Understanding different sampling methods

When you conduct research about a group of people, it’s rarely possible to collect data from every person in that group. Instead, you select a sample. The sample is the group of individuals who will actually participate in the research.

To draw valid conclusions from your results, you have to carefully decide how you will select a sample that is representative of the group as a whole. There are two types of sampling methods:

  • Probability sampling involves random selection, allowing you to make statistical inferences about the whole group.
  • Non-probability sampling involves non-random selection based on convenience or other criteria, allowing you to easily collect initial data.

You should clearly explain how you selected your sample in the methodology section of your paper or thesis.

Population vs sample

First, you need to understand the difference between a population and a sample, and identify the target population of your research.

  • The population is the entire group that you want to draw conclusions about.
  • The sample is the specific group of individuals that you will collect data from.

The population can be defined in terms of geographical location, age, income, and many other characteristics.

Population vs sampleIt can be very broad or quite narrow: maybe you want to make inferences about the whole adult population of your country; maybe your research focuses on customers of a certain company, patients with a specific health condition, or students in a single school.

It is important to carefully define your target population according to the purpose and practicalities of your project.

If the population is very large, demographically mixed, and geographically dispersed, it might be difficult to gain access to a representative sample.

Sampling frame

The sampling frame is the actual list of individuals that the sample will be drawn from. Ideally, it should include the entire target population (and nobody who is not part of that population).

Example

You are doing research on working conditions at Company X. Your population is all 1000 employees of the company. Your sampling frame is the company’s HR database which lists the names and contact details of every employee.

Sample size

The number of individuals in your sample depends on the size of the population, and on how precisely you want the results to represent the population as a whole.

You can use a sample size calculator to determine how big your sample should be. In general, the larger the sample size, the more accurately and confidently you can make inferences about the whole population.

Probability sampling methods

Probability sampling means that every member of the population has a chance of being selected. It is mainly used in quantitative research. If you want to produce results that are representative of the whole population, you need to use a probability sampling technique.

There are four main types of probability sample.

Probability sampling

1. Simple random sampling

In a simple random sample, every member of the population has an equal chance of being selected. Your sampling frame should include the whole population.

To conduct this type of sampling, you can use tools like random number generators or other techniques that are based entirely on chance.

Example

You want to select a simple random sample of 100 employees of Company X. You assign a number to every employee in the company database from 1 to 1000, and use a random number generator to select 100 numbers.

2. Systematic sampling

Systematic sampling is similar to simple random sampling, but it is usually slightly easier to conduct. Every member of the population is listed with a number, but instead of randomly generating numbers, individuals are chosen at regular intervals.

Example

All employees of the company are listed in alphabetical order. From the first 10 numbers, you randomly select a starting point: number 6. From number 6 onwards, every 10th person on the list is selected (6, 16, 26, 36, and so on), and you end up with a sample of 100 people.

If you use this technique, it is important to make sure that there is no hidden pattern in the list that might skew the sample. For example, if the HR database groups employees by team, and team members are listed in order of seniority, there is a risk that your interval might skip over people in junior roles, resulting in a sample that is skewed towards senior employees.

3. Stratified sampling

This sampling method is appropriate when the population has mixed characteristics, and you want to ensure that every characteristic is proportionally represented in the sample.

You divide the population into subgroups (called strata) based on the relevant characteristic (e.g. gender, age range, income bracket, job role).

From the overall proportions of the population, you calculate how many people should be sampled from each subgroup. Then you use random or systematic sampling to select a sample from each subgroup.

Example

The company has 800 female employees and 200 male employees. You want to ensure that the sample reflects the gender balance of the company, so you sort the population into two strata based on gender. Then you use random sampling on each group, selecting 80 women and 20 men, which gives you a representative sample of 100 people.

4. Cluster sampling

Cluster sampling also involves dividing the population into subgroups, but each subgroup should have similar characteristics to the whole sample. Instead of sampling individuals from each subgroup, you randomly select entire subgroups.

If it is practically possible, you might include every individual from each sampled cluster. If the clusters themselves are large, you can also sample individuals from within each cluster using one of the techniques above.

This method is good for dealing with large and dispersed populations, but there is more risk of error in the sample, as there could be substantial differences between clusters. It’s difficult to guarantee that the sampled clusters are really representative of the whole population.

Example

The company has offices in 10 cities across the country (all with roughly the same number of employees in similar roles). You don’t have the capacity to travel to every office to collect your data, so you use random sampling to select 3 offices – these are your clusters.

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Non-probability sampling methods

In a non-probability sample, individuals are selected based on non-random criteria, and not every individual has a chance of being included.

This type of sample is easier and cheaper to access, but it has a higher risk of sampling bias, and you can’t use it to make valid statistical inferences about the whole population.

Non-probability sampling techniques are often appropriate for exploratory and qualitative research. In these types of research, the aim is not to test a hypothesis about a broad population, but to develop an initial understanding of a small or under-researched population.

Non probability sampling

1. Convenience sampling

A convenience sample simply includes the individuals who happen to be most accessible to the researcher.

This is an easy and inexpensive way to gather initial data, but there is no way to tell if the sample is representative of the population, so it can’t produce generalizable results.

Example

You are researching opinions about student support services in your university, so after each of your classes, you ask your fellow students to complete a survey on the topic. This is a convenient way to gather data, but as you only surveyed students taking the same classes as you at the same level, the sample is not representative of all the students at your university.

2. Voluntary response sampling

Similar to a convenience sample, a voluntary response sample is mainly based on ease of access. Instead of the researcher choosing participants and directly contacting them, people volunteer themselves (e.g. by responding to a public online survey).

Voluntary response samples are always at least somewhat biased, as some people will inherently be more likely to volunteer than others.

