Representative-sample

Representative Sample: Definition, method and examples

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What is a representative sample?

One can describe representative samples as a small quantity or part or subset of a larger set or entity. It has the same properties as that of the larger population of which it is a representative of. 

Lets take for example a brand which is about to release a new service or product in Bavaria. Germany. It would be impossible for the brand to survey each and every person in Bavaria to gauge their reception towards the launch. As an alternative, researchers can gather a small sample from different parts of Bavaria, who will represent that region’s population. The respondents will then be surveyed to get their feedback on the product/service. This sample of people who were chosen from Bavaria is called a “representative sample”.

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Importance of representative samples in research

  • A representative sample is essentially a small amount of individuals who reflect the properties of your target demographic with a high degree of accuracy. This cuts out the need to survey the entire target demographic.
  • Without surveying a sample, launching a product or passing a new law would be akin to shooting in the dark. One would have no idea about the opinion of the target demographic, and how they would receive said changes.
  • Without representative samples, all collected data would not accurately reflect how perceptions are on the ground. Which is why samples need to be selected carefully.
  • It is required for any successful market research study- the logistics of interviewing an entire demographic (it could be a city, a state or even an entire country) are just not feasible.
  • Larger samples give more clarity about the target demographic, and help reduce bias in your findings. 
  • Larger representative samples also help cover for people dropping off while partaking in your surveys. It would make the impact of incomplete surveys negligible.

How to build and develop a representative sample

There are two techniques available which researchers can use to create representative samples. They are probability sampling and non-probability sampling.

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Probability Sampling in Representative Sample

Probability Sampling

This is a technique based on the probability theory, in which analysts or researchers select a sample from a larger population. Participants must be selected via a randomized selection process to be considered a probability sample.

Simple random sampling is the ideal choice if one wishes to use probability to acquire a representative sample. Sample choices are random, which ensures that all members of the demographic will have approximately the same probability of being included or selected in the sample.

Non-Probability Sampling

Non-probability sampling is a method in which analysts and researchers select samples depending on their judgment (which is subjective) rather than randomly selecting respondents. When it comes to non-probability sampling, not every member in the population has a chance to participate in the study directly as opposed to probability sampling, where all members of the target demographic have a chance to be selected for the study.

As researchers are aware of the selected representative sample’s demographic/population characteristics, it helps keep a limit on the profile of the target sample. Researchers can define the variables that are of interest to them, such as locality, age, race, gender, etc. By being aware of these attributes even before obtaining the information, researchers have the ability to create a representative sample that is ideal for their requirements. However, they must be sure to avoid developing a sample that does not reflect the target demographic. The key aim being that the research study has the most accurate data possible.

How to avoid sampling errors

Should a sample not be a representative one, it creates a problem known as margin of error. Let’s say one wants to have a representative sample of 200 employees working in a large organization. You must select a similar number of people from men and women. If your sample is biased, the results you receive will be biased as well. 

Another thing to keep in mind is sample size – it needs to be as large as realistically possible within your budget. Tools like Voxco panel manager can help select the right sample for your surveys, and nurture respondents. It is necessary to include people from every type of demographic in your sample for honest and accurate feedback.

What your survey software must have to effectively research representative samples

Omnichannel data collection

Your target demographic can be anywhere, and not all of them will have constant access to the internet. That is why Voxco’s omnichannel survey software allows researchers and analysts to collect feedback from representative samples across all channels – online, face-to-face and via telephone.

This ensures that your research has accurate data, and that geographical barriers do not come in the way of your surveys.

Analytics and Dashboards

Uncovering insights from your sample requires capable analytics tools. Voxco’s analytics can help find relationships between your demographic and the topic of your research. Findings are conveniently displayed on intuitive dashboards- which relay the right information and help you make sense of the data you’ve collected.

Rewards integration

The accuracy of representative sample data depends on the number of people you’ve surveyed.

To entice more people to partake in your surveys you can consider incentivizing them.Incentivizing surveys also ensures that your drop off rates are low, which also helps in ensuring that your survey data is accurate. Voxco’s online survey software can seamlessly integrate rewards into your surveys which are tied to survey completion, ensuring that rewards are presented only to respondents who have completed the survey.

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