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Sampling Methods | Types, Techniques & Examples

Sampling Methods | Types, Techniques & Examples

Published on September 19, 2019 by Shona McCombes . Revised on June 22, 2023.

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. This is called a sampling method . There are two primary types of sampling methods that you can use in your research:

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

You should clearly explain how you selected your sample in the methodology section of your paper or thesis, as well as how you approached minimizing research bias in your work.

Population vs. sample, probability sampling methods, non-probability sampling methods, other interesting articles, frequently asked questions about sampling.

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, or many other characteristics.

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. A lack of a representative sample affects the validity of your results, and can lead to several research biases , particularly sampling bias .

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).

Sample size

The number of individuals you should include in your sample depends on various factors, including the size and variability of the population and your research design. There are different sample size calculators and formulas depending on what you want to achieve with statistical analysis .

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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, probability sampling techniques are the most valid choice.

There are four main types of probability sample.

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.

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.

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

Stratified sampling involves dividing the population into subpopulations that may differ in important ways. It allows you draw more precise conclusions by ensuring that every subgroup is properly represented in the sample.

To use this sampling method, you divide the population into subgroups (called strata) based on the relevant characteristic (e.g., gender identity, age range, income bracket, job role).

Based on 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.

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 is called multistage sampling .

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.

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 . That means the inferences you can make about the population are weaker than with probability samples, and your conclusions may be more limited. If you use a non-probability sample, you should still aim to make it as representative of the population as possible.

Non-probability sampling techniques are often used in 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.

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. Convenience samples are at risk for both sampling bias and selection bias .

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, leading to self-selection bias .

3. Purposive sampling

This type of sampling, also known as judgement sampling, involves the researcher using their expertise 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, or where the population is very small and specific. An effective purposive sample must have clear criteria and rationale for inclusion. Always make sure to describe your inclusion and exclusion criteria and beware of observer bias affecting your arguments.

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. The downside here is also representativeness, as you have no way of knowing how representative your sample is due to the reliance on participants recruiting others. This can lead to sampling bias .

5. Quota sampling

Quota sampling relies on the non-random selection of a predetermined number or proportion of units. This is called a quota.

You first divide the population into mutually exclusive subgroups (called strata) and then recruit sample units until you reach your quota. These units share specific characteristics, determined by you prior to forming your strata. The aim of quota sampling is to control what or who makes up your sample.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

Student’s t -distribution
Normal distribution
Null and Alternative Hypotheses
Chi square tests
Confidence interval
Quartiles & Quantiles
Cluster sampling
Stratified sampling
Data cleansing
Reproducibility vs Replicability
Peer review
Prospective cohort study

Research bias

Implicit bias
Cognitive bias
Placebo effect
Hawthorne effect
Hindsight bias
Affect heuristic
Social desirability bias

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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.

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

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 .

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 .

In multistage sampling , or multistage cluster sampling, you draw a sample from a population using smaller and smaller groups at each stage.

This method is often used to collect data from a large, geographically spread group of people in national surveys, for example. You take advantage of hierarchical groupings (e.g., from state to city to neighborhood) to create a sample that’s less expensive and time-consuming to collect data from.

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

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Sampling Methods: A guide for researchers

Affiliation.

1 Arizona School of Dentistry & Oral Health A.T. Still University, Mesa, AZ, USA [email protected].
PMID: 37553279

Sampling is a critical element of research design. Different methods can be used for sample selection to ensure that members of the study population reflect both the source and target populations, including probability and non-probability sampling. Power and sample size are used to determine the number of subjects needed to answer the research question. Characteristics of individuals included in the sample population should be clearly defined to determine eligibility for study participation and improve power. Sample selection methods differ based on study design. The purpose of this short report is to review common sampling considerations and related errors.

Keywords: research design; sample size; sampling.

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Types of Sampling in Research

Bhardwaj, Pooja

Department of Cardiology, AIIMS, New Delhi, India

Address for correspondence: Dr. Pooja Bhardwaj, AIIMS, New Delhi, India. E-mail: [email protected]

Received November 01, 2019

Received in revised form November 20, 2019

Accepted November 28, 2019

This is an open access journal, and articles are distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as appropriate credit is given and the new creations are licensed under the identical terms.

Sampling is one of the most important factors which determines the accuracy of a study. This article review the sampling techniques used in research including Probability sampling techniques, which include simple random sampling, systematic random sampling and stratified random sampling and Non-probability sampling, which include quota sampling, self-selection sampling, convenience sampling, snowball sampling and purposive sampling.

INTRODUCTION TO RESEARCH

Research in common language means to search for knowledge.

Research is made up of two words – Re + cerchier derived from old French recherchier meaning to search.

Definition of research

D. Slesinger and M. Stephenson in the Encyclopaedia of the Social Sciences define research as “the manipulation of things, concepts or symbols for the purpose of generalising to extend, correct or verify knowledge, whether that knowledge aids in construction of theory or in the practice of an art.”

According to Clifford Woody, research comprises defining and redefining problems; formulating hypothesis or suggested solutions; collecting, organizing, and evaluating the data; making deductions and reaching conclusions; and at last carefully testing the conclusions to determine whether they fit the formulating hypothesis.

Research can be taken as the contribution to the existing bundle of knowledge, making it more advanced.

The main objective of research is to know or to find out the answers to questions in a scientific way.

Some of the general objectives of research are as follows:

To know about a subject or to find out something new in that – exploratory or formulative research
To know about the subject in depth, for example, the characteristics, nature of a particular group, or individual-descriptive research
To correlate the association of some particulars with something else – diagnostic research.

There are different types of research; some of them are listed below:

Descriptive and analytical
Applied and fundamental
Quantitative and qualitative
Conceptual and empirical
Other types include clinical, historical, and conclusion oriented.

There are different steps which provide a useful procedural guideline regarding the research process, some of the steps are as follows:

Formulating the research problem
Extensive literature survey
Hypothesis developing
Preparing research design
Determining the sample size
Collecting the data
Execution of the project
Analysis of data
Hypothesis testing
Generalization and interpretation
Preparation of report or presentation of the results.

According to the above steps, we have to prepare the research design and determine the sample size to carry out a complete research. Hence, we will discuss in detail about the different types of sampling or the sample designs.

WHAT IS SAMPLING

Sampling is defined as a procedure to select a sample from individual or from a large group of population for certain kind of research purpose. There are different advantages and disadvantages of sampling. We would be thinking sometimes that – Why there is a need of sampling? the answer is as it is too expensive and too time consuming to survey a whole population in a research study, we use sampling [ Figure 1 ].[ 1 , 2 , 3 , 4 , 5 ]

Advantages and disadvantages of sampling

Saves time and money and gives faster results as the sample size is smaller than the whole population
Sampling gives more accurate results as it is performed by trained and experienced investigators
When there is large population, sampling is the best way
Sampling enables to estimate the sampling errors. Hence, it assists in getting information concerning to some characteristics of the population
Study of samples requires less space and equipment as they are small in size
When there is limited resources, sampling is best.

The main disadvantage of the sampling is chances of bias. But, seeing so many of advantages, sampling is the best way to proceed in a research.

Types of sampling

Before we discuss about the different kinds of sampling, let us discuss about what the word sample mean.

In research term, a sample is a group of people, objects, or items that are taken from a large population for measurement. So, to get the accurate results, sampling is done.[ 6 , 7 , 8 , 9 , 10 ]

For example, if we have to check all the chips in a factory made are good or not, it is very difficult to check each chip, so to check, we will be taking a random chip and check for its accurate taste, shape, and size.

Hence, sampling is an important tool in research, when the population size is large. Based on this, we have divided it into two types: (1) probability (2) and nonprobability [ Figure 2 ].

These two types of sampling are further divided into the following subtypes:

Probability sampling

In this type of sampling, there is a known probability of each member of the population of being selected in the sample. When population is highly homogenous, there are high chances of each member of being selected in a sample. For example, in a bag full of rice, if we want to pick some rice, there are high chances of each rice grain of being selected in a sample. Hence, the sample collected will be a representative of the whole rice bag.

For such a study, the population serves as relatively a homogenous group as every member of the population is the target respondent of the research [ Figure 3 ].

Simple random sampling

In this type of sampling, the members of the sample are selected randomly and purely by chance. Hence, the quality of the sample is not affected as every member has an equal chance of being selected in the sample.

This type of sampling is best for population which is highly homogenous.

There are two different ways in which this type of sampling is carried out:

Lottery method/envelope method

In this method, we assign unique numbers to each member or element of the population, say in a population of 100 members, we give number from 1 to 100 to the members on a paper and keep it in a box. Then, we will take out any chit, and the number on that chit is a random sample.

However, in this method, when the population size is larger, it is difficult to write the name of every number on the chits. Hence, another method is used, i.e., random number table (which will be discussed later).