Example

You send out the survey to all students at your university and a lot of students decide to complete it. This can certainly give you some insight into the topic, but the people who responded are more likely to be those who have strong opinions about the student support services, so you can’t be sure that their opinions are representative of all students.

3. Purposive sampling

This type of sampling involves the researcher using their judgement to select a sample that is most useful to the purposes of the research.

It is often used in qualitative research, where the researcher wants to gain detailed knowledge about a specific phenomenon rather than make statistical inferences. An effective purposive sample must have clear criteria and rationale for inclusion.

Example

You want to know more about the opinions and experiences of disabled students at your university, so you purposefully select a number of students with different support needs in order to gather a varied range of data on their experiences with student services.

4. Snowball sampling

If the population is hard to access, snowball sampling can be used to recruit participants via other participants. The number of people you have access to “snowballs” as you get in contact with more people.

Example

You are researching experiences of homelessness in your city. Since there is no list of all homeless people in the city, probability sampling isn’t possible. You meet one person who agrees to participate in the research, and she puts you in contact with other homeless people that she knows in the area.

Frequently asked questions about sampling

What is sampling?

A sample is a subset of individuals from a larger population. Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

Why are samples used in research?

Samples are used to make inferences about populations. Samples are easier to collect data from because they are practical, cost-effective, convenient and manageable.

What is probability sampling?

Probability sampling means that every member of the target population has a known chance of being included in the sample. Probability sampling methods include simple random sampling, systematic sampling, stratified sampling, and cluster sampling.

What is non-probability sampling?

In non-probability sampling, the sample is selected based on non-random criteria, and not every member of the population has a chance of being included.

Common non-probability sampling methods include convenience sampling, voluntary response sampling, purposive sampling, snowball sampling, and quota sampling.

What is sampling bias?

Sampling bias occurs when some members of a population are systematically more likely to be selected in a sample than others.

Is this article helpful?
Shona McCombes

Shona has a bachelor's and two master's degrees, so she's an expert at writing a great thesis. She has also worked as an editor and teacher, working with students at all different levels to improve their academic writing.

24 comments

Funmi Awotunde
June 27, 2020 at 6:09 PM

This is a great resource. I would recommend it to any novice in research. It is veri simple and easy to apply.

Well done Shona and her team

Funmi

Reply

Emiola
June 24, 2020 at 4:42 AM

Very helpful material ... tanks, well done, God bless!

Reply

Jess
June 18, 2020 at 7:11 PM

Hi,

I am intending on using a gatekeeper to access a specific group of people in which they will send out a survey I created, but I wasn't sure what type of sampling this fell under?

Reply

s madangombe
June 15, 2020 at 3:01 PM

very insightful

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Isidore Brown
June 13, 2020 at 10:42 PM

what if the the number of students I want to use for the sampling is the same as the population of the class, which type do I use?

Reply

Farai Chavunduka
June 9, 2020 at 5:04 PM

a brilliant piece of work

Reply

Evaristo mapunda
June 5, 2020 at 9:49 AM

Helpfull

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Pavan kumar Chivukula
May 30, 2020 at 10:57 PM

This article is just perfectly amazing.

Reply

ThansiyaPremchand
May 28, 2020 at 7:48 PM

Very useful...

Reply

Lamech
May 28, 2020 at 4:06 AM

Thanks. Your piece has been helpful to me.

Reply

Itumeleng Khoabane
May 20, 2020 at 6:44 PM

I would like to know if it is wrong to choose non-probability sampling techniques while my research is in quantitative form

Reply

Shona McCombes
Shona McCombes (Scribbr-team)
May 29, 2020 at 7:58 PM

Hi,

You can use non-probability sampling in quantitative research. However, this limits the generalizability of your results – it means you can't use your sample to make valid statistical inferences about a broader population. When writing up your research, make sure to explain how you selected your sample and discuss the potential limitations.

Reply

Tayseer
May 17, 2020 at 9:59 PM

Hi,i liked the topic it was very helpoful,but i have heard about the quota sampling can you explain it to me

Reply

Shona McCombes
Shona McCombes (Scribbr-team)
May 19, 2020 at 7:19 PM

Hi,

Quota sampling is similar to stratified sampling, but it is a non-probability method. The researcher splits up the population into subgroups (e.g. by age or gender), and then uses their judgement to select a specific number of participants (or a "quota") from each subgroup.

Hope that helps!

Reply

priya
May 15, 2020 at 8:52 PM

Very useful content. Thank you so much.

Reply

Rakhal
May 11, 2020 at 4:28 PM

Really useful...Thank You.

Reply

chisala
March 2, 2020 at 3:00 PM

how do i cite your work?

Reply

Shona McCombes
Shona McCombes (Scribbr-team)
March 3, 2020 at 12:40 PM

Hi,

You can cite any article on the Scribbr website by following the standard rules for the citation style you're using.

Reply

Julie Jacobe
February 8, 2020 at 4:30 PM

I would like to ask how to create a research design. Because I don't really know how to do it.

Reply

Shona McCombes
Shona McCombes (Scribbr-team)
February 10, 2020 at 5:53 PM

Hi Julie,

Creating a research design means making decisions about where, when, and how you'll collect and analyze data in order to answer a research question. You can learn more in our step-by-step guide to research design.

Hope that helps!

Reply

usman farooq
March 5, 2020 at 8:27 AM

Really Appreciated!!!

Reply

Joseph Sowah Laryea
May 17, 2020 at 9:17 AM

Very helpful, thank you very much!
Please what is the difference between research tools and research methods?

Reply

B.Ramija
July 1, 2020 at 6:23 PM

Nice and very helpful

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