Types of Sampling Methods & Techniques in Business Analytics

One of the major challenges faced by Data Scientists is dealing with enormous amounts of data. It would be unnecessary and even impossible to evaluate the entire population whenever one undertakes research on a specific group. Without looking at the complete dataset, one may conduct research using a variety of sampling strategies used in Data Analytics. Discussed below in detail is what sampling is and how it functions before moving on to different sample approaches in data analytics. 

What is Sampling?

Sampling is a common practice that is used to pick one group from a community to research in order to better understand the entire population. For example, one would like to know the proportion of iPhone users in a particular city. Calling everyone in the city and asking what kind of phone 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. 

However, this procedure is more complicated than it seems. The sample size ought to be perfect whenever one uses this procedure; it shouldn't be either too big or too tiny. Once the size of the sample has been determined, one 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: The sample is chosen using random selection methods.
• Non-probability Sampling: To choose the sample, non-random selection methods based on particular criteria are used.

Types of Sampling in Data Analytics

Discussed below are some of the basic sampling techniques that are used by Data Analysts.

Probability Sampling Techniques

One of the key categories of sampling techniques is probability sampling. By using probability sampling, every member of the population has a chance of being chosen. When an individual wishes to obtain results that are representative of the entire population, it is primarily employed in quantitative research.

Simple Random Sampling

In simple random sampling, the participants are chosen at random by the researcher. Many data analytics techniques, like random number generators and random number tables, are completely based on chance. 

Example: According to the size of your company, the researcher might assign each member of a database of companies a number between 1 and 1000 before using a random number generator to choose 100 individuals.

Systematic Sampling

Just like simple random sampling, each population in systematic sampling is assigned a number. However, the samples are selected at predetermined intervals rather than being generated at random.

Example: The researcher gives each person in the firm database a number, for instance. Instead of creating numbers at random, a random beginning point (let's say 5), is chosen. The researcher then chooses, say, every tenth individual on the list (5, 15, 25, and so on) until the sample is collected.

Stratified Sampling

According to certain factors (such as age, gender, and wealth), the population is divided into smaller subgroups known as strata in stratified sampling. You can choose a sample for each subgroup using random or systematic sampling after creating the subgroups. You can reach more exact conclusions with this strategy since it guarantees that each subgroup is fairly represented.

Example: The researcher wants to make sure that the sample accurately reflects gender if a company has 100 female employees and 500 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: As an illustration, a corporation with more than 100 offices in ten different locations around the world and nearly the same number of employees in comparable positions. Two or three offices are chosen at random by the researcher and used as the sample.

Non-Probability Sampling Techniques

One of the key categories of sampling techniques is non-probability sampling. 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

In this sampling technique, the researcher just picks the people who are closest to them. Although it is simple to collect data in this manner, it is impossible to determine if the sample is representative of the total population. The only need is for participants be available and willing.

Example: A researcher may ask incoming employees to conduct a survey or answer questions while they are standing outside a business.

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

In purposive sampling, the researcher chooses a sample that they believe is the best fit using their knowledge and discretion. It is frequently employed when the population is relatively tiny and the researcher is more interested in learning about a particular phenomenon than drawing general conclusions from statistics.

Example: For illustration, the researcher is interested in the experiences of impaired workers at a company. Therefore, the sample is specifically chosen from this demography.

Snowball Sampling

In a snowball sampling procedure, research participants solicit other study participants. When it's difficult to find the people a study needs, this method is used. Snowball sampling gets its name from the way it grows larger and larger as it travels, much like a snowball.

Example: The researcher is interested in learning more about what it's like to be homeless in a city. A random sample is not feasible because there is no comprehensive list of homeless persons. Contacting one homeless person who will then bring you in touch with other homeless persons in a specific area is the only way to obtain the sample.

Which Sampling Technique Should Be Used?

Discussed above are both the probability and non-probability sampling approaches in depth in our post on several types of sampling techniques in data analytics. Prior to beginning any form of research, it is crucial to select the appropriate sample methods. The sample one selects will have a significant impact on the success of the study. There are many more sampling procedures from which to choose in order to hone one's research.