Another example given is an envelope method, say we want to select dilated cardiomyopathy patients DCM patients for yoga in a research project. The details of the 100 patients will be in each envelope and any one will be selected randomly. Hence, here, the chances of all patients to be selected as a sample are equal.

Random number table method

There are different number of random tables available, for example, Fisher's and yates tables and Tippets random number.

Here also, first we assign numbers to the population. If we have population of 20 and we have to choose five samples from this, we have to choose five random numbers from the table. For example, we choose – 12, 19, 01, 08, and 15. Hence, members of these numbers will be selected as the sample.

Types of simple random sampling

In the above section, we have discussed about the methods of doing simple random sampling. In this section, we will be discussing about the types of simple random sampling.

There are two types of simple random sampling:

Simple random sampling with replacement (SRSWR)
Simple random sampling without replacement (SRSWOR).

Simple random sampling with replacement

Selecting “ n ” number of units out of “ N ” units one by one in such a way that at each stage of selection, the sample each unit has equal chance of being selected, i.e., 1/ N .

Simple random sampling without replacement

Selecting “ n ” number of units out of “ N ” one by one at any stage of selecting a sample in such a way that anyone of the left units have the probability of being selected as a sample, i.e., 1/ N .

For example, if we want to know the number of turtles in a pond of a village, so if we are catching turtles from water, measure them, and return them to water, there are high chances that we choose the same turtle, this is SRSWR. However, if we take out the turtle from the water and don't return it without taking the next, it becomes SRSWOR.

Stratified random sampling

In this type, the population is first divided into subgroups called strata on the basis of similarities and then from each group or strata, the members are selected randomly.

Here, the purpose is to address the issue of less homogeneity of the population and to make a true representative sample.

For example, in a school of 1000 students, if we want to know how many of them will choose medical as their career, asking each student is difficult. Hence, as inquiring the whole class is difficult, we will ask few grades and from them, we will choose samples.[ 6 , 7 , 8 , 9 , 10 ]

For example, consider the following number of students in the class:

Grade No. of students “n”

Grade–6 – 50
Grade–7 – 50
Grade–8 – 100
Grade–9 – 100
Grade–10 – 200
Grade–11 – 200
Grade–12 – 300

Now calculate the sample of each grade using the following formula:

Stratified sample: n 6 = 100/1000 × 50 = 5, n 7 = 100/1000 × 50 = 5…. and so on.

So, in this, from each grade, five samples will be selected, and these will be selected according to the simple random method.

This type of sample is also called random quota sampling.

There should be classification on the basis of age, socioeconomics, nationality, religion, and other such classifications.

Detailed steps to select stratified random sample:

First, we will target the audience
Then, we will recognize the stratification variables which should match with the research objective and then will figure out the number of strata to be used
After gathering the information of stratification variables, we will create a frame on this basis for all elements in target audience
The whole population is then divided into different strata which will be unique and different from each but should cover each and every element/member of population. But, each member should be in one strata only
Now, we will assign random, unique number to each element
Then divide the number of samples to be taken with the total number of population into number of people in that group
The number now what we got is the number of samples to be selected for that particular strata. Here, we will use the simple random technique.

Types of stratified sampling

There are two types – (a)-Proportionate stratified random sampling – in this type, the sample size is directly proportional to the entire population of strata, i.e., each strata sample has the same sampling fraction. (b) When the sample size is not proportional.

Examples – in a medical college of 1000 students doing postgraduation (PG), there are five different branches of doing PG and we want to study the reading pattern of all the students. Hence, it is highly difficult for us to go and ask every PG student. So, here, we will divide the class according to the subjects and then according to the formulation, we will count each number of samples to be taken from each stratum.

In another example – if in a study a researcher wants to study which sex, male or female, is predominantly affected by heart failure and what are the causes behind that. He/she will divide the given population into two groups – one male and then female. According to the stratified formula, the number of males and females to be selected from each strata will be counted and then the members in sample with simple random method will be selected.

From 1000 people, 700 males and 300 females, according to which if we want to choose 100 people, then 70 males should be selected and 30 females should be selected, and this selection will be random.

Importance of this sampling

The main advantage of this sampling is that it gives better accuracy in results as compared to other sampling methods
It is very easy to teach and easy to grasp by the trainees
Even smaller sample sizes can also give good results using strata
We can divide the large population into different subgroups/strata according to our need.

When to use stratified random sampling

When we want to focus on a particular strata from the given population data
When we want to establish relationship between two strata
When it is difficult to contact/access the sample population, this method is best as samples are easily involved in research with this method
As the elements of samples are chosen from some specific strata, the accuracy of statistical results is higher than that of simple random sampling.

Systematic sampling

Systematic sampling is an advanced form of simple random sampling, in which we need complete data about the population.

In this, a member is selected after a fixed interval. The member thus selected will be known as the K th element.

Steps to form/select the sample using systematic sampling:

First develop a well-defined structural population to start on sampling aspect
Figure out the ideal size of sample
After deciding the sample size, assign number to every member of sample
Then, the interval of the sample is decided.

For example, we want to select a total of ten patients from a group of forty, then the K th element will be selected by dividing 40/10 = 4, so every 4 th patient will be taken for sampling – 4, 8, 12, 16, 20, 24, 28, 32, 36, and 40.

Types of systematic sampling

Linear systematic sampling

A list is made in a sequential manner of the whole population list. Decide the sample size and find the sampling interval by formula: K = N / n , where K is the K th element, N is the whole population, and n = number of samples. Now, choose random number between 1 and K and then to the number what we got add K to that to get the next sample.

Circular systematic sampling

In this, first, we will determine sample interval and then select number nearest to N / n . For example, if N = 17 and n = 4, then k is taken as 4 not 5 and then start selecting randomly between 1 to N , skip K units each time when we select the next unit until we get n units. In this type, there will be N number of samples unlike K samples in linear systematic sampling method.

It is very easy to create, conduct, and analyze the sample
Risk factor is very minimal
As there is even distribution of members to form a sample, systematic sampling is beneficial when there are diverse members of population.

Cluster sampling

In cluster sampling, various segments of a population are treated as cluster, and members from each cluster are selected randomly.

Cluster sampling and stratified sampling are different from each other.

In stratified sampling, the researcher is dividing the population into subgroups on the basis of age, sex, profession, etc., but in cluster sampling, we are selecting randomly from already-existing or naturally occurring groups/cluster, for example, towns within a district and families within a society.

For example, in a city, if we want to know the list of individuals affected by HBsAg, here it is difficult to find, but if we search area wise, we may get better results. Here, the area acts as a cluster and the individuals will be treated as sampling unit.

In this method, first, we make clusters according to our need and then we select sample according to simple random sampling/systematic sampling.

Multistage sampling

As the name suggests, it contains many stages and hence called multistage sampling.

In this, each cluster of samples is further divided into smaller clusters and the members are selected from each smaller cluster randomly. It is a complex form of cluster sampling

Naturally, groups in a population selected as cluster

Each cluster is divided into smaller cluster

Then, from each smaller cluster, members are selected randomly.

Nonprobability sampling

Nonprobability sampling is a type of sampling where each member of the population does not have known probability of being selected in the sample. For example, to study the impact of child labor on children, the researcher will search and interview only the children who are subjected to child labor.

It is of the following types:

Purposive sampling

In this type of sampling, according to the purpose of the study, the members for a sample are selected. It is also called deliberate sampling. It is also called judgmental sampling.

For example, to study the impact of yoga on DCM patients, only the DCM patients can be the best respondents for this study; every member of heart disease is not the best respondent for this study. Hence, the researcher deliberately selects only the DCM patients as respondents for this study.

When to use/execute judgmental sampling:

When the number of people is less in the population and the researcher knows that the target population fulfill his/her demands, in that case, the judgmental sampling is the best sample
When there is a need to filter the samples chosen by other sample methods, this sampling method is best as it depends on the researcher's knowledge and experience.

Another example of this type of sampling is if a researcher wants to know how many patients of depression are doing particular yoga postures and meditation, he/she will select those patients who he/she thinks will give 100% feedback.

Advantage of judgmental sampling

As selection of the sampling is done by experienced researcher, there will be no hurdles and thus selecting the sample becomes convenient
As the samples selected will be good respondents for that particular study, almost we will get the real-time results, as members will have appropriate knowledge and they understand the subject well
A researcher can produce desired results as he/she can directly communicate with the target audience.

Convenience sampling

Selecting the members of a sample on the basis of their convenient accessibility is called convenience sampling. In this, only those members are selected who are easily accessible to the researcher.

In this sampling, the available data are used without any further additional requirements.

This is used in pilot testing more commonly.

The participants/samples are selected which are easier to recruit for the study.

