Last Updated on August 12, 2020
If you are wondering what is convenience sampling and how it can help you, then you have arrived at the right place. Today we are going to check out everything there is to know about convenience sampling starting with the definition and ending tips that will help you efficiently analyze convenience sampling data.
Convenience Sampling – Definition and Examples
The first thing that we want to mention about convenience sampling is that you might also find it referenced as grab sampling, accidental sampling, or opportunity sampling. The reason behind this is that the method is adopted by researchers to collect market research data from a conveniently available pool of respondents. The biggest benefit of choosing this method is that it is prompt, simple, and more importantly, economical.
If you are still wondering what exactly does this survey method do, then you should think of this method as Facebook polls or questions. Using this method to collect samples will help you get access to the first available primary data source that can be used for research without any other requirements. The secret is that you will involve participants wherever you can find them and where it is convenient for them. You do not need to make any subject selection.
The Top 5 Advantages of Convenience Sampling
Check out below the most significant advantages of implementing convenience sampling:
- The simplicity of sampling and the ease of research;
- The data is collected quickly and effortlessly;
- It is cheap to create samples;
- There are not many rules that you need to follow;
- You can use students when on a small budget to avoid big expenses.
What is the Best Way to Analyze Sample Data?
Since convenience sampling will help you get results quickly and with ease, the biggest challenge comes when analyzing the data. Fortunately, there are three main rules that you can follow to efficiently the sampling data. Read them below:
- Take as many samples as possible.
- Repeat the survey multiple times to get a better understanding of the results;
- When using big samples, make sure to cross-validate half the data.