s a particularly excellent technique for reducing sampling bias and getting reliable data. To further increase your chances of collecting quality data for your research, we recommend you use a secure tool like Formplus.

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