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Introduction
Dealing with enormous amounts of data is one of the main challenges in data analytics. It would be unnecessary and even impossible to analyze the entire population whenever you undertake research on a specific group. So how can we solve this issue? Is it possible to select a portion of the data to serve as a representative sample of the complete dataset? It appears that there is. Without looking at the complete dataset, you can do research using a variety of sampling strategies used in data analytics. In this article, we’ll learn about what sampling is, how it functions, different types of sampling techniques, and how to decide on the type of sampling to use. Dive deep into the world of sampling and similar concepts by pursuing an Executive PG Program in Business Analytics from LIBA.
What is Sampling?
Sampling is a technique for choosing certain individuals or a small portion of the population in order to draw conclusions about the population as a whole and determine its characteristics. Researchers frequently utilize various sampling techniques in market research so they do not have to study the full community in order to gather useful information.
Consider the case where we would like to know the proportion of bike users in a particular city. Calling everyone in the city and asking what kind of mode of transport they use is one method to go about this. The alternative would be to ask the same question of a smaller subgroup of people and then use the results to estimate the size of the entire population.
But this procedure is more complicated than it seems. Your sample size ought to be perfect whenever you use this procedure; it shouldn’t be either too big or too tiny. Once the size of your sample has been determined, you must then gather a sample from the population using the appropriate sampling techniques. Every sampling method ultimately falls into one of two basic categories: Probability sampling and Non-probability sampling.
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Types of Sampling: Sampling Methods
There are two types of sampling methods used in market action research: probability sampling and non-probability sampling. Let’s examine these two sampling techniques in more detail.
- Probability sampling: In this sampling strategy, a researcher chooses a few criteria and randomly selects individuals from a population. With the use of this selection option, each member has an equal chance of taking part in the sample.
- Non-probability sampling: In non-probability sampling, participants are chosen at random for the study. This type of sampling is not a set or predetermined selection procedure. This makes it challenging for every component of the population to have an equal chance of being included in a sample.
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Examples
Probability Sampling Methods:
- Selecting a random sample of 50 employees from a company’s employee database.
- Dividing a population of students into strata based on their grade levels and then randomly selecting a sample from each stratum.
- Dividing a city into different neighborhoods, randomly selecting a few neighborhoods, and then surveying all households within the selected neighborhoods.
Non-probability Sampling Methods:
- Conducting on-the-spot interviews with people passing by in a shopping mall.
- Selecting specific individuals who possess the desired characteristics for a study, such as selecting expert witnesses for a legal case.
- Starting with a few initial participants and asking them to refer other potential participants for a study on a sensitive topic like illegal drug use.
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Population vs Sample
Every researcher needs to be able to understand the difference between the population and the sample. It is simple to figure out the distinction between a population and a sample. One essential rule of statistics that you must always keep in mind is that a sample is always a smaller group (subset) inside the population.
Every study in market research and statistics has a central question in mind. The outcome of this investigation is determined by observation and experiment with a population sample size. Insights that explain a phenomenon throughout the population are gained through this process.
Population
We are all aware of what the term “population” means. It is often employed to refer to the total number of people residing in a particular region of our nation or state. In research, a population is a complete set of components connected by a common parameter.
It’s not necessary for the ‘population’ in the study to be human. Any data parameter that contains a common characteristic qualifies.
Sample
A sample is a more limited representation of the population as a whole. It is an accurate representation of the study’s population. The population members who are asked to do the survey constitute the representative sample while surveys are being conducted. As a result, a sample is a subset or subgroup of the population. This sample can be examined to learn more about the traits or actions of the total population.
Numerous research techniques, including probability sampling and non-probability sampling, are used to create data samples. Depending on the type of study and the needed level of information, several sampling techniques are used.
Sampling frame
The objects in your population are listed in a sampling frame. It is an exhaustive list of all the people or things you want to study. A population and a sampling frame differ in that a population is more general and a frame is more particular. However, you can’t just use any list you find. To ensure that your sampling period meets your demands, caution must be used. Because of this, a sampling frame needs to:
- Include everyone who belongs to the target population.
