The sample used to conduct a study is one of the most important elements of any research project. A research sample is those who partake in any given study, and enables researchers to conduct studies of large populations without needing to reach every single person within a population. Sample source, sample size, and how the sample was selected all have an effect on the reliability and validity of a study’s results – that is, how much those reading the results can trust that they will continue to produce the same results over time, and that they represent the wider population being studied.
In this series of blog posts, GeoPoll will outline the various aspects that make up a sample and why each one is important. First, we will examine how sample is selected and the differences between a probability sample and a non-probability sample.
Sampling methods are vital for collecting representative data in research When your population size is very large, it becomes impractical to gather information from every single individual Sampling allows you to study a subset instead.
There are two main categories of sampling methods
- Probability sampling
- Non-probability sampling
Understanding the difference between these two is crucial for researchers. Keep reading as I break it down in simple terms!
What is Probability Sampling?
In probability sampling, each member of the target population has an equal chance of being chosen for the sample. This is done using random selection techniques
Some examples of probability sampling methods include:
- Simple random sampling
- Systematic sampling
- Stratified sampling
- Cluster sampling
- Multistage sampling
The key advantage of probability sampling is that it aims to create a representative sample. This means the sample should have similar characteristics to the overall population.
For example, let’s say 55% of the population is female. An ideal probability sample would also be 55% female.
Representative samples allow you to make statistical inferences. In other words, you can use statistics to make generalizations from your sample back to the population.
Probability sampling requires a sampling frame – a complete list of all the units in the population. This ensures every single individual has a chance of being selected.
What is Non-Probability Sampling?
In non-probability sampling, not every member of the population has an equal chance of being selected. Instead, subjective methods are used to decide who is included.
Some examples of non-probability sampling techniques are:
- Convenience sampling
- Purposive sampling
- Quota sampling
- Snowball sampling
- Self-selection sampling
Researchers use non-probability methods when:
- There is no sampling frame available
- Only individuals with certain traits are needed
- Representativeness is not the main priority
For instance, let’s say you need to survey doctors about a new surgical technique. It would make sense to specifically select doctors with experience performing that type of surgery.
The sample from non-probability methods is unlikely to accurately represent the population. However, it can still provide valuable, specialized insights.
Key Differences Between Probability and Non-Probability Sampling
| Probability Sampling | Non-Probability Sampling |
|---|---|
| Aims for a representative, random sample | Does not aim for a random, representative sample |
| Equal chance of selection for all population units | Unequal chance of selection |
| Requires a complete sampling frame | Does not require a sampling frame |
| Allows statistical inferences about the population | Does not allow statistical inferences about the population |
| Examples: Simple random, systematic, stratified, cluster | Examples: Convenience, purposive, quota, snowball, self-selection |
When to Use Probability vs Non-Probability Sampling
Use probability sampling when:
- You need a sample that represents the population
- You want to make statistical generalizations
- You have access to a complete, accurate sampling frame
Use non-probability sampling when:
- Representativeness is not essential
- You need a targeted sample with specific traits
- No sampling frame exists for the population
- Resources are limited
Advantages and Disadvantages of Probability Sampling
Advantages
- Creates a representative, unbiased sample
- Allows statistical analysis and generalizations
- Equal chance of selection reduces sampling bias
Disadvantages
- Requires a complete, accurate sampling frame
- Can be more expensive and time-consuming
Advantages and Disadvantages of Non-Probability Sampling
Advantages
- No need for a sampling frame
- Can target specific subgroups
- Usually quicker and cheaper to conduct
Disadvantages
- Samples are unlikely to represent the population
- Increased risk of sampling bias
- No statistical generalizations are possible
Examples of Probability and Non-Probability Sampling
Probability sampling example
A market researcher wants to survey public opinion on a new product. They obtain a list of all postal codes in the area, randomly select 200, and mail the survey to households in those zones.
This is an example of cluster sampling, a probability technique. Every household has an equal chance of being included. Statistical analysis of the resulting data can be used to make inferences about overall public opinion.
Non-probability sampling example
A psychologist needs 30 teenagers to participate in interviews about risk-taking behavior. She stands outside a high school and asks passing students if they want to take part, stopping after 30 agree.
