Types of Sampling Methods for Market Research (With Examples)

By Indeed Editorial Team

Published June 2, 2022

The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.

Market research can help a company understand the consumers who buy its products and services. Preliminary research can create new sales opportunities, provide valuable insights, and uncover ways to allocate resources fairly and efficiently. Understanding the different sampling methods can help a company use data from a small group to draw conclusions about a larger target audience. In this article, we explore the different types of sampling and discuss the challenges to consider when creating a sample.

Related: What Is a Survey Method? (With Types and Applications)

What are the different types of sampling?

Companies often use different types of sampling to select groups to develop statistical assumptions and estimate specific characteristics applicable to an entire population. Using different sampling methods removes the necessity to involve the whole population to collect actionable insights. Sampling is also time- and cost-efficient and forms the basis of research designs. Sampling types primarily consist of probability and non-probability sampling:

• Probability sampling: This form of sample selection involves randomization instead of a deliberate choice.

• Non-probability sampling: Researchers use this technique to deliberately select individuals or items for the sample according to the research goals or knowledge.

Related: What Is a Research Methodology? (With Types and FAQs)

Types of probability sampling

The following includes the various types of probability sampling techniques:

Simple random sampling

Simple random sampling is a reliable method that researchers can use to randomly select participants to obtain certain information. Each individual has the same chance of becoming part of the sample group. The simple random sampling method is less susceptible to bias or influence when selecting the participants, providing researchers with an objective conclusion.

For example, if a company employs 800 individuals and wishes to perform a survey to determine employee satisfaction, the research team can select a sample group of 80 employees to participate in the survey, representing the total company population. Depending on the sample size, the company can select any of its employees for the survey. This means that every employee has an equal chance to participate.

Cluster sampling

Researchers use the cluster sampling method to divide the entire population into representative clusters. They identify the clusters in a sample according to various demographic parameters, such as gender, age, and location. This simplifies the effective interpretation of the results.

For example, if a government wishes to evaluate the number of immigrants living in the country, they can divide the population into clusters based on the different provinces. They can then survey each of these clusters. This can be more effective than other methods because researchers can organize the results into different provinces to obtain insightful data.

Systematic sampling

Systematic sampling involves selecting a sample from a population at regular intervals by choosing a repeatable sample starting point and size. A researcher can change the intervals when selecting the samples to help eliminate the possibility of accidentally clustering them together. This sampling method has a pre-defined range, making it less time-consuming.

For example, if a school has the names of 600 students in reverse alphabetical order, they can select a sample group of 30 students starting at a random name. They may decide to start with the 10th student, followed by every 30th student on the list. Each selected student forms part of the sample group.

Related: What Is a Decision Matrix? And How to Use One in 6 Steps

Stratified random sampling

Researchers can use the stratified random sampling method to divide the entire population into small representative groups that don't overlap. They can then separate the groups according to variables, such as occupation, age, and gender. After defining these smaller groups, they can select elements from each using simple random sampling.

For example, a researcher can use this technique to determine the characteristics of people with different annual incomes. They begin by dividing these individuals according to their yearly family income. They can then pass this information on to marketers, who determine which income groups to target for the best results during future marketing campaigns.

Types of non-probability sampling

The following includes the various types of non-probability sampling techniques:

Convenience sampling

Convenience sampling refers to the ease with which researchers can access the subjects, such as surveying pedestrians on a busy street or customers at a mall. This method is simple to perform and allows researchers to easily contact the subjects of the sample. A researcher has almost no control over selecting the sample elements and performs the sampling based on proximity. They typically use this technique when cost and time constraints limit result collection.

For example, a start-up company can perform convenience sampling at a mall by distributing leaflets regarding an upcoming promotion. They can accomplish this by standing at the mall entrance and randomly handing out the leaflets. The customers who accept a leaflet become part of the sample group.

Judgmental or purposive sampling

Researchers can perform judgmental or purposive sampling at their discretion. They only consider the purpose of the study, combined with a general understanding of the target population. For example, this can involve determining the thought processes of individuals planning to obtain master's degrees. A researcher can ask if anyone is considering acquiring their master's degree and ultimately only include those who respond in the affirmative in the sample group.

Snowball sampling

The snowball sampling method can be useful when the subjects are challenging to find. A researcher can identify an initial group of participants, which they can then expand to create a more extensive network of individuals who qualify as the target population. For example, when studying people with a rare illness, it may be challenging to locate everyone with the disease. If a researcher can find a few individuals to participate in the study, they can ask them to recruit more people they may know via support groups or other means.

Quota sampling

Quota sampling uses control features to help divide a target population into subgroups with similar characteristics. When researchers define the subgroups, they select elements from each group using sampling techniques like convenience or judgment sampling. Quota sampling is similar to stratified random sampling because both methods divide the population into subgroups according to specific variables.

For example, a study may require a researcher to include a quota of a specific number of males and females in a sample. It may also expect the sample to incorporate a certain income level, age range, or ethnic group. A researcher may introduce some bias during sample selection. For example, a volunteer bias may direct the sample towards individuals with free time and a willingness to participate in the research. Bias may also result from how the researcher divides the categories for the quota.

Related: What Is a Biased Sample? (Definition and List of Examples)

The challenges to consider when creating a sample

The following are common challenges that require consideration when creating a sample:

Population-specific errors

Sampling errors can occur when a researcher's understanding of the survey participants is inadequate. For example, when surveying the purchase of video streaming services, a sample group consisting of individuals 15–25 years of age may not be appropriate, as they may not have full-time jobs. Alternatively, if a researcher selects a sample of employed individuals responsible for these purchases, they may not utilize the streaming service regularly.

To address this issue, a researcher can carefully review the survey's objective and select the survey participants accordingly. When considering the example above, if a researcher is creating a survey about the nature of a video streaming service, it may be suitable to aim the questions at the 15–25 age group, who may be more regular viewers.

Selection errors

Selection errors occur when the individuals who respond to participate in a study don't meet the researcher's sample requirements. These types of errors may skew the results. To address this challenge, a researcher can create a small pre-survey before continuing with the primary study.

Non-response errors

A non-response error occurs when a researcher attempts to contact the selected individuals for the sample group but receives no response. This can happen because the individuals are unavailable at the time of contact or elect not to respond. A significant number of non-responses can distort the sample population by over- or under-representing specific demographics. To overcome this issue, a researcher can administer follow-up surveys to help elicit a response or ensure that the survey represents enough of the target population via alternate respondents.