Some of the examples for this type of sampling are:

Different challenges/games at the shopping malls on different festivals
In a study, a researcher wants to know how many people in a particular area know about dengue, so the researcher will ask questionnaire to the people present and who knows something about dengue will participate in it.

Even the researcher can use the different social networking sites by putting his/her questions on them and interested people will join.

Advantage of convenience sampling

Very easy to implement and inexpensive to create samples
Useful for pilot studies and for hypothesis generations
In a very short duration of time, we can collect data.

Disadvantages

Chances of high sampling error.

Snow-ball sampling

Also known as chain sampling or sequential sampling, it is used where one respondent identifies other respondents (from his/her friends or relatives or known-to). This kind of sampling is adopted in situ ations where it is difficult to identify the members in a sample.

For example, a researcher wants to study problems faced by the migrants in an area. So, he/she will start from one and that migrant will give him/her the information about the other migrant and so it makes a chain and in this way, sample goes on growing like a snowball and the researcher continues this method until the required sample size is achieved.

When to use snowball sampling:

Snowball sampling totally depends on referrals. In this, the population is unknown and rare, due to which it is highly difficult to find the samples/participants.

Just as snowball increases on adding more snow, samples increase in this technique until we collect enough data to analyze. Hence, it is named snowball sampling.

Types of snowball sampling

There are three types:

Linear snowball sampling: In this, the collection of samples starts from collecting data from one and then that individual tells about the other and so in this way, a chain is formed and it continues till we get enough number of individuals to analyze.
Exponential nondiscrimination snowball sampling: In this, one individual will be giving information about more than one individual and those individuals in turn will be giving information about the others and in this way, with more and more referrals, the chain is formed and we collect data.

For example, to collect data regarding Diabetic mellitus from an area, we find an individual who is suffering from Diabetic mellitus. So from him, there are high chances that we will get some information about other people he may know suffering from Diabetic mellitus.

Exponential discrimination snowball sampling: In this type of snowball sampling, one patient gives multiple referrals, but the recruitment will be done only for one patient on the basis of the nature and type of the research study.

In the following areas, snowball sampling can be applied:

Medical records: There are many rare diseases which are yet to be researched and there could be restricted number of individuals suffering from such rare disease. Some of the examples of such disease are mad cow disease, Alice in Wonderland, water allergy, laughing death, pica, and Moebius syndrome. Hence, with this kind of sampling, the people affected with such disease can be traced and research could be done
Social research: In this, we take as many participants as much possible
Cases of discord: In cases of disputes and act of terrorism, rights violation, we will choose people who are witness for that or people who are affected by that.

Advantage of snowball sampling

Can collect samples very quickly
It is cost-effective.

Disadvantages of snowball sampling

High chances of sampling bias and margin of error
If no one cooperates, it is difficult to find the samples.

Quota sampling

In this kind of sampling, members are selected on the basis of some specific characteristics chosen by the researcher. These specific characteristics serve as a quota for selection of the members of the sample.

In this type of sampling, we gather representative data from a group. It is similar to stratified random sampling which is a type of probability sampling. The only difference between both is that in stratified random sampling, the elements of sample are chosen randomly, but in quota sampling, it is not so.

The number of participants is taken in specific category in well-planned manner; for example, 100 males and 100 females.

It is of two types – controlled quota sampling in which there are limitations to the choice of the researcher. The other type is uncontrolled quota sampling in which there are no limitations, and samples are selected according to the convenience of the researcher.

Consecutive sampling

In this type of nonprobability sampling, the researcher will select the samples according to his/her ease/convenience. This is also similar to convenience sampling with little change.

In this, the researcher first picks up a group of people for research, does it for some time period, collects samples, gives results, and once the research completes, he/she will move on to the next group of people. Hence, in this way, a researcher will fine tune his/her research work with the help of this sampling, and he/she gets chance to work with multiple sampling.

In many of the researches, the techniques used, the data analyzed, and conclusion given by researcher will either come under null hypothesis or disapproving it and accepting the alternative hypothesis.

Null hypothesis is denoted by H0, and there is no significant difference in the variables, whereas alternative hypothesis is denoted by H1, which is opposite to null hypothesis where there is some relationship between the two variables.

However, consecutively, the 3 rd option is available, that is, here the researcher, will either come under null hypothesis or if he disapproves it, he accepts the alternative hypothesis.

For example, for advertising the hospital, we distribute leaflets telling about the hospital and its facilities, once the camp organized for checking blood sugar and blood pressure (BP) as free, people will come and do their checkups. Many of the people will just see the leaflet and will move, but some of them will come and check for GRBS and BP. In this case, some might be only checking and going, and there will be another group of people who will check and want to show results to doctor and consult them. Hence, this group of people will provide conclusive results for showing the reports to doctor.

In this, there are different options to sample size and sampling schedule
Sampling schedule depends on the nature of research, if we are not able to get conclusive results with one sample, then we will go to next
This is not time-consuming and also very little effort is required.

The samples obtained cannot be randomized, and we cannot represent the whole population by this.

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What are Sampling Methods? Techniques, Types, and Examples

Every type of research includes samples from which inferences are drawn. The sample could be biological specimens or a subset of a specific group or population selected for analysis. The goal is often to conclude the entire population based on the characteristics observed in the sample. Now, the question comes to mind: how does one collect the samples? Answer: Using sampling methods. Various sampling strategies are available to researchers to define and collect samples that will form the basis of their research study.

In a study focusing on individuals experiencing anxiety, gathering data from the entire population is practically impossible due to the widespread prevalence of anxiety. Consequently, a sample is carefully selected—a subset of individuals meant to represent (or not in some cases accurately) the demographics of those experiencing anxiety. The study’s outcomes hinge significantly on the chosen sample, emphasizing the critical importance of a thoughtful and precise selection process. The conclusions drawn about the broader population rely heavily on the selected sample’s characteristics and diversity.

Table of Contents

What is sampling?

Sampling involves the strategic selection of individuals or a subset from a population, aiming to derive statistical inferences and predict the characteristics of the entire population. It offers a pragmatic and practical approach to examining the features of the whole population, which would otherwise be difficult to achieve because studying the total population is expensive, time-consuming, and often impossible. Market researchers use various sampling methods to collect samples from a large population to acquire relevant insights. The best sampling strategy for research is determined by criteria such as the purpose of the study, available resources (time and money), and research hypothesis.

For example, if a pet food manufacturer wants to investigate the positive impact of a new cat food on feline growth, studying all the cats in the country is impractical. In such cases, employing an appropriate sampling technique from the extensive dataset allows the researcher to focus on a manageable subset. This enables the researcher to study the growth-promoting effects of the new pet food. This article will delve into the standard sampling methods and explore the situations in which each is most appropriately applied.

What are sampling methods or sampling techniques?

Sampling methods or sampling techniques in research are statistical methods for selecting a sample representative of the whole population to study the population’s characteristics. Sampling methods serve as invaluable tools for researchers, enabling the collection of meaningful data and facilitating analysis to identify distinctive features of the people. Different sampling strategies can be used based on the characteristics of the population, the study purpose, and the available resources. Now that we understand why sampling methods are essential in research, we review the various sample methods in the following sections.

Types of sampling methods

Before we go into the specifics of each sampling method, it’s vital to understand terms like sample, sample frame, and sample space. In probability theory, the sample space comprises all possible outcomes of a random experiment, while the sample frame is the list or source guiding sample selection in statistical research. The sample represents the group of individuals participating in the study, forming the basis for the research findings. Selecting the correct sample is critical to ensuring the validity and reliability of any research; the sample should be representative of the population.

There are two most common sampling methods:

Probability sampling: A sampling method in which each unit or element in the population has an equal chance of being selected in the final sample. This is called random sampling, emphasizing the random and non-zero probability nature of selecting samples. Such a sampling technique ensures a more representative and unbiased sample, enabling robust inferences about the entire population.
Non-probability sampling: Another sampling method is non-probability sampling, which involves collecting data conveniently through a non-random selection based on predefined criteria. This offers a straightforward way to gather data, although the resulting sample may or may not accurately represent the entire population.

Irrespective of the research method you opt for, it is essential to explicitly state the chosen sampling technique in the methodology section of your research article. Now, we will explore the different characteristics of both sampling methods, along with various subtypes falling under these categories.

What is probability sampling?

The probability sampling method is based on the probability theory, which means that the sample selection criteria involve some random selection. The probability sampling method provides an equal opportunity for all elements or units within the entire sample space to be chosen. While it can be labor-intensive and expensive, the advantage lies in its ability to offer a more accurate representation of the population, thereby enhancing confidence in the inferences drawn in the research.

