What is convenience sampling?
Definition: Convenience sampling is defined as a method adopted by researchers where they collect market research data from a conveniently available pool of respondents. It is the most commonly used sampling technique as it’s incredibly prompt, uncomplicated, and economical. In many cases, members are readily approachable to be a part of the sample.
Researchers use various sampling techniques in situations where there are large populations. In most cases, testing the entire community is practically impossible because they are not easy to reach. Researchers use convenience sampling in situations where additional inputs are not necessary for the principal research. There are no criteria required to be a part of this sample. Thus, it becomes incredibly simplified to include elements in this sample. All components of the population are eligible and dependent on the researcher’s proximity to get involved in the sample.
The researcher chooses members merely based on proximity and doesn’t consider whether they represent the entire population or not. Using this technique, they can observe habits, opinions, and viewpoints in the easiest possible manner.
A good example of convenience sampling is: A new NGO wants to establish itself in 20 cities. It selects the top 20 cities to serve based on the proximity to where they’re based.
Applications of convenience sampling:
Convenience sampling is applied by brands and organizations to measure their perception of their image in the market. Data is collected from potential customers to understand specific issues or manage opinions of a newly launched product. In some cases, it is the only available option. For example, a university student working on a project and wants to understand the average consumption of soda on campus on a Friday night will most possibly call his/her classmates and friends and ask how many cans of soda they consume. Or may go to a party nearby and conduct an easy survey. There is always a chance that the randomly selected population may not accurately represent the population of interest, thus increasing the chances of bias.
Convenience sampling examples:
A basic example of a convenience sampling method is when companies distribute their promotional pamphlets and ask questions at a mall or on a crowded street with randomly selected participants.
Businesses use this sampling method to gather information to address critical issues arising from the market. They also use it when collecting feedback about a particular feature or a newly launched product from the sample created.
During the initial stages of survey research, researchers usually prefer using convenience sampling as it’s quick and easy to deliver results. Even if many statisticians avoid implementing this technique, it is vital in situations where you intend to get insights in a shorter period or without investing too much money.
For instance, a marketing student needs to get feedback on the “scope of content marketing in 2020.” The student may quickly create an online survey, send a link to all the contacts on your phone, share a link on social media, and talk to people you meet daily, face-to-face.
Top six advantages of using convenience sampling
Here are the advantages of adopting a convenience sampling approach:
- Collect data quickly: In situations where time is a constraint, many researchers choose this method for quick data collection. The rules to gather elements for the sample are least complicated in comparison to techniques such as simple random sampling, stratified sampling, and systematic sampling. Due to this simplicity, data collection takes minimal time.
- Inexpensive to create samples: The money and time invested in other probability sampling methods are quite large compared to convenience sampling. It allows researchers to generate more samples with less or no investment and in a brief period.
- Easy to do research: The name of this surveying technique clarifies how samples are formed. Elements are easily accessible by the researchers and so, collecting members for the sample becomes easy.
- Low cost: Low cost is one of the main reasons why researchers adopt this technique. When on a small budget, researchers – especially students, can use the budget in other areas of the project.
- Readily available sample: Data collection is easy and accessible. Most convenience sampling considers the population at hand. Samples are readily available to the researcher. They do not have to move around too much for data collection. Quotas are met quickly, and the data collection can commence even within a few hours.
- Fewer rules to follow: It doesn’t require going through a checklist to filter members of an audience. Here, gathering critical information and data becomes uncomplicated. For instance, if an NGO wants to survey women’s empowerment, they can go to schools, colleges, offices, etc. in their proximity and gather quick responses.
How to reduce bias in convenience sampling?
The best way of reducing bias in convenience sampling is to use it along with probability sampling. Since it is usually biased, probability sampling gets the measurement parameter with it to keep this sampling bias under check.
After receiving a fair idea about this bias using probability sampling, the researcher can use both convenience sampling and probability sampling techniques to draw a more accurate estimation. The probability aspect used, along with convenience sampling, will have to be powerful enough to overcome it. Bias can make the entire sample futile, and that’s the last thing that a researcher needs. This bias can be reduced or eliminated by including probability sampling.
How to efficiently analyze convenience sampling data?
Here are three quick hacks to efficiently analyze convenience sampling data. It is best to use probability sampling, but when that is not possible, here are three hacks you should keep in mind.
- Take multiple samples. It helps you in producing reliable results.
- Repeat the survey to understand whether your results truly represent the population.
- For a big sample size, try cross-validation for half the data. Then compare the findings with the other half of the data.