Importance of Sampling

Types of Sampling

There are many different types of sampling methods, here’s a summary of the most common:

Cluster sampling

Units in the population can often be found in certain geographic groups or “clusters” for example, primary school children in Derbyshire.

A random sample of clusters is taken, then all units within the cluster are examined.

Advantages

• Quick and easy
• Doesn’t need complete population information
• Good for face-to-face surveys

Disadvantages

• Expensive if the clusters are large
• Greater risk of sampling error

Convenience sampling

Uses those who are willing to volunteer and easiest to involve in the study.

Advantages

• Subjects are readily available
• Large amounts of information can be gathered quickly

Disadvantages

• The sample is not representative of the entire population, so results can’t speak for them inferences are limited.
• Prone to volunteer bias

Judgement sampling

A deliberate choice of a sample the opposite of random

Advantages

• Good for providing illustrative examples or case studies

Disadvantages

• Very prone to bias
• Samples often small
• Cannot extrapolate from sample

Quota sampling

The aim is to obtain a sample that is “representative” of the overall population.

The population is divided (“stratified”) by the most important variables such as income, age and location. The required quota sample is then drawn from each stratum.

Advantages

• Quick and easy way of obtaining a sample

Disadvantages

• Not random, so some risk of bias
• Need to understand the population to be able to identify the basis of stratification

Simply random sampling

This makes sure that every member of the population has an equal chance of selection.

Advantages

• Simple to design and interpret
• Can calculate both estimate of the population and sampling error

Disadvantages

• Need a complete and accurate population listing
• May not be practical if the sample requires lots of small visits over the country

Systematic sampling

• After randomly selecting a starting point from the population between 1 and *n, every nth unit is selected.

*n equals the population size divided by the sample size.

Advantages

• Easier to extract the sample than via simple random
• Ensures sample is spread across the population

Disadvantages

• Can be costly and time consuming if the sample is not conveniently located

Importance of Sampling Design

Save Time

Contacting everyone in a population takes time. And, invariably, some people will not respond to the first effort at contacting them, meaning researchers have to invest more time for follow-up. Random sampling is much faster than surveying everyone in a population, and obtaining a non-random sample is almost always faster than random sampling. Thus, sampling saves researchers lots of time.

Save Money

The number of people a researcher contacts is directly related to the cost of a study. Sampling saves money by allowing researchers to gather the same answers from a sample that they would receive from the population.

Non-random sampling is significantly cheaper than random sampling, because it lowers the cost associated with finding people and collecting data from them. Because all research is conducted on a budget, saving money is important.

Collect Richer Data

Sometimes, the goal of research is to collect a little bit of data from a lot of people (e.g., an opinion poll). At other times, the goal is to collect a lot of information from just a few people (e.g., a user study or ethnographic interview). Either way, sampling allows researchers to ask participants more questions and to gather richer data than does contacting everyone in a population.

The Importance of Knowing Where to Sample

Efficient sampling has a number of benefits for researchers. But just as important as knowing how to sample is knowing where to sample. Some research participants are better suited for the purposes of a project than others. Finding participants that are fit for the purpose of a project is crucial, because it allows researchers to gather high-quality data.

For example, consider an online research project. A team of researchers who decides to conduct a study online has several different sources of participants to choose from. Some sources provide a random sample, and many more provide a non-random sample. When selecting a non-random sample, researchers have several options to consider. Some studies are especially well-suited to an online panel that offers access to millions of different participants worldwide. Other studies, meanwhile, are better suited to a crowdsourced site that generally has fewer participants overall but more flexibility for fostering participant engagement.

To make these options more tangible, let’s look at examples of when researchers might use different kinds of online samples.