Cluster Sampling vs. Convenience Sampling


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Both cluster sampling and convenience sampling have their place in the world of market research. This article will explore both the pros and cons of each method. First, let’s define what these two sampling methods are all about.

Cluster sampling means that the entire population of your sampling method is divided into small groups and that data is derived from each group. Each of the groups should look relatively similar to one another. So, for instance, if you have a sample size of 100 people in your population, you may divide them into groups so that there is one person in each age range in each group.

Convenience sampling is when you simply include the people who are easiest to reach. This may involve reaching out to people who have participated in studies before, or who are already in your customer base and might receive communications from your company. Convenience sampling could even mean just reaching out to the first people you walk past on the street. Convenience sampling has long been considered a less accurate method of sampling, since a general population may not be qualified to participate, but it can be convenient for any emergency or exploratory study.

Cluster sampling cons

This method of sampling runs the risk of higher sampling errors because of the potential for bias. The very act of separating your population out based on details like age, race, or location could yield uneven grouping, which could disproportionally impact insights.

For instance, perhaps you group people based on race and location, but everyone in one group is in the same economic bracket. This overlooked demographic detail could result in lopsided or inconclusive responses, especially (in the case of economic class) if your research is behavioral.

Convenience sampling cons

Convenience sampling runs a higher risk of bias from respondents, as respondents may simply be willing or eager to respond to earn their incentive, and not because they are a good fit for the study. During a packaging concept test, people responding in a convenience sampling situation may not actually use your product (even though you might prefer that your respondent do so). During an ad concept test, a convenience sample might not have seen your existing ads.

Cluster sampling pros

Cluster sampling requires fewer resources (meaning it’s often less expensive) and therefore makes the obtaining of data more feasible. It is also easier to collect the data from small groups of respondents, unless you use an AI-powered tool to organize your data.

Convenience sampling pros

Convenience sampling is usually one of the least expensive means of data collection, as you aren’t seeking out respondents in the same way as other sampling methods. It also requires far less time. With other means of data collection you are working to seek out respondents, perhaps even doing promotional events or offering rewards for responses. With convenience sampling, your sample is random, which means that you save time and money by declining to filter.

Last words on sampling

Both convenience sampling and cluster sampling have the potential for bias, but in different ways. In cluster sampling the potential is in the actual clustering process, whereas in convenience sampling the bias with who is willing and nearby enough to participate. They also both have benefits when it comes to saving money and time.

Depending on the kind of market research you are seeking, and the answers you’re hoping to get out of that research, either of these methods may be useful to you. Either way, all of these aspects should be taken into consideration when you develop your sampling method and start to pursue your market research.

Emily Smith
Emily is the Content Director at Remesh, where she spends most of her time spinning data in stories.