- Include no one who is not a member of the target population.
- Contains precise data that can be utilized to get in touch with particular people.
Sample Size
The phrase “sample size” in market research refers to the total population of participants in the study. Based on factors such as age, gender, and geographic area, researchers select their sample. It may be general or precise.
For instance, you might be interested in learning what consumers between the ages of 30-45 think about your product. Also providing you with a wide population range is the option of merely requiring that your sample reside in India. The sample size is the total number of people included in a sample. Learn more about these methods and terms via Professional Certificate Program in Data Science and Business Analytics.
Probability Sampling Methods
One of the important categories of sampling techniques is probability sampling. Every member of the population has a chance to be chosen via probability sampling. , it is typically used when you wish to obtain results that are representative of the entire population
Simple Random Sampling
In simple random sampling, every observation in the population has an equal likelihood of being chosen, and any potential sample of a given size also has an equal chance of being chosen. One way to choose a simple random sample is to sequentially number each unit on the sampling frame and make the selections using random numbers generated by a random number generator.
Simple random sampling allows for the selection of the units with or without replacement. In contrast to sampling without replacement, which only allows for a single selection of a unit, replacement sampling permits numerous selections of the units. The most popular technique, absent replacement, is sampling.
Example: Depending on the size of your firm, the researcher gives each member of a database of companies a number between 1 and 1000 before using a random number generator to choose 100 individuals.
Systematic Random Sampling
The first object from the population is randomly chosen by the researcher as part of systematic random sampling. The researcher will then pick the nth object from the list for each choice. Systematic random sampling is a fairly simple process that can be carried out manually. Unless specific demographic traits are reproduced for every nth object, the results are representative of the population.
Example: Every individual in the company database is given a number by the researcher. Instead of creating numbers at random, a random beginning point (let’s say 10), is chosen. The researcher then chooses, say, every tenth individual on the list (20, 30, 40, and so on) until the sample is collected.
Stratified Random Sampling
In stratified random sampling, the total population is separated into several distinct, homogenous groups (strata), and final participants are randomly selected from the strata for study. To ensure that every group member has an equal chance of being chosen using basic probability, the members of each group should be different.
Example: The researcher wants to make sure that the sample accurately reflects gender in a company that has 200 female employees and 600 male employees. So, based on gender, the population is split into two sections.
Cluster Sampling
Cluster sampling divides the population into smaller groups, yet each group shares traits with the entire sample. You choose an entire subgroup at random as opposed to choosing a sample from every subgroup. When working with sizable and diverse populations, this approach is useful.
Example: A fast-food chain has more than a hundred locations in 10 cities throughout the globe, with nearly the same number of staff members working in comparable positions. Then, two or three offices are chosen at random by the researcher and used as the sample.
Uses of Probability Sampling
Probability sampling has several applications, including:
- Reduce Sample Bias: When using the probability sampling approach, the sample that is drawn from a population has little to no research bias. The sample choice primarily reflects the researcher’s comprehension and conclusions. Data gathering through probability sampling results in higher-quality data since the sample accurately represents the population.
- Population Diversity: It’s crucial to have enough representation in large, diverse populations to prevent statistics from being biased towards one demographic.
- Create an Accurate Sample: Probability sampling assists in the planning and production of an accurate sample by the researchers. This makes it easier to get precise data.
Non-probability Sampling Methods
Non-probability sampling limits the possibility that each person will be selected for the sample. Although easier and less expensive, this sampling technique carries a significant risk of bias. It is frequently employed in qualitative and exploratory research with the goal of gaining a basic understanding of the community.
Convenience Sampling
The simplest sampling technique is convenience sampling, where participants are chosen based on their availability and desire to take part in the survey. The sample could not be representative of the population as a whole, hence the results are subject to severe bias.
Example: The surveys that are done on YouTube or Facebook. People who are interested in participating in the survey or poll will show up, but the outcomes may not be reliable because they are highly biased.
Voluntary Response Sampling
Similar to convenience sampling, voluntary response sampling depends only on participants’ willingness to participate. However, people volunteer themselves rather than being selected by the researcher.
