Which of the following techniques or methods is most likely to result in a representative sample?

Representative sample definition: A representative sample is defined as a small quantity or a subset of something larger. It represents the same properties and proportions like that of a larger population.

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

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

Why must you use a representative sample in research?

A representative sample allows researchers to abstract the collected information to a larger population. Most market research and psychological studies are unsuitable in terms of time, money, and resources to collect data on everyone. It is practically impossible to collect data from each 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 significant research studies.  

Importance of a representative sample for practical research studies

• A representative sample will work in your favor to carry out successful market research. Can you imagine having to interview all the people in a country or even a city? It would sound the most impractical plan, be too complicated, and take a long time.
• A representative sample is a small number of people who reflect a more extensive group as accurately as possible. 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 represent the opinion of our target audience.
• If we do not have representativeness, indeed, we will have data that will not serve us at all. We must guarantee that the sample carries the characteristics that matter to us for investigation.
• 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 various 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 represent the broader population closely.
• 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.

How to build a representative sample

Researchers use two methods to build representative samples – Probability sampling and non-probability sampling

1. Probability sampling: Probability sampling is a technique in which a researcher chooses a sample from a larger population using a method based on the probability theory. For a participant to be considered a probability sample, he/she must be selected using a random selection.

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

2. Non-probability sampling: Non-probability sampling is a sampling technique in which the researcher selects samples based on the researcher’s subjective judgment rather than random selection. In non-probability sampling, not all population members 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 selected sample’s demographic characteristics will undoubtedly help 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. We must avoid having a sample that does not reflect the target population. The idea is to have the most accurate data possible for our project’s success.  

Avoid sampling errors for better representation

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

The sample size is essential, but it does not guarantee that it accurately represents the population that we need. More than size, representativeness is 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.

Example of a representative sample

A group of citizens representing the whole country is designated as a nationally representative sample. Researchers use it to reflect and project 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 its 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.

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Let’s take the example of age: if the researcher sets quotas at 16 to 34, 35 to 54, or greater than 55, the sample will be represented within these proportions. But if he/she analyzes 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 sample’s size 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 section may require only half a person, and therefore it will not have the data in it.

Weighting can be used to make a sample more representative. As an alternative to interlaced cells, the quota cells can be structured independently. The disadvantage here is that there may be considerable “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.

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Surveys would be meaningless and incomplete without accounting for the respondents that they’re aimed at. The best survey design practices keep the target population at the core of their thought process.

‘All the residents of the Dharavi slums in Mumbai’, ‘every NGO in Calcutta’ and ‘all students below the age of 16 in Manipur’ are examples of a population; they are countable, finite and well-defined.

When the population is small enough, researchers have the resources to reach out to all of them. This would be the best case scenario, making sure that everybody who matters to the survey is represented accurately. A survey that covers the entire target population is called a census.

However, most surveys cannot survey the entire population. This is when sampling techniques become crucial to your survey.

Why Is It Important?

Resource Constraints

If the target population is not small enough, or if the resources at your disposal don’t give you the bandwidth to cover the entire population, it is important to identify a subset of the population to work with – a carefully identified group that is representative of the population. This process is called survey sampling, and it is one of the most important aspects of survey design.

Whatever the sample size, there are fixed costs associated with any survey. Once the survey has begun, the marginal costs associated with gathering more information, from more people, are proportional to the size of the sample.

Drawing Inferences About the Population

Researchers are not interested in the sample itself, but in the understanding that they can potentially infer from the sample and then apply across the entire population.

A sample survey usually offers greater scope than a census. Working within a given resource constraint, sampling may make it possible to study the population of a larger geographical area or to find out more about the same population by examining an area in greater depth through a smaller sample.

Before we dive into the survey sampling methods at our disposal it is imperative that we develop a perspective on what an effective sample should look like.

3 Features to Keep in Mind While Constructing a Sample

Consistency

It is important that researchers understand the population on a case-by-case basis and test the sample for consistency before going ahead with the survey. This is especially critical for surveys that track changes across time and space where we need to be confident that any change we see in our data reflects real change – across consistent and comparable samples.

Diversity

Ensuring diversity of the sample is a tall order, as reaching some portions of the population and convincing them to participate in the survey could be difficult. But to be truly representative of the population, a sample must be as diverse as the population itself and sensitive to the local differences that are unavoidable as we move across the population.

Transparency

There are several constraints that dictate the size and structure of the population. It is imperative that researchers discuss these limitations and maintain transparency about the procedures followed while selecting the sample so that the results of the survey are seen with the right perspective.

Now that we understand the necessity of choosing the right sample and have a vision of what an effective sample for your survey should be like, let’s explore the various methods of constructing a sample and understand the relative pros and cons of each of these approaches.

Sampling methods can broadly be classified as probability and non-probability.

3 Probability Sampling Techniques

When each entity of the population has a definite, non-zero probability of being incorporated into the sample, the sample is known as a probability sample.

Probability samples are selected in such a way as to be representative of the population. They provide the most valid or credible results because they reflect the characteristics of the population from which they are selected.

Probability sampling techniques include random sampling, systematic sampling, and stratified sampling.

Random Sampling

When: There is a very large population and it is difficult to identify every member of the population.