This convenience sampling method does not give an equal chance of inclusion to all high school teens. The sample is unlikely to accurately represent all local youth. However, it meets the need for readily available subjects with this specific trait.
Summary
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Probability sampling aims for representative, unbiased random samples using techniques like simple random and stratified sampling. This allows statistical generalizations.
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Non-probability sampling uses subjective methods like purposive and quota sampling to target particular groups, but does not produce statistically representative samples.
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Choose probability sampling when inferences and representativeness are needed, and non-probability methods when resources are limited or a targeted sample is required.
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Be aware of the advantages and disadvantages of both approaches. Combine them wisely based on your specific research goals and constraints.
Types of Probability Sampling
There are several sampling methods that fall under probability sampling. In each method, those who are within the sample frame have some chance of being selected to participate in a study. Four of the common types of probability sampling are:
Simple Random Sample: The most basic form of probability sampling, in a simple random sample each member of a population is assigned an identifier such as a number, and those selected to be within the sample are picked at random, often using an automated software program.
Stratified Random Sample: A stratified random sample is a step up from complexity from a simple random sample. In this method, the population is divided into sub-groups, such as male and female, and within those sub-groups a simple random sample is performed. This enables a random sample that is representative of a larger population and its specific makeup, such as a country’s population.
Cluster Sample: In cluster sampling, a population is divided into clusters which are unique, yet represent a diverse group – for example, cities are often used as clusters. From the list of clusters, a select number are randomly selected to take part in a study.
Systematic Sample: Using a systematic sample, participants are selected to be part of a sample using a fixed interval. For example, if using an interval of 5, the sample may consist of the fifth, 10th, 15th, and 20th, and so forth person on a list.
Probability Sample vs Non-Probability Sample
There are two main methods of sampling: Probability sampling and non-probability sampling. In probability sampling, respondents are randomly selected to take part in a survey or other mode of research. For a sample to qualify as a probability sample, each person in a population must have an equal chance of being selected for a study, and the researcher must know the probability that an individual will be selected. Probability sampling is the most common form of sampling for public opinion studies, election polling, and other studies in which results will be applied to a wider population. This is the case whether or not the wider population is very large, such as the population of an entire country, or small, such as young females living in a specific town.
Non-probability sampling is when a sample is created through a non-random process. This could include a researcher sending a survey link to their friends or stopping people on the street. This type of sampling would also include any targeted research that intentionally samples from specific lists such as aid beneficiaries, or participants in a specific training course. Non-probability samples are often used during the exploratory stage of a research project, and in qualitative research, which is more subjective than quantitative research, but are also used for research with specific target populations in mind, such as farmers that grow maize.
Generally speaking, non-probability sampling can be a more cost-effective and faster approach than probability sampling, but this depends on a number of variables including the target population being studied. Certain types of non-probability sampling can also introduce bias into the sample and results. For general population studies intended to represent the entire population of a country or state, probability sampling is usually the preferred method.
Sampling Methods 101: Probability & Non-Probability Sampling Explained Simply
What is the difference between probability sampling and non-probability sampling?
In non-probability sampling, each unit in your target population does not have an equal chance of being included. Here, you can form your sample using other considerations, such as convenience or a particular characteristic. In probability sampling, each unit in your target population must have an equal chance of selection.
Why is non-probability sampling used in quantitative research?
However, non-probability sampling is often used in quantitative research because probability sampling is not always feasible. Going back to the college professor example, it may not be possible for you to select a random sample from all possible college professors in the general population.
What is a non-probability sample?
For example, visitors to a website that doesn’t require users to create an account could form part of a non-probability sample. Note that this type of sampling is at higher risk for research biases than probability sampling, particularly sampling bias. Be careful not to confuse probability and non-probability sampling.
What is the difference between probability sampling and non-random sampling?
Hence it is considered as Non-random sampling. In probability sampling, the sampler chooses the representative to be part of the sample randomly, whereas, in non-probability sampling, the subject is chosen arbitrarily, to belong to the sample by the researcher. The chances of selection in probability sampling, are fixed and known.