Example: The researcher offers the option for participation in a survey that is sent to every employee of a company.
Purposive Sampling Technique
In the Purposive sampling technique, a researcher uses discretion in selecting individuals from the population to take part in the study. Researchers frequently think they can use good judgment to gather a representative sample while also saving time and money
There is a likelihood that the results will be extremely accurate with a small margin of error because the researcher’s knowledge is required to generate a sample in this sampling technique.
Ex: A television network wishes to investigate one of its programmes. The target audience is known to the researcher, who can then select participants from the target audience for the study.
Snowball Sampling Technique
This sampling strategy involves primary data sources suggesting other potential primary data sources that could be used in the investigation. The snowball sampling strategy depends on recommendations from the initial participants to generate more subjects. As a result, sample group members are selected using this sampling technique via chain referral.
In the social sciences, this sampling technique is widely used for studying hard-to-reach groups. The sample expands like a snowball when more candidates who are known to the existing candidates are nominated.
Example: Finding people with uncommon disorders who are willing to participate in research could be challenging for a pharmaceutical business. By asking them to refer participants from their contacts, the pharma manufacturer can recruit a small number of people to take part in the clinical study.
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Uses of Non-probability Sampling
Non-probability sampling has several applications, including:
- Creating hypothesis: When there is little to no prior knowledge available, researchers employ the non-probability sampling method to make an assumption. This technique aids in the quick return of data and creates a foundation for additional study.
- Exploratory research: When performing exploratory research, pilot studies, or qualitative research, researchers frequently employ this sample strategy.
- Budget and time restrictions: The non-probability technique is used when these factors are present and preliminary data must be gathered. It is simpler to choose respondents at random and have them complete the survey or questionnaire because the survey design is flexible.
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How do you Decide on the Type of Sampling to Use?
To achieve your research objectives, it is crucial to select a sampling technique carefully. Your sampling’s effectiveness will depend on a number of variables. Here are several procedures used by professionals to select the most appropriate sampling strategy.
- Note the objectives of the study. Typically, it has to be a combination of cost, accuracy, and precision.
- Determine the efficient sampling strategies that could possibly meet the research objectives.
- Test each of these strategies to see if they assist you in achieving your objective.
- Pick the approach that suits the research the best.
Difference Between Probability Sampling and Non-probability Sampling Methods
|
Basis | Probability Sampling Technique |
Non-Probability Sampling Technique |
| Definition | In this technique, samples from a larger population are selected using a process based on the theory of probability. | In this technique, samples are chosen by the researcher based on their own judgment rather than by random selection. |
| Nature | The research conducted is definitive. | The research conducted is exploratory. |
| Sample | The sample is selected using a procedure, so the demographics of the population are clearly represented. | The depiction of the population’s demographics tends to be biased because the sampling methodology is random. |
| Time | Takes longer to complete since the selection criteria are defined by the research design before the market research study starts. | Since neither the sample nor its selection criteria are ambiguous, this kind of sampling technique is quick. |
| Hypothesis | Prior to the study’s start, there is a guiding hypothesis in probability sampling, and this approach seeks to substantiate it. | In non-probability sampling, the hypothesis is created after the research study has been completed. |
| Result | The results of this kind of sampling are conclusive since it is completely unbiased. | Because of the complete bias of this form of sampling, the findings are also biased, making the research questionable. |
Conclusion
The entire subject of probability and non-probability sampling procedures is covered in this article. Prior to beginning any form of research, it is crucial to select the appropriate sample methods. The sample you select will have a significant impact on the success of your study. There are many more sampling procedures from which to choose in order to hone your study; these are simply the top ones.
It’s very important to be certain of the appropriate sampling technique to utilize and at what times you want to succeed as a business analyst. If you want to learn more about business analytics, you may check out Job-ready Program in Business Analytics, which is offered by upGrad. It offers a variety of learning resources, including live faculty engagement, interactive discussion boards, quizzes, and assignments, career counseling, interview preparation, and much more. Get started with this course right away to launch a successful business analytics career.
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