Representative samples surveys are a part of population surveys. Statistics often involves studying the characteristics of a specific group of people. A study for a particular group of people looks like this:
- The overall job satisfaction of mechanical engineers.
- Healthcare brand preferences of individuals.
- The age distribution of vice presidents.
- Technological tool preferences of a digital marketing company, and the list continues.
In each of these examples, a specific group of people is studied for particular attributes. However, this process of jotting down data for every individual in a group is time-consuming and costly.
Limiting the sample size could avoid such lengthy and resource-intensive processes. The findings of the smaller population are used to generalize the possible trends of the larger population. This smaller sample is called a representative sample.
Formal Definition of Representative Sample
The term representative sample refers to sampling a small part of a larger group. It exhibits the same type of trends and characteristics as that of a larger set.
For example, consider a brand vying to launch its new product in Hanoi city. It will be a next-to-impossible task for a researcher to collect insight regarding all the nitty-gritty about the taste of Hanoi’s total customer population.
Hence, the researcher will first collect insights into the product’s features from a small sample set of customers. These customers should be the representative of the whole city’s population. A survey can also be sent to these sample customers to determine what customers like and dislike about the product. This is a classic example of a representative sample!
Representative sampling is not limited to selecting a human sample set. This survey can also study how certain types of chemical substances react in a chemical reaction. However, our write-up will only consider how representative samplings occur when the sample set constitutes only humans!
Why Do Businesses Use Representative Samples for New Areas?
Representative surveys are brief of the larger population. Every representative survey forms an abstract to collect information regarding a vast and varied set of people.
Most long-form market researching protocols are time and money-sucking. However, the good news is that expert market researchers can conduct representative sample research on behalf of a company.
The following are the reason why the representative survey is so popular among market researchers:
To save time and effort, researchers might conduct interviews with a representative sample of the population instead of a completely unknown one. Instead, it interviews a select sample of people to find the best way to achieve a goal.
When conducted properly, representative surveys can correctly reflect a sizable population’s preferences. Experts in the field of market research are familiar with the strategies for conducting online sample surveys that elicit respondents’ most candid feedback.
They can also see the broader picture and apply narrowly focused solutions. With the help of this link, we can get a feel for the preferences of the largest possible audience.
If you skip the survey and go straight to the product launch, you may be disappointed with the outcome. In the time leading up to its release to consumers, nearly every product carries some prejudice.
For instance, if a pasta manufacturer from the United States were to try to introduce its products in Hanoi, it might need to be made aware of the local palate. Locals in Hanoi frequently eat rice noodles for dinner. To counter this, most American pasta manufacturers use wheat or flour.
If they do a thorough survey first, they could avoid losing money by sending wheat pasta to Hanoi. Yet, by conducting a statistically valid poll, the corporation can determine that switching to rice pasta will result in a successful product launch.
Methods of Conducting a Random Sampling
Probability Sampling Method
In probability sampling, every member of the subset population is included in the final subset. It is primarily used in quantitative research. Suppose a market researcher is inclined to produce results representative of the whole population. In that case, the probability sampling technique works best there.
There are four subsets of probability sampling:
1. Simple random sampling
The very basics of random sampling are simple random sampling. A simple random sample has an equal chance of selecting every member of the population. The sampling framing encircles the whole population.
Generally, random number generators or other techniques are the most used tools for this.
2. Systematic Sampling
In many ways, systematic sampling resembles simple random sampling. This process is less complicated and widely used. Every member of this sample subset is enlisted with a number.
However, in this method, numbers are not generated randomly. The individuals for a sample subset are chosen at regular intervals.
3. Stratified Sampling
Stratified sampling involves the division of populations into subpopulations that may differ in the most critical ways. This method has enough room to accommodate every subgroup adequately. It also helps to generate precise conclusions based on the relevant characteristics of a subset. These characteristics range from gender, age, income, job role, etc.
A researcher can calculate how many people should be sampled from each group based on the overall proportions.
4. Cluster Sampling
Cluster sampling involves dividing a population into multiple subgroups. Here, each subgroup should bear similar characteristics to the whole sample. The method needs to sample based on proper analysis. But, it only chooses its samples randomly.
In this method, clusters are generally significant. However, some researchers also customize the size of a subset. For its flexibility, the technique is known as multistage sampling.
The method generally deals with large and dispersed populations. However, due to its complexity, the sample may yield some things that need to be corrected. At times the subsets may not properly represent the whole population.
In non-probability sampling, individuals are selected randomly. The opportunity to participate in this program is not available to every individual.
However, these sampling techniques are less complicated and more affordable. However, it comes with a higher chance of sampling bias. A sampling bias can make the interfaces about a population weaker. The challenge for a market researcher using a non-probability sample is to make it representative of the actual population as closely as possible.
Non-probability sampling is used for exploratory and qualitative research. These research methods aim not to test a hypothesis about an actual population but to develop a knowledge base for a small under-research population cluster.
A convenience sample includes individuals who remain the most accessible to a market researcher.
This is an inexpensive medium to gather data. However, the sample cannot mimic the actual population test.
It generally yields generalized results and carries the risk of sample and selection bias.
Voluntary Response Sampling
Similar to convenience sampling, voluntary response sampling is based on ease of access. Instead of a researcher choosing participants or directly contacting them in this method, people spontaneously volunteer themselves.
Voluntary sampling always has the threat of sample bias. As a group of people will be more spontaneous to take part than the rest, voluntary response sampling always carries some self-selection bias.
This type of sampling is also termed judgment sampling. Purposive sampling reverts around a researcher’s ability to select a sample that suits the research purpose the most.
Often this type of sampling is known as quantitative sampling. In this sampling, a researcher aims to gain detailed knowledge about a particular phenomenon. Generally, no statistical interference is not chosen for this sampling. As a result of this sampling, the sample size is minimal and specific.
An effective purposive sampling progresses following a clear idea and chalked-out rationale. In this sampling, an observer can make inclusion and exclusion based on observer bias affecting sampling arguments.
There are a number of population clusters that need to be addressed. Participants can be recruited by snowball sampling by working with other participants.
As the point of contact increases, the snowballs keep including more and more people to it.
The flip side of this technique is representativeness. In this sampling method, a researcher will never know how well the representation has happened due to primary reliance on participants who recruit others. Generally, snowball sampling has some sample biases.
To Roll Up
In conclusion, using representative samples is essential to ensure that study findings are valid and generalizable to the population of interest. Failing to use representative samples increases the risk of obtaining skewed conclusions that are inapplicable to the entire population. Thus, researchers need to take the appropriate measures to guarantee the validity of their samples.
Researchers should weigh the merits of each sampling technique against their specific research objectives and target populations to determine which is the best fit. Using representative samples, researchers may guarantee that their results are valid, reliable, and applicable to the general population.
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