How: The entire process of sampling is done in a single step with each subject selected independently of the other members of the population. The term random has a very precise meaning and you can’t just collect responses on the street and have a random sample.

Pros: In this technique, each member of the population has an equal chance of being selected as subject.

Cons: When there are very large populations, it is often difficult to identify every member of the population and the pool of subjects becomes biased. Dialing numbers from a phone book for instance, may not be entirely random as the numbers, though random, would correspond to a localized region. A sample created by doing so might leave out many sections of the population that are significant to the study.

Use case: Want to study and understand the rice consumption pattern across rural India? While it might not be possible to cover every household, you could draw meaningful insights by building your sample from different districts or villages (depending on the scope).

Systematic Sampling

When: Your given population is logically homogenous.

How: In a systematic sample, after you decide the sample size, arrange the elements of the population in some order and select terms at regular intervals from the list.

Pros: The main advantage of using systematic sampling over simple random sampling is its simplicity. Another advantage of systematic random sampling over simple random sampling is the assurance that the population will be evenly sampled. There exists a chance in simple random sampling that allows a clustered selection of subjects. This can be avoided through systematic sampling.

Cons: The possible weakness of the method that may compromise the randomness of the sample is an inherent periodicity of the list. This can be avoided by randomizing the list of your population entities, as you would randomize a deck of cards for instance, before you proceed with systematic sampling.

Use Case: Suppose a supermarket wants to study buying habits of their customers. Using systematic sampling, they can choose every 10th or 15th customer entering the supermarket and conduct the study on this sample.

Stratified Sampling

When: You can divide your population into characteristics of importance for the research.

How: A stratified sample, in essence, tries to recreate the statistical features of the population on a smaller scale. Before sampling, the population is divided into characteristics of importance for the research — for example, by gender, social class, education level, religion, etc. Then the population is randomly sampled within each category or stratum. If 38% of the population is college-educated, then 38% of the sample is randomly selected from the college-educated subset of the population.

Pros: This method attempts to overcome the shortcomings of random sampling by splitting the population into various distinct segments and selecting entities from each of them. This ensures that every category of the population is represented in the sample. Stratified sampling is often used when one or more of the sections in the population have a low incidence relative to the other sections.

Cons: Stratified sampling is the most complex method of sampling. It lays down criteria that may be difficult to fulfill and place a heavy strain on your available resources.

Use Case: If 38% of the population is college-educated and 62% of the population have not been to college, then 38% of the sample is randomly selected from the college-educated subset of the population and 62% of the sample is randomly selected from the non-college-going population. Maintaining the ratios while selecting a randomized sample is key to stratified sampling.

3 Non-Probability Sampling Techniques

Non-probability sampling techniques include convenience sampling, snowball sampling and quota sampling.

In these techniques, the units that make up the sample are collected with no specific probability structure in mind. The selection is not completely randomized, and hence the resultant sample isn’t truly representative of the population.

Convenience Sampling

When: During preliminary research efforts.

How: As the name suggests, the elements of such a sample are picked only on the basis of convenience in terms of availability, reach and accessibility.

Pros: The sample is created quickly without adding any additional burden on the available resources.

Cons: The likelihood of this approach leading to a sample that is truly representative of the population is very poor.

Use Case: This method is often used during preliminary research efforts to get a gross estimate of the results, without incurring the cost or time required to select a random sample.

Snowball Sampling

When: When you can rely on your initial respondents to refer you to the next respondents.

How: Just as the snowball rolls and gathers mass, the sample constructed in this way will grow in size as you move through the process of conducting a survey. In this technique, you rely on your initial respondents to refer you to the next respondents whom you may connect with for the purpose of your survey.

Pros: The costs associated with this method are significantly lower, and you will end up with a sample that is very relevant to your study.

Cons: The clear downside of this approach is that you may restrict yourself to only a small, largely homogenous section of the population.

Use Case: Snowball sampling can be useful when you need the sample to reflect certain features that are difficult to find. To conduct a survey of people who go jogging in a certain park every morning, for example, snowball sampling would be a quick, accurate way to create the sample.

Quota Sampling

When: When you can characterize the population based on certain desired features.

How: Quota sampling is the non-probability equivalent of stratified sampling that we discussed earlier. It starts with characterizing the population based on certain desired features and assigns a quota to each subset of the population.

Pros: This process can be extended to cover several characteristics and varying degrees of complexity.

Cons: Though the method is superior to convenience and snowball sampling, it does not offer the statistical insights of any of the probability methods.

Use Case: If a survey requires a sample of fifty men and fifty women, a quota sample will survey respondents until the right number of each type has been surveyed. Unlike stratified sampling, the sample isn’t necessarily randomized.

Related read: When to do quota sampling and how to do it correctly

Probability sampling techniques are superior, but the costs can be prohibitive. For the initial stages of a study, non-probability sampling techniques might be sufficient to give you a sense of what you’re dealing with. For detailed insights and results that you can bank upon, move on to the more sophisticated techniques as the study gathers pace and takes a more concrete structure.

Once you have created your sample, go ahead and start creating an effective survey by choosing the right survey question types.

Photo by Benny Jackson on Unsplash

Note: This article was originally published on 27 April 2015, then refreshed and updated on 25 July 2017.