Types of probability sampling

Various probability sampling methods exist, such as simple random sampling, systematic sampling, stratified sampling, and clustered sampling. Here, we provide detailed discussions and illustrative examples for each of these sampling methods:

Simple random sampling: In simple random sampling, each individual has an equal probability of being chosen, and each selection is independent of the others. Because the choice is entirely based on chance, this is also known as the method of chance selection. In the simple random sampling method, the sample frame comprises the entire population.

For example, A fitness sports brand is launching a new protein drink and aims to select 20 individuals from a 200-person fitness center to try it. Employing a simple random sampling approach, each of the 200 people is assigned a unique identifier. Of these, 20 individuals are then chosen by generating random numbers between 1 and 200, either manually or through a computer program. Matching these numbers with the individuals creates a randomly selected group of 20 people. This method minimizes sampling bias and ensures a representative subset of the entire population under study.

Systematic sampling: The systematic sampling approach involves selecting units or elements at regular intervals from an ordered list of the population. Because the starting point of this sampling method is chosen at random, it is more convenient than essential random sampling. For a better understanding, consider the following example.

For example, considering the previous model, individuals at the fitness facility are arranged alphabetically. The manufacturer then initiates the process by randomly selecting a starting point from the first ten positions, let’s say 8. Starting from the 8th position, every tenth person on the list is then chosen (e.g., 8, 18, 28, 38, and so forth) until a sample of 20 individuals is obtained.

Stratified sampling: Stratified sampling divides the population into subgroups (strata), and random samples are drawn from each stratum in proportion to its size in the population. Stratified sampling provides improved representation because each subgroup that differs in significant ways is included in the final sample.

For example, Expanding on the previous simple random sampling example, suppose the manufacturer aims for a more comprehensive representation of genders in a sample of 200 people, consisting of 90 males, 80 females, and 30 others. The manufacturer categorizes the population into three gender strata (Male, Female, and Others). Within each group, random sampling is employed to select nine males, eight females, and three individuals from the others category, resulting in a well-rounded and representative sample of 200 individuals.

Clustered sampling: In this sampling method, the population is divided into clusters, and then a random sample of clusters is included in the final sample. Clustered sampling, distinct from stratified sampling, involves subgroups (clusters) that exhibit characteristics similar to the whole sample. In the case of small clusters, all members can be included in the final sample, whereas for larger clusters, individuals within each cluster may be sampled using the sampling above methods. This approach is referred to as multistage sampling. This sampling method is well-suited for large and widely distributed populations; however, there is a potential risk of sample error because ensuring that the sampled clusters truly represent the entire population can be challenging.

For example, Researchers conducting a nationwide health study can select specific geographic clusters, like cities or regions, instead of trying to survey the entire population individually. Within each chosen cluster, they sample individuals, providing a representative subset without the logistical challenges of attempting a nationwide survey.

Use s of probability sampling

Probability sampling methods find widespread use across diverse research disciplines because of their ability to yield representative and unbiased samples. The advantages of employing probability sampling include the following:

Representativeness

Probability sampling assures that every element in the population has a non-zero chance of being included in the sample, ensuring representativeness of the entire population and decreasing research bias to minimal to non-existent levels. The researcher can acquire higher-quality data via probability sampling, increasing confidence in the conclusions.

Statistical inference

Statistical methods, like confidence intervals and hypothesis testing, depend on probability sampling to generalize findings from a sample to the broader population. Probability sampling methods ensure unbiased representation, allowing inferences about the population based on the characteristics of the sample.

Precision and reliability

The use of probability sampling improves the precision and reliability of study results. Because the probability of selecting any single element/individual is known, the chance variations that may occur in non-probability sampling methods are reduced, resulting in more dependable and precise estimations.

Generalizability

Probability sampling enables the researcher to generalize study findings to the entire population from which they were derived. The results produced through probability sampling methods are more likely to be applicable to the larger population, laying the foundation for making broad predictions or recommendations.

Minimization of Selection Bias

By ensuring that each member of the population has an equal chance of being selected in the sample, probability sampling lowers the possibility of selection bias. This reduces the impact of systematic errors that may occur in non-probability sampling methods, where data may be skewed toward a specific demographic due to inadequate representation of each segment of the population.

What is non-probability sampling?

Non-probability sampling methods involve selecting individuals based on non-random criteria, often relying on the researcher’s judgment or predefined criteria. While it is easier and more economical, it tends to introduce sampling bias, resulting in weaker inferences compared to probability sampling techniques in research.

Types of Non-probability Sampling

Non-probability sampling methods are further classified as convenience sampling, consecutive sampling, quota sampling, purposive or judgmental sampling, and snowball sampling. Let’s explore these types of sampling methods in detail.

Convenience sampling: In convenience sampling, individuals are recruited directly from the population based on the accessibility and proximity to the researcher. It is a simple, inexpensive, and practical method of sample selection, yet convenience sampling suffers from both sampling and selection bias due to a lack of appropriate population representation.

For example, imagine you’re a researcher investigating smartphone usage patterns in your city. The most convenient way to select participants is by approaching people in a shopping mall on a weekday afternoon. However, this convenience sampling method may not be an accurate representation of the city’s overall smartphone usage patterns as the sample is limited to individuals present at the mall during weekdays, excluding those who visit on other days or never visit the mall.

Consecutive sampling: Participants in consecutive sampling (or sequential sampling) are chosen based on their availability and desire to participate in the study as they become available. This strategy entails sequentially recruiting individuals who fulfill the researcher’s requirements.

For example, In researching the prevalence of stroke in a hospital, instead of randomly selecting patients from the entire population, the researcher can opt to include all eligible patients admitted over three months. Participants are then consecutively recruited upon admission during that timeframe, forming the study sample.

Quota sampling: The selection of individuals in quota sampling is based on non-random selection criteria in which only participants with certain traits or proportions that are representative of the population are included. Quota sampling involves setting predetermined quotas for specific subgroups based on key demographics or other relevant characteristics. This sampling method employs dividing the population into mutually exclusive subgroups and then selecting sample units until the set quota is reached.

For example, In a survey on a college campus to assess student interest in a new policy, the researcher should establish quotas aligned with the distribution of student majors, ensuring representation from various academic disciplines. If the campus has 20% biology majors, 30% engineering majors, 20% business majors, and 30% liberal arts majors, participants should be recruited to mirror these proportions.

Purposive or judgmental sampling: In purposive sampling, the researcher leverages expertise to select a sample relevant to the study’s specific questions. This sampling method is commonly applied in qualitative research, mainly when aiming to understand a particular phenomenon, and is suitable for smaller population sizes.

For example, imagine a researcher who wants to study public policy issues for a focus group. The researcher might purposely select participants with expertise in economics, law, and public administration to take advantage of their knowledge and ensure a depth of understanding.

Snowball sampling: This sampling method is used when accessing the population is challenging. It involves collecting the sample through a chain-referral process, where each recruited candidate aids in finding others. These candidates share common traits, representing the targeted population. This method is often used in qualitative research, particularly when studying phenomena related to stigmatized or hidden populations.

For example, In a study focusing on understanding the experiences and challenges of individuals in hidden or stigmatized communities (e.g., LGBTQ+ individuals in specific cultural contexts), the snowball sampling technique can be employed. The researcher initiates contact with one community member, who then assists in identifying additional candidates until the desired sample size is achieved.

Uses of non-probability sampling

Non-probability sampling approaches are employed in qualitative or exploratory research where the goal is to investigate underlying population traits rather than generalizability. Non-probability sampling methods are also helpful for the following purposes:

Generating a hypothesis

In the initial stages of exploratory research, non-probability methods such as purposive or convenience allow researchers to quickly gather information and generate hypothesis that helps build a future research plan.

Qualitative research

Qualitative research is usually focused on understanding the depth and complexity of human experiences, behaviors, and perspectives. Non-probability methods like purposive or snowball sampling are commonly used to select participants with specific traits that are relevant to the research question.

Convenience and pragmatism

Non-probability sampling methods are valuable when resource and time are limited or when preliminary data is required to test the pilot study. For example, conducting a survey at a local shopping mall to gather opinions on a consumer product due to the ease of access to potential participants.

