Representative Sample

Representative Sample: Definition

A representative sample is defined as a small quantity or a subset of something larger, such that, it represents the same properties and proportions as that of it’s larger population.

For example, consider a brand that is about to launch a new product in a city in the USA. It will be practically impossible to send a survey to collect insights into the features of the product from every person in the city. Therefore, a small sample of people are collected who will represent the population of the city and a survey can be deployed to them to collect their feedback on the product. This sample is called a representative sample.

A representative sample could be people or even chemical substance in scientific studies, that can be taken to a laboratory and tested to analyze the result of any particular chemical reaction. However, in this blog, we will concentrate on people and understand what is the importance of representative population sample in market research and other helpful aspects.  

A representative sample allows the collected information to be abstracted to a greater population. For most of market research and psychological studies, it is unsuitable in terms of time, money and resources to collect data on everyone, it is practically impossible to collect data from each and every person especially, for a large population such as an entire country.  

The good news is, “you don’t need to do it!”. What is more important here is to get a good representative sample, so that the vast majority of your time and energy will go into getting responses from a small group of people who will represent a larger population.

Time and again research studies have employed a smaller group of people to conduct studies, collect data and analyze the results. Let us understand the importance of a representative sample for effective research studies.  

Learn more: Research Questions- Examples and Samples

Importance of a Representative Sample for Effective Research Studies

  • To carry out a successful market research, a representative sample will work in your favor. Can you imagine having to interview all the people in a country or even a city? It would definitely sound the most impractical plan, would definitely be extremely complicated and take a very long time.
  • A representative sample is a small number of people that reflect, as accurately as possible, a larger group. Then we can apply, for example, an online survey to a sample of the population looking for it to be the most representative of our target population.
  • We will not have better results if, for example, we send a survey without taking representativeness into account and we do not know who answers it and if the results really represent the opinion of our target audience.
  • If we do not have representativeness, surely we will have data that will not serve us at all. It is important that we guarantee that the characteristics that matter to us and that we need to investigate are found in the sample that is going to be studied.
  • Take into consideration that we will always have a bias in the sample, because there will always be people who will not answer the survey because of “n” number of reasons, or answer it incompletely. In this case, we cannot fully obtain the data that we require. Now regarding the sample size, the larger the sample size, it is more likely to closely represent the larger population.
  • A large representative sample gives us greater certainty that the people included are the ones we need, and we also reduce any possible bias. Therefore, if we want to avoid inaccuracy in our surveys, we must have representative and balanced samples.

Learn more: How to Calculate Survey Sample Size

How to Obtain a Representative Sample

There are two methods by which a researcher can obtain a representative sample:

1. Probability Sampling– Probability Sampling is defined as a sampling technique in which a sample from a larger population is chosen using a method based on the theory of probability. For a participant to be considered as a probability sample, he/she must be selected using a random selection.  

If we are going to use probability sampling to obtain a representative sample then simple random sampling is the best choice. The choice of the sample will be made at random, which guarantees that each member of the population will have the same probability of selection and inclusion in the sample group.

Learn more: Probability Sampling Methods- Stratified Random Sampling, Cluster Random Sampling, and Systematic Sampling

2. Non-Probability Sampling– Non-probability sampling is a sampling technique in which the researcher selects samples based on the subjective judgment of the researcher rather than random selection. In non-probability sampling, not all members of the population have a chance of participating in the study unlike probability sampling, where each member of the population has a known chance of being selected.

Knowing the demographic characteristics of the selected sample will undoubtedly help to limit the profile of the desired sample and define the variables that interest us, such as gender, age, place of residence, etc. By knowing these criteria, before obtaining the information, we can have the control to create a representative sample that is efficient for us. We must avoid having a sample that does not reflect the target population, the ideal is to have the most accurate data possible for the success of our project.  

Avoid Sampling Errors

When a sample is not representative, then we will have a sampling error also known as margin of error. If we want to have a representative sample of 100 employees, then we must choose a similar number of men and women. For example, if we have a sample inclined to a certain genre, then we will have an error in the sample.

The sample size is very important, but it does not guarantee that it accurately represents the population that we need. More than size, representativeness is more related to the sampling frame, that is, to the list from which people are selected, for example, part of a survey. Therefore, we must take care that people from our target audience are included in that list to say that it is a representative sample.

Learn more: How to Calculate Margin of Error

Representative Sample Example

A group of citizens representing the whole country is designated as a national representative sample. It is used to carry out research that projects a result that reflects the national reality, it can be preferences of any kind, behavior or socio-demographic profiles.

At its best, the representative sample will give the impression of being the total population, regardless of how it looks. The numbers of men vs. women must match the national proportions, the percentage in each age group or each region will exactly match the population, etc. In non-demographic measures (such as product ownership or psychographic segmentation) the sample must match the population.

Let’s take the example of age: if the quotas are set at 16 to 34, 35 to 54, or greater than 55, the sample will be representative within these proportions, but if the analysis is done in age ranges 16 to 20, 21 a 30, 31 to 40, etc., there is no guarantee that the sample will remain correct.

The extent to which quota control in a sample is possible depends on the size of the sample and the reference data available in a survey. Six periods of age, two genera, and 15 regions create a grid of 180 cells. If the sample size is only 100, it is not possible to fill all the cells. Even with a larger sample size, a cell may require only half a person, and therefore it will not have the data in it.

To make a sample more representative, weighting can be used. As an alternative to interlaced cells, the quota cells can be structured independently. The disadvantage here is that there may be large “gaps” in the sample, if all the youth are men, for example, it will not be possible to use the weighting to correct the gaps.

Learn more : Free Demographic Survey Examples