6 Types of Data Sampling (and the Best Practices for Each)

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Shannon FlynnManaging Editor at ReHack

Friday, October 8, 2021

Data sampling, the process of selecting a subset of a larger population for research purposes, is a necessary step when creating surveys or generating market research.

Article 4 Minutes
6 Types of Data Sampling (and the Best Practices for Each)

Different strategies for data sampling can make it easier or harder to conduct a survey, and may impact the quality of the survey results. These strategies also require different best practices to work well.

These are six of the most common approaches to data sampling, and how business researchers should apply them to create the most accurate and most comprehensive information possible.

Two categories of data sampling types

All types of data sampling can be grouped into one of two categories:

  • Probability sampling: This involves random selection and attempts to guarantee that all members of a population could be selected. These sampling methods allow researchers to make stronger inferences about the population they are studying.
  • Non-probability sampling: This involves non-random selection based on criteria like convenience. These sampling techniques are often easier to implement but can make inferences harder to defend.

While probability sampling methods tend to produce more accurate results, they also tend to be more expensive and more time-consuming in practice.

Non-probability sampling methods are typically more accessible to businesses but produce less accurate data. They are useful for data scientists who want to generate ideas and feel out a hypothesis before committing to a more extensive survey.

Businesses often use a combination of both sampling methods to produce data sets large enough to yield unique insights. Large-scale surveys are one common source of “big data,” which companies typically use for a few different purposes — like improving personalization, identifying new market trends and developing better security strategies.

Types of probability sampling

1. Simple random sampling

Everyone in the population — the pool of people you’re studying and will draw your sample from — has an equal chance of being selected. Researchers draw participants at random until there’s a sample size large enough to draw conclusions about the population.

For simple random sampling, effective sample sizes will be determined primarily by mathematical factors — like the population size, desired confidence interval and margin of error.

These studies typically work best when the population is small and likely to respond to a survey request — like all the employees at a single office. To conduct this survey, the business will simply compile a list of everyone in the population and use a random generator to select people to survey.

Organizing the data needed to collect a random sample — as well as the sample data — will also require the right data management techniques. Management strategies that work for big data are often useful, especially when the population being studied is very large.

2. Stratified sampling

Like random sampling, but the overall population is divided into important subgroups that the survey designers want to be represented.

A business surveying its own employees may divide its workforce into departments, then randomly sample each department individually to ensure each department is represented equally in the final sample.

Another may do the same for all the customers in a particular geographic area, grouping them into segments with characteristics like age and income bracket properly represented.

3. Cluster sampling

Cluster sampling is similar to stratified sampling, but each subgroup is instead designed to reflect the overall structure of the population.

Instead of dividing an office population into departments, for example, you’d divide it into clusters of people, where each cluster has an equal mix of people from different departments. Then, you select only certain clusters to study.

This approach can help to simplify the study of a large and distributed population if each cluster contains only respondents from the same geographical area.

Types of non-probability sampling

4. Convenience sampling

The study designers interview whoever is closest, or whoever responds to their survey request. This approach can produce biased results but is a common strategy for businesses wanting quick feedback.

An email survey, for example, sent to recent customers can be a great way to use convenience sampling to develop a rough idea of how a business’s current customers feel about the services it provides.

5. Snowball sampling

Researchers contact a few individuals from the population to start, then encourage respondents to refer people from their network to the study organizers.

This approach can be useful when researchers know very little about the group they’re studying, to the point that they’re not sure of its size and have little contact information for possible study participants. The downside of the approach is that it’s easy to survey just one clique or social circle.

This sampling method can be effective for businesses wanting to learn more about potential customers they haven’t had contact with yet.

6. Expert sampling

Survey designers sometimes go straight to the experts.

This strategy typically works best when researchers want to know the current range of expert opinions on a topic, but don’t need to know what the average person may think.

Which data sampling method is best for my business?

Because each of these sampling methods can produce extremely different results, choice of method and implementation will be important.

For early, experimental research, a non-probability sampling method may work well. These surveys are generally easier to run than probability sampling-based surveys — though the data they produce won’t be as quality or comprehensive.

If high-quality data is needed, probability sampling methods may be more appropriate.

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Shannon Flynn

Managing Editor at ReHack

rehack.com

Shannon Flynn is a tech journalist with experience in business technology and consumer IT. She has contributed to sites like International Policy Digest, RobotLab, Finovate and more. Visit ReHack.com for more techie reads by Shannon.

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