Probability vs Non-probability Sampling Methods


Selection of participants	Random selection of participants from the population using randomization methods	Non-random selection of participants from the population based on convenience or criteria
Representativeness	Likely to yield a representative sample of the whole population allowing for generalizations	May not yield a representative sample of the whole population; poor generalizability
Precision and accuracy	Provides more precise and accurate estimates of population characteristics	May have less precision and accuracy due to non-random selection
Bias	Minimizes selection bias	May introduce selection bias if criteria are subjective and not well-defined
Statistical inference	Suited for statistical inference and hypothesis testing and for making generalization to the population	Less suited for statistical inference and hypothesis testing on the population
Application	Useful for quantitative research where generalizability is crucial	Commonly used in qualitative and exploratory research where in-depth insights are the goal

Frequently asked questions

What is multistage sampling ? Multistage sampling is a form of probability sampling approach that involves the progressive selection of samples in stages, going from larger clusters to a small number of participants, making it suited for large-scale research with enormous population lists.
What are the methods of probability sampling? Probability sampling methods are simple random sampling, stratified random sampling, systematic sampling, cluster sampling, and multistage sampling.
How to decide which type of sampling method to use? Choose a sampling method based on the goals, population, and resources. Probability for statistics and non-probability for efficiency or qualitative insights can be considered . Also, consider the population characteristics, size, and alignment with study objectives.
What are the methods of non-probability sampling? Non-probability sampling methods are convenience sampling, consecutive sampling, purposive sampling, snowball sampling, and quota sampling.
Why are sampling methods used in research? Sampling methods in research are employed to efficiently gather representative data from a subset of a larger population, enabling valid conclusions and generalizations while minimizing costs and time.

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What is Convenience Sampling: Definition, Method, and Examples

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What are sampling methods and how do you choose the best one?

Posted on 18th November 2020 by Mohamed Khalifa

This tutorial will introduce sampling methods and potential sampling errors to avoid when conducting medical research.

Introduction to sampling methods

Examples of different sampling methods, choosing the best sampling method.

It is important to understand why we sample the population; for example, studies are built to investigate the relationships between risk factors and disease. In other words, we want to find out if this is a true association, while still aiming for the minimum risk for errors such as: chance, bias or confounding .

However, it would not be feasible to experiment on the whole population, we would need to take a good sample and aim to reduce the risk of having errors by proper sampling technique.

What is a sampling frame?

A sampling frame is a record of the target population containing all participants of interest. In other words, it is a list from which we can extract a sample.

What makes a good sample?

A good sample should be a representative subset of the population we are interested in studying, therefore, with each participant having equal chance of being randomly selected into the study.

We could choose a sampling method based on whether we want to account for sampling bias; a random sampling method is often preferred over a non-random method for this reason. Random sampling examples include: simple, systematic, stratified, and cluster sampling. Non-random sampling methods are liable to bias, and common examples include: convenience, purposive, snowballing, and quota sampling. For the purposes of this blog we will be focusing on random sampling methods .

Example: We want to conduct an experimental trial in a small population such as: employees in a company, or students in a college. We include everyone in a list and use a random number generator to select the participants

Advantages: Generalisable results possible, random sampling, the sampling frame is the whole population, every participant has an equal probability of being selected

Disadvantages: Less precise than stratified method, less representative than the systematic method

Simple sampling method example in stick men.

Example: Every nth patient entering the out-patient clinic is selected and included in our sample

Advantages: More feasible than simple or stratified methods, sampling frame is not always required

Disadvantages: Generalisability may decrease if baseline characteristics repeat across every nth participant

Systematic sampling method example in stick men

Example: We have a big population (a city) and we want to ensure representativeness of all groups with a pre-determined characteristic such as: age groups, ethnic origin, and gender

Advantages: Inclusive of strata (subgroups), reliable and generalisable results

Disadvantages: Does not work well with multiple variables

Stratified sampling method example stick men

Example: 10 schools have the same number of students across the county. We can randomly select 3 out of 10 schools as our clusters

Advantages: Readily doable with most budgets, does not require a sampling frame

Disadvantages: Results may not be reliable nor generalisable

Cluster sampling method example with stick men

How can you identify sampling errors?

Non-random selection increases the probability of sampling (selection) bias if the sample does not represent the population we want to study. We could avoid this by random sampling and ensuring representativeness of our sample with regards to sample size.

An inadequate sample size decreases the confidence in our results as we may think there is no significant difference when actually there is. This type two error results from having a small sample size, or from participants dropping out of the sample.

In medical research of disease, if we select people with certain diseases while strictly excluding participants with other co-morbidities, we run the risk of diagnostic purity bias where important sub-groups of the population are not represented.

Furthermore, measurement bias may occur during re-collection of risk factors by participants (recall bias) or assessment of outcome where people who live longer are associated with treatment success, when in fact people who died were not included in the sample or data analysis (survivors bias).

By following the steps below we could choose the best sampling method for our study in an orderly fashion.

Research objectiveness

Firstly, a refined research question and goal would help us define our population of interest. If our calculated sample size is small then it would be easier to get a random sample. If, however, the sample size is large, then we should check if our budget and resources can handle a random sampling method.

Sampling frame availability

Secondly, we need to check for availability of a sampling frame (Simple), if not, could we make a list of our own (Stratified). If neither option is possible, we could still use other random sampling methods, for instance, systematic or cluster sampling.

Study design

Moreover, we could consider the prevalence of the topic (exposure or outcome) in the population, and what would be the suitable study design. In addition, checking if our target population is widely varied in its baseline characteristics. For example, a population with large ethnic subgroups could best be studied using a stratified sampling method.

Random sampling

Finally, the best sampling method is always the one that could best answer our research question while also allowing for others to make use of our results (generalisability of results). When we cannot afford a random sampling method, we can always choose from the non-random sampling methods.

To sum up, we now understand that choosing between random or non-random sampling methods is multifactorial. We might often be tempted to choose a convenience sample from the start, but that would not only decrease precision of our results, and would make us miss out on producing research that is more robust and reliable.

References (pdf)

Mohamed Khalifa

No Comments on What are sampling methods and how do you choose the best one?

Thank you for this overview. A concise approach for research.

really helps! am an ecology student preparing to write my lab report for sampling.

I learned a lot to the given presentation.. It’s very comprehensive… Thanks for sharing…

Very informative and useful for my study. Thank you

Oversimplified info on sampling methods. Probabilistic of the sampling and sampling of samples by chance does rest solely on the random methods. Factors such as the random visits or presentation of the potential participants at clinics or sites could be sufficiently random in nature and should be used for the sake of efficiency and feasibility. Nevertheless, this approach has to be taken only after careful thoughts. Representativeness of the study samples have to be checked at the end or during reporting by comparing it to the published larger studies or register of some kind in/from the local population.

Thank you so much Mr.mohamed very useful and informative article

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When undertaking any research study, researchers must choose their sample carefully to minimise bias. This paper highlights why practitioners need to pay attention to issues of sampling when appraising research, and discusses sampling characteristics we should look for in quantitative and qualitative studies. Because of space restrictions, this editorial focuses on the randomised controlled trial (RCT) as an example of quantitative research, and grounded theory as an example of qualitative research. Although these 2 designs are used as examples, the general principles as outlined can be applied to all quantitative and qualitative research designs.

What is sampling?

Research studies usually focus on a defined group of people, such as ventilated patients or the parents of chronically ill children. The group of people in a study is referred to as the sample . Because it is too expensive and impractical to include the total population in a research study, the ideal study sample represents the total population from which the sample was drawn (eg, all ventilated patients or all parents of chronically ill children). This point—that studying an entire population is, in most cases, unnecessary—is the key to the theory of sampling . Sampling means simply studying a proportion of the population rather than the whole. The results of a study that has assembled its sample appropriately can be more confidently applied to the population from which the sample came. Using the examples of samples provided at the start of the paragraph, we can see that Chlan sampled 54 patients from a population of patients who required mechanical ventilation, 1 (see Evidence-Based Nursing 1999 April, p49) whereas Burke et al sampled 50 children (and their parents) from a population of all children requiring admission to hospital for chronic health conditions. 2 (see Evidence-Based Nursing 1998 July, p79) In both studies the researchers wanted to …

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Sampling Methods & Strategies 101

Everything you need to know (including examples)

By: Derek Jansen (MBA) | Expert Reviewed By: Kerryn Warren (PhD) | January 2023

If you’re new to research, sooner or later you’re bound to wander into the intimidating world of sampling methods and strategies. If you find yourself on this page, chances are you’re feeling a little overwhelmed or confused. Fear not – in this post we’ll unpack sampling in straightforward language , along with loads of examples .

Overview: Sampling Methods & Strategies

What is sampling in a research context?
The two overarching approaches

Simple random sampling

Stratified random sampling, cluster sampling, systematic sampling, purposive sampling, convenience sampling, snowball sampling.

How to choose the right sampling method

What (exactly) is sampling?

At the simplest level, sampling (within a research context) is the process of selecting a subset of participants from a larger group . For example, if your research involved assessing US consumers’ perceptions about a particular brand of laundry detergent, you wouldn’t be able to collect data from every single person that uses laundry detergent (good luck with that!) – but you could potentially collect data from a smaller subset of this group.

In technical terms, the larger group is referred to as the population , and the subset (the group you’ll actually engage with in your research) is called the sample . Put another way, you can look at the population as a full cake and the sample as a single slice of that cake. In an ideal world, you’d want your sample to be perfectly representative of the population, as that would allow you to generalise your findings to the entire population. In other words, you’d want to cut a perfect cross-sectional slice of cake, such that the slice reflects every layer of the cake in perfect proportion.

Achieving a truly representative sample is, unfortunately, a little trickier than slicing a cake, as there are many practical challenges and obstacles to achieving this in a real-world setting. Thankfully though, you don’t always need to have a perfectly representative sample – it all depends on the specific research aims of each study – so don’t stress yourself out about that just yet!

With the concept of sampling broadly defined, let’s look at the different approaches to sampling to get a better understanding of what it all looks like in practice.

The two overarching sampling approaches

At the highest level, there are two approaches to sampling: probability sampling and non-probability sampling . Within each of these, there are a variety of sampling methods , which we’ll explore a little later.

Probability sampling involves selecting participants (or any unit of interest) on a statistically random basis , which is why it’s also called “random sampling”. In other words, the selection of each individual participant is based on a pre-determined process (not the discretion of the researcher). As a result, this approach achieves a random sample.

Probability-based sampling methods are most commonly used in quantitative research , especially when it’s important to achieve a representative sample that allows the researcher to generalise their findings.

Non-probability sampling , on the other hand, refers to sampling methods in which the selection of participants is not statistically random . In other words, the selection of individual participants is based on the discretion and judgment of the researcher, rather than on a pre-determined process.

Non-probability sampling methods are commonly used in qualitative research , where the richness and depth of the data are more important than the generalisability of the findings.

If that all sounds a little too conceptual and fluffy, don’t worry. Let’s take a look at some actual sampling methods to make it more tangible.

Need a helping hand?

Probability-based sampling methods

First, we’ll look at four common probability-based (random) sampling methods:

Importantly, this is not a comprehensive list of all the probability sampling methods – these are just four of the most common ones. So, if you’re interested in adopting a probability-based sampling approach, be sure to explore all the options.

Simple random sampling involves selecting participants in a completely random fashion , where each participant has an equal chance of being selected. Basically, this sampling method is the equivalent of pulling names out of a hat , except that you can do it digitally. For example, if you had a list of 500 people, you could use a random number generator to draw a list of 50 numbers (each number, reflecting a participant) and then use that dataset as your sample.

Thanks to its simplicity, simple random sampling is easy to implement , and as a consequence, is typically quite cheap and efficient . Given that the selection process is completely random, the results can be generalised fairly reliably. However, this also means it can hide the impact of large subgroups within the data, which can result in minority subgroups having little representation in the results – if any at all. To address this, one needs to take a slightly different approach, which we’ll look at next.

Stratified random sampling is similar to simple random sampling, but it kicks things up a notch. As the name suggests, stratified sampling involves selecting participants randomly , but from within certain pre-defined subgroups (i.e., strata) that share a common trait . For example, you might divide the population into strata based on gender, ethnicity, age range or level of education, and then select randomly from each group.

The benefit of this sampling method is that it gives you more control over the impact of large subgroups (strata) within the population. For example, if a population comprises 80% males and 20% females, you may want to “balance” this skew out by selecting a random sample from an equal number of males and females. This would, of course, reduce the representativeness of the sample, but it would allow you to identify differences between subgroups. So, depending on your research aims, the stratified approach could work well.

Next on the list is cluster sampling. As the name suggests, this sampling method involves sampling from naturally occurring, mutually exclusive clusters within a population – for example, area codes within a city or cities within a country. Once the clusters are defined, a set of clusters are randomly selected and then a set of participants are randomly selected from each cluster.

Now, you’re probably wondering, “how is cluster sampling different from stratified random sampling?”. Well, let’s look at the previous example where each cluster reflects an area code in a given city.

With cluster sampling, you would collect data from clusters of participants in a handful of area codes (let’s say 5 neighbourhoods). Conversely, with stratified random sampling, you would need to collect data from all over the city (i.e., many more neighbourhoods). You’d still achieve the same sample size either way (let’s say 200 people, for example), but with stratified sampling, you’d need to do a lot more running around, as participants would be scattered across a vast geographic area. As a result, cluster sampling is often the more practical and economical option.

If that all sounds a little mind-bending, you can use the following general rule of thumb. If a population is relatively homogeneous , cluster sampling will often be adequate. Conversely, if a population is quite heterogeneous (i.e., diverse), stratified sampling will generally be more appropriate.

The last probability sampling method we’ll look at is systematic sampling. This method simply involves selecting participants at a set interval , starting from a random point .

For example, if you have a list of students that reflects the population of a university, you could systematically sample that population by selecting participants at an interval of 8 . In other words, you would randomly select a starting point – let’s say student number 40 – followed by student 48, 56, 64, etc.

What’s important with systematic sampling is that the population list you select from needs to be randomly ordered . If there are underlying patterns in the list (for example, if the list is ordered by gender, IQ, age, etc.), this will result in a non-random sample, which would defeat the purpose of adopting this sampling method. Of course, you could safeguard against this by “shuffling” your population list using a random number generator or similar tool.

Systematic sampling simply involves selecting participants at a set interval (e.g., every 10th person), starting from a random point.

Non-probability-based sampling methods

Right, now that we’ve looked at a few probability-based sampling methods, let’s look at three non-probability methods :

Again, this is not an exhaustive list of all possible sampling methods, so be sure to explore further if you’re interested in adopting a non-probability sampling approach.

First up, we’ve got purposive sampling – also known as judgment , selective or subjective sampling. Again, the name provides some clues, as this method involves the researcher selecting participants using his or her own judgement , based on the purpose of the study (i.e., the research aims).

For example, suppose your research aims were to understand the perceptions of hyper-loyal customers of a particular retail store. In that case, you could use your judgement to engage with frequent shoppers, as well as rare or occasional shoppers, to understand what judgements drive the two behavioural extremes .

Purposive sampling is often used in studies where the aim is to gather information from a small population (especially rare or hard-to-find populations), as it allows the researcher to target specific individuals who have unique knowledge or experience . Naturally, this sampling method is quite prone to researcher bias and judgement error, and it’s unlikely to produce generalisable results, so it’s best suited to studies where the aim is to go deep rather than broad .

Purposive sampling involves the researcher selecting participants using their own judgement, based on the purpose of the study.

Next up, we have convenience sampling. As the name suggests, with this method, participants are selected based on their availability or accessibility . In other words, the sample is selected based on how convenient it is for the researcher to access it, as opposed to using a defined and objective process.

Naturally, convenience sampling provides a quick and easy way to gather data, as the sample is selected based on the individuals who are readily available or willing to participate. This makes it an attractive option if you’re particularly tight on resources and/or time. However, as you’d expect, this sampling method is unlikely to produce a representative sample and will of course be vulnerable to researcher bias , so it’s important to approach it with caution.

Last but not least, we have the snowball sampling method. This method relies on referrals from initial participants to recruit additional participants. In other words, the initial subjects form the first (small) snowball and each additional subject recruited through referral is added to the snowball, making it larger as it rolls along .

Snowball sampling is often used in research contexts where it’s difficult to identify and access a particular population. For example, people with a rare medical condition or members of an exclusive group. It can also be useful in cases where the research topic is sensitive or taboo and people are unlikely to open up unless they’re referred by someone they trust.

Simply put, snowball sampling is ideal for research that involves reaching hard-to-access populations . But, keep in mind that, once again, it’s a sampling method that’s highly prone to researcher bias and is unlikely to produce a representative sample. So, make sure that it aligns with your research aims and questions before adopting this method.

How to choose a sampling method

Now that we’ve looked at a few popular sampling methods (both probability and non-probability based), the obvious question is, “ how do I choose the right sampling method for my study?”. When selecting a sampling method for your research project, you’ll need to consider two important factors: your research aims and your resources .

As with all research design and methodology choices, your sampling approach needs to be guided by and aligned with your research aims, objectives and research questions – in other words, your golden thread. Specifically, you need to consider whether your research aims are primarily concerned with producing generalisable findings (in which case, you’ll likely opt for a probability-based sampling method) or with achieving rich , deep insights (in which case, a non-probability-based approach could be more practical). Typically, quantitative studies lean toward the former, while qualitative studies aim for the latter, so be sure to consider your broader methodology as well.

The second factor you need to consider is your resources and, more generally, the practical constraints at play. If, for example, you have easy, free access to a large sample at your workplace or university and a healthy budget to help you attract participants, that will open up multiple options in terms of sampling methods. Conversely, if you’re cash-strapped, short on time and don’t have unfettered access to your population of interest, you may be restricted to convenience or referral-based methods.

In short, be ready for trade-offs – you won’t always be able to utilise the “perfect” sampling method for your study, and that’s okay. Much like all the other methodological choices you’ll make as part of your study, you’ll often need to compromise and accept practical trade-offs when it comes to sampling. Don’t let this get you down though – as long as your sampling choice is well explained and justified, and the limitations of your approach are clearly articulated, you’ll be on the right track.

Let’s recap…

In this post, we’ve covered the basics of sampling within the context of a typical research project.

Sampling refers to the process of defining a subgroup (sample) from the larger group of interest (population).
The two overarching approaches to sampling are probability sampling (random) and non-probability sampling .
Common probability-based sampling methods include simple random sampling, stratified random sampling, cluster sampling and systematic sampling.
Common non-probability-based sampling methods include purposive sampling, convenience sampling and snowball sampling.
When choosing a sampling method, you need to consider your research aims , objectives and questions, as well as your resources and other practical constraints .

If you’d like to see an example of a sampling strategy in action, be sure to check out our research methodology chapter sample .

Last but not least, if you need hands-on help with your sampling (or any other aspect of your research), take a look at our 1-on-1 coaching service , where we guide you through each step of the research process, at your own pace.

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

Excellent and helpful. Best site to get a full understanding of Research methodology. I’m nolonger as “clueless “..😉

Excellent and helpful for junior researcher!

Grad Coach tutorials are excellent – I recommend them to everyone doing research. I will be working with a sample of imprisoned women and now have a much clearer idea concerning sampling. Thank you to all at Grad Coach for generously sharing your expertise with students.

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The Sample and Sampling Techniques

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Efficient Random Sampling from Very Large Databases

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Idan Cohen 13 ,
Aviv Yehezkel 13 &
Zohar Yakhini 13

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One of the major research questions in large databases is how to efficiently sample a random subset of records. This sample can then be used to estimate query results and optimize query execution plans and other tasks. In order to have quick access to the data, the common practice is to create an index, which is often implemented by using B+Trees. Existing state-of-the-art algorithms for random sampling over B+Trees result in a significant performance overhead. This paper proposes novel approaches for efficient random sampling over B+Trees in very large databases. We analyze the algorithms’ correctness and use extensive simulation study, which showcases their superior performance compared to previous works while not affecting the quality of the random sample.

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Cohen, I., Yehezkel, A., Yakhini, Z. (2024). Efficient Random Sampling from Very Large Databases. In: Strauss, C., Amagasa, T., Manco, G., Kotsis, G., Tjoa, A.M., Khalil, I. (eds) Database and Expert Systems Applications. DEXA 2024. Lecture Notes in Computer Science, vol 14910. Springer, Cham. https://doi.org/10.1007/978-3-031-68309-1_10

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Ann Indian Acad Neurol
v.14(4); Oct-Dec 2011

Design, data analysis and sampling techniques for clinical research

Karthik suresh.

Department of Pulmonary and Critical Care Medicine, Johns Hopkins University School of Medicine, Trivandrum, India

Sanjeev V. Thomas

1 Department of Neurology, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Trivandrum, India

Geetha Suresh

2 Department of Justice Administration, University of Louisville, Louiseville, USA

Statistical analysis is an essential technique that enables a medical research practitioner to draw meaningful inference from their data analysis. Improper application of study design and data analysis may render insufficient and improper results and conclusion. Converting a medical problem into a statistical hypothesis with appropriate methodological and logical design and then back-translating the statistical results into relevant medical knowledge is a real challenge. This article explains various sampling methods that can be appropriately used in medical research with different scenarios and challenges.

Problem Identification

Clinical research often starts from questions raised at the bedside in hospital wards. Is there an association between neurocysticercosis (NCC) and epilepsy? Are magnetic resonance imaging changes good predictors of multiple sclerosis? Is there a benefit in using steroids in pyogenic meningitis? Typically, these questions lead us to set up more refined research questions. For example, do persons with epilepsy have a higher probability of having serological (or computed tomography [CT] scan) markers for NCC? What proportion of persons with multiple lesions in the brain has Multiple Sclerosis (MS) Do children with pyogenic meningitis have a lesser risk of mortality if dexamethasone is used concomitantly with antibiotics?

Designing a clinical study involves narrowing a topic of interest into a single focused research question, with particular attention paid to the methods used to answer the research question from a cost, viability and overall effectiveness standpoint. In this paper, we focus attention on residents and younger faculty who are planning short-term research projects that could be completed in 2–3 years. Once we have a fairly well-defined research question, we need to consider the best strategy to address these questions. Further considerations in clinical research, such as the clinical setting, study design, selection criteria, data collection and analysis, are influenced by the disease characteristics, prevalence, time availability, expertise, research grants and several other factors. In the example of NCC, should we use serological markers or CT scan findings as evidence of NCC? Such a question raises further questions. How good are serologic markers compared with CT scans in terms of identifying NCC? Which test (CT or blood test) is easier, safer and acceptable for this study? Do we have the expertise to carry out these laboratory tests and make interpretations? Which procedure is going to be more expensive? It is very important that the researcher spend adequate time considering all these aspects of his study and engage in discussion with biostatisticians before actually starting the study.

The major objective of this article is to explain these initial steps. We do not intend to provide a tailor-made design. Our aim is to familiarize the reader with different sampling methods that can be appropriately used in medical research with different scenarios and challenges.

One of the first steps in clinical study is choosing an appropriate setting to conduct the study (i.e., hospital, population-based). Some diseases, such as migraine, may have a different profile when evaluated in the population than when evaluated in the hospital. On the other hand, acute diseases such as meningitis would have a similar profile in the hospital and in the community. The observations in a study may or may not be generalizable, depending on how closely the sample represents the population at large.

Consider the following studies. Both De Gans et al .[ 1 ] and Scarborough et al .[ 2 ] looked at the effect of adjunctive Dexamethasone in bacterial meningitis. Both studies are good examples of using the hospital setting. Because the studies involved acute conditions, they utilize the fact that sicker patients will seek hospital care to concentrate their ability to find patients with meningitis. By the same logic, it would be inappropriate to study less-acute conditions in such a fashion as it would bias the study toward sicker patients.

On the other hand, consider the study by Holroyd et al .[ 3 ] investigating therapies in the treatment of migraine. Here, the authors intentionally chose an outpatient setting (the patients were referred to the study clinic from a network of other physician clinics as well as local advertisements) so that their population would not include patients with more severe pathology (requiring hospital admission).

If the sample was restricted to a particular age group, sex, socioeconomic background or stage of the disease, the results would be applicable to that particular group only. Hence, it is important to decide how you select your sample. After choosing an appropriate setting, attention must be turned to the inclusion and exclusion criteria. This is often locale specific. If we compare the exclusion criteria for the two meningitis studies mentioned above, we see that in the study by de Gans,[ 1 ] patients with shunts, prior neurosurgery and active tuberculosis were specifically excluded; in the Scarbrough study, however, such considerations did not apply as the locale was considerably different (sub-saharan Africa vs. Europe).

Validity (Precision) and Reliability (Consistency)

Clinical research generally requires making use of an existing test or instrument. These instruments and investigations have usually been well validated in the past, although the populations in which such validations were conducted may be different. Many such questionnaires and patient self-rating scales (MMSE or QOLIE, for instance) were developed in another part of the world. Therefore, in order to use these tests in clinical studies locally, they require validation. Socio-demographic characteristics and language differences often influence such tests considerably. For example, consider a scale that uses the ability to drive a motor car as a Quality of Life measure. Does this measure have the same relevance in India as in the USA, where only a small minority of people drive their own vehicles? Hence, it is very important to ensure that the instruments that we use have good validity.

Validity is the degree to which the investigative goals are measured accurately. The degree to which the research truly measures what it intended to measure[ 4 ] determines the fundamentals of medical research. Peace, Parrillo and Hardy[ 5 ] explain that the validity of the entire research process must be critically analyzed to the greatest extent possible so that appropriate conclusions can be drawn, and recommendations for development of sound health policy and practice can be offered.

Another measurement issue is reliability. Reliability refers to the extent to which the research measure is a consistent and dependable indicator of medical investigation. In measurement, reliability is an estimate of the degree to which a scale measures a construct consistently when it is used under the same condition with the same or different subjects. Reliability (consistency) describes the extent to which a measuring technique consistently provides the same results if the measurement is repeated. The validity (accuracy) of a measuring instrument is high if it measures exactly what it is supposed to measure. Thus, the validity and reliability together determine the accuracy of the measurement, which is essential to make valid statistical inference from a medical research.

Consider the following scenario. Kasner et al .[ 6 ] established reliability and validity of a new National Institute of Health Stroke Scale (NIHSS) generation method. This paper provides a good example of how to test a new instrument (NIH stroke score generation via retrospective chart review) with regards to its reliability and validity. To test validity, the investigators had multiple physicians review the same set of charts and compared the variability within the scores calculated by these physicians. To test reliability, the investigators compared the new test (NIHSS calculated by chart review) to the old test (NIHSS calculated at the bedside at the time of diagnosis). They reported that, overall, 88% of the estimated scores deviated by less than five points from the actual scores at both admission and discharge.

A major purpose of doing research is to infer or generalize research objectives from a sample to a larger population. The process of inference is accomplished by using statistical methods based on probability theory. A sample is a subset of the population selected, which is an unbiased representative of the larger population. Studies that use samples are less-expensive, and study of the entire population is sometimes impossible. Thus, the goal of sampling is to ensure that the sample group is a true representative of the population without errors. The term error includes sampling and nonsampling errors. Sampling errors that are induced by sampling design include selection bias and random sampling error. Nonsampling errors are induced by data collection and processing problems, and include issues related to measurement, processing and data collection errors.

Methods of sampling

To ensure reliable and valid inferences from a sample, probability sampling technique is used to obtain unbiased results. The four most commonly used probability sampling methods in medicine are simple random sampling, systematic sampling, stratified sampling and cluster sampling.

In simple random sampling, every subject has an equal chance of being selected for the study. The most recommended way to select a simple random sample is to use a table of random numbers or a computer-generated list of random numbers. Consider the study by Kamal et al .[ 7 ] that aimed to assess the burden of stroke and transient ischemic attack in Pakistan. In this study, the investigators used a household list from census data and picked a random set of households from this list. They subsequently interviewed the members of the randomly chosen households and used this data to estimate cerebrovascular disease prevalence in a particular region of Pakistan. Prevalence studies such as this are often conducted by using random sampling to generate a sampling frame from preexisting lists (such as census lists, hospital discharge lists, etc.).

A systematic random sample is one in which every k th item is selected. k is determined by dividing the number of items in the sampling frame by sample size.

A stratified random sample is one in which the population is first divided into relevant strata or subgroups and then, using the simple random sample method, a sample is drawn from each strata. Deng et al .[ 8 ] studied IV tissue Plasminogen Activator (tPA) usage in acute stroke among hospitals in Michigan. In order to enroll patients across a wide array of hospitals, they employ a stratified random sampling in order to construct the list of hospitals. They stratified hospitals by number of stroke discharges, and then randomly picked an equal number of hospitals within each stratum. Stratified random sampling such as this can be used to ensure that sampling adequately reflects the nature of current practice (such as practice and management trends across the range of hospital patient volumes, for instance).

A cluster sample results from a two-stage process in which the population is divided into clusters, and a subset of the clusters is randomly selected. Clusters are commonly based on geographic areas or districts and, therefore, this approach is used more often in epidemiologic research than in clinical studies.[ 9 ]

Random samples and randomization

Random samples and randomization (aka, random assignment) are two different concepts. Although both involve the use of the probability sampling method, random sampling determines who will be included in the sample. Randomization, or random assignment, determines who will be in the treatment or control group. Random sampling is related to sampling and external validity (generalizability), whereas random assignment is related to design and internal validity.

In experimental studies such as randomized controlled trials, subjects are first selected for inclusion in the study on the basis of appropriate criteria; they are then assigned to different treatment modalities using random assignment. Randomized controlled trials that are considered to be the most efficient method of controlling validity issues by taking into account all the potential confounding variables (such as other outside factors that could influence the variables under study) are also considered most reliable and impartial method of determining the impact of the experiment. Any differences in the outcome of the study are more likely to be the result of difference in the treatments under consideration than due to differences because of groups.

Scarborough et al .,[ 2 ] in a trial published in the New England Journal of Medicine , looked at corticosteroid therapy for bacterial meningitis in sub-saharan Africa to see whether the benefits seen with early corticosteroid administration in bacterial meningitis in the developed world also apply to the developing world. Interestingly, they found that adjuvant Dexamethasone therapy did not improve outcomes in meningitis cases in sub-saharan Africa. In this study, they performed random assignment of therapy (Dexamethasone vs. placebo). It is useful to note that the process of random assignment usually involves multiple sub-steps, each designed to eliminate confounders. For instance, in the above-mentioned study, both steroids and placebo were packaged similarly, in opaque envelopes, and given to patients (who consented to enroll) in a randomized fashion. These measures ensure the double-blind nature of the trial. Care is taken to make sure that the administrators of the therapy in question are blinded to the type of therapy (steroid vs. placebo) that is being given.

Sample size

The most important question that a researcher should ask when planning a study is “How large a sample do I need?” If the sample size is too small, even a well-conducted study may fail to answer its research question, may fail to detect important effects or associations, or may estimate those effects or associations too imprecisely. Similarly, if the sample size is too large, the study will be more difficult and costly, and may even lead to a loss in accuracy. Hence, optimum sample size is an essential component of any research. Careful consideration of sample size and power analysis during the planning and design stages of clinical research is crucial.

Statistical power is the probability that an empirical test will detect a relationship when a relationship in fact exists. In other words, statistical power explains the generalizability of the study results and its inferential power to explain population variability. Sample size is directly related to power; ceteris paribus, the bigger a sample, the higher the statistical power.[ 10 ] If the statistical power is low, this does not necessarily mean that an undetected relationships exist, but does indicate that the research is unlikely to find such links if they exist.[ 10 ] Flow chart relating research question, sampling and research design and data analysis is shown in Figure 1 .

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Overall framework of research design

The power of a study tells us how confidently we can exclude an association between two parameters. For example, regarding the prior research question of the association between NCC and epilepsy, a negative result might lead one to conclude that there is no association between NCC and epilepsy. However, the study might not have been sufficiently powered to exclude any possible association, or the sample size might have been too small to reveal an association.

The sample sizes seen in the two meningitis studies mentioned earlier are calculated numbers. Using estimates of prevalence of meningitis in their respective communities, along with variables such as size of expected effect (expected rate difference between treated and untreated groups) and level of significance, the investigators in both studies would have calculated their sample numbers ahead of enrolling patients. Sample sizes are calculated based on the magnitude of effect that the researcher would like to see in his treatment population (compared with placebo). It is important to note variables such as prevalence, expected confidence level and expected treatment effect need to be predetermined in order to calculate sample size. As an example, Scarborough et al .[ 2 ] state that “on the basis of a background mortality of 56% and an ability to detect a 20% or greater difference in mortality, the initial sample size of 660 patients was modified to 420 patients to detect a 30% difference after publication of the results of a European trial that showed a relative risk of death of 0.59 for corticosteroid treatment.” Determining existing prevalence and effect size can be difficult in areas of research where such numbers are not readily available in the literature. Ensuring adequate sample size has impacts for the final results of a trial, particularly negative trials. An improperly powered negative trial could fail to detect an existing association simply because not enough patients were enrolled. In other words, the result of the sample analysis would have failed to reject the null hypothesis (that there is no difference between the new treatment and the alternate treatment), when in fact it should have been rejected, which is referred to as type II error. This statistical error arises because of inadequate power to explain population variability. Careful consideration of sample size and power analysis is one of the prerequisites of medical research. Another prerequisite is appropriate and adequate research design, which will be addressed in the next issue.

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Sampling Methods | Types, Techniques & Examples

Table of contents

Sampling frame

Sample size

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1. Simple random sampling

2. Systematic sampling

3. Stratified sampling

4. Cluster sampling

1. Convenience sampling

2. Voluntary response sampling

3. Purposive sampling

4. Snowball sampling

5. Quota sampling

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Sampling Methods: A guide for researchers

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Secondary Logo

Types of Sampling in Research

INTRODUCTION TO RESEARCH

Definition of research

WHAT IS SAMPLING

Advantages and disadvantages of sampling

Types of sampling

Probability sampling

Simple random sampling

Lottery method/envelope method

Random number table method

Types of simple random sampling

Simple random sampling with replacement

Simple random sampling without replacement

Stratified random sampling

Types of stratified sampling

Importance of this sampling

When to use stratified random sampling

Systematic sampling

Types of systematic sampling

Linear systematic sampling

Circular systematic sampling

Cluster sampling

Multistage sampling

Nonprobability sampling

Purposive sampling

Advantage of judgmental sampling

Convenience sampling

Advantage of convenience sampling

Disadvantages

Snow-ball sampling

Types of snowball sampling

Advantage of snowball sampling

Disadvantages of snowball sampling

Quota sampling

Consecutive sampling

Financial support and sponsorship

What are Sampling Methods? Techniques, Types, and Examples

What is sampling?

What are sampling methods or sampling techniques?

Types of sampling methods

What is probability sampling?

Types of probability sampling

Use s of probability sampling

What is non-probability sampling?

Types of Non-probability Sampling

Uses of non-probability sampling

Probability vs Non-probability Sampling Methods

Frequently asked questions

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