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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.

Table of contents

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.

Population vs sample

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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research paper sampling methods

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

There are four main types of probability sample.

Probability sampling

1. Simple random sampling

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

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

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.

Non probability sampling

1. Convenience sampling

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

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

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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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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)

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Mohamed Khalifa

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

' src=

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

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

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Very informative and useful for my study. Thank you

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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.

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

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  • Knowledge Base
  • Methodology
  • Sampling Methods | Types, Techniques, & Examples

Sampling Methods | Types, Techniques, & Examples

Published on 3 May 2022 by Shona McCombes . Revised on 10 October 2022.

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

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

  • Probability sampling involves random selection, allowing you to make strong statistical inferences about the whole group. It minimises the risk of selection bias .
  • 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.

Table of contents

Population vs sample, probability sampling methods, non-probability sampling methods, 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, and many other characteristics.

Population vs sample

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

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

Sampling frame

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

You are doing research on working conditions at Company X. Your population is all 1,000 employees of the company. Your sampling frame is the company’s HR database, which lists the names and contact details of every employee.

Sample size

The number of individuals 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.

Probability sampling

1. Simple random sampling

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

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

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

2. Systematic sampling

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

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

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

3. Stratified sampling

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, 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.

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

4. Cluster sampling

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

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

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

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.

Non probability sampling

1. Convenience sampling

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

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

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

2. Voluntary response sampling

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

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

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

3. Purposive sampling

Purposive 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.

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

4. Snowball sampling

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

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

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.

Statistical sampling allows you to test a hypothesis about the characteristics of a population. There are various sampling methods you can use to ensure that your sample is representative of the population as a whole.

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 .

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 & 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.

research paper sampling methods

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.

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research paper sampling methods

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.

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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.

research paper sampling methods

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.

research paper sampling methods

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Sampling Methods – A Guide with Examples

Published by Alvin Nicolas at August 16th, 2021 , Revised On August 25, 2023

If you are performing research on a large community, organisation, or country, then it may not be possible to collect data individually from each participant. To deal with this issue, you can use a group of a specific number of participants, and this group is referred to as a sample .

The method you apply for selecting your participants is known as the  sampling method . It helps in concluding the entire population based on the  outcomes of the research .

Example: If you want to research China’s entire population, it isn’t easy to gather information from 1.38 billion people. You can use a sampling method by conducting your research on a specific number of participants and drawing a conclusion about the entire population based on your study’s outcomes.

Uses of Sampling Method

The sampling method is used to:

  • Gather data from a large group of population.
  • Counter check on data collection.
  • Speed up tabulation and publication of results.
  • Increase the efficiency of the research.
  • Conduct experimental research
  • Obtain data for researches on population census.

What is the Difference between Population and Sample? 

Before starting with the sampling methods, it is important to understand the difference between sample and population.

It is a group selected from the target population when you aim to study a large population. This group is considered as the representative of the overall targeted population.

Example: Sample of 20 female cricketers.

If you add the set of individuals with specific characteristics according to the research requirements, the resulting group is called the population. 

Example: The income of government teachers in India

Sampling method

Sampling Frame Vs. Sampling Size

Sampling frame.

A list of all the elements from a population is known as the sampling frame.

For instance, you are selecting a telephone directory of students or a list of social media users.

This information can be gathered by contacting any commercial organisation. Sometimes some errors are also possible in the sampling frame due to its discrepancy in selecting samples.

Sampling Size

It is considered a subset of the population as it is selected to make the inference to the original population of a study. The chances of accuracy are depended on the size of the population. The larger the size, the more accurate the study is.

When it comes to census, the sample size is the same or parallel with the population size. But to maintain the budget and to consider the time frame, only a representative class is selected.

Methods of Sampling

There are usually two methods of sampling which are used widely. These are considered the best methods:

  • Probability Method
  • Non-Probability Method

Probability Method 

This method of sampling is conducted by using the method of randomisation. In this method, each individual has an equal and independent opportunity to be selected.  It has further sub-categories.

  • Simple Random Methods
  • Stratified Methods
  • Systematic Method
  • Cluster Method
  • Multi-Stage

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Simple Random Method

The participants are selected randomly and assigned to the experimental group. It is known as probability sampling. If the selection is not random, it’s considered non-probability sampling. 

Example: You want to identify how much time people spend on social media. You need to randomly select the participants and assign a specific number of hours to spend on social media.

Example: You want to find out the benefits of a balanced diet. You need to select the participants randomly and assign a balanced diet.

Systematic Sampling Method

In this type of sampling, method participants are selected according to the fixed period interval and starting point. The fixed period interval can be calculated by dividing the sample size by the respective population size. 

Example: Framingham study , which includes selecting every second person from a list of two residents.

Stratified Method

Stratified sampling is a random selection of the participants by dividing them into strata and randomly selecting the participants from each level.

Example: You want to identify how much time people spend on social media. You need to divide the participants into groups based on their age and then assign a specific number of hours to spend on social media.

Example: You want to find out the benefits of a balanced diet. You need to divide participants into various groups based on their age, gender, and health conditions and assigned them to each group’s treatment group.

Matching Method

Even though if participants are selected randomly, they can be assigned to the various groups of comparison. Another procedure for selecting the participants is ‘matching.’ The participants are selected from the controlled group to match the experimental groups’ participants in all aspects based on the dependent variables.

Cluster Sampling

It is a kind of sampling where the population is converted into sub-groups called clusters. These sub-groups or clusters are then selected randomly as a sample. The selected group should have all the characteristics of other groups. 

Example: You want to check high school students’ communication skills, and there are more than 50 schools in the city. You can’t visit each school to gather information. In such situations, you can select any five schools, and these schools will be your clusters.

Non-probability Sampling

Non-probability sampling techniques are often appropriate for exploratory and  qualitative research . This type of sample is not to test a  hypothesis  about a broad population but to develop an initial understanding of a small or under-researched population.

This type of sampling is different from probability, as its criteria are unique. The samples are not selected randomly; rather, these samples are selected according to the researcher’s ability. This might result in a biased result, and participants may find it difficult to be a part of the sample. Still, this is a prevalent method. It has the following types:

  • Purposive type sampling
  • Referral sampling

Convenience Sampling

Quota sampling.

Reading material: Research Prospect has also published a very detailed guide about inductive and deductive reasoning for students.

Purposive Sampling

This type of sampling is based on the aims of the research. Therefore, only such elements of the population will be selected, which are according to the research’s purpose.

Example: You want to find out the opinion of people about jobs and businesses. You can select a few participants interested in doing 9-5 jobs and a few interested in doing business.

Referral Sampling

This type of sampling is used where the population is not defined or rare. In this technique, one participant is selected according to defined criteria. After that, the same selected participant is asked to refer to other samples fulfilling the study’s criteria. In this way, it goes enlarging its size with the help of the referral. 

Example: You can use it while conducting a study on the victims of physical harassment at workplaces. No matter how smoothly you approach them, not all women respond openly to your questions as they feel uncomfortable, or they get afraid of being humiliated. You can select the people from these victims’ circles (ex: their colleagues, friends, relatives) to get in touch with them and gather the required information for your research.

This type of sampling is applied according to availability. If the samples are not available easily, and the research is getting costly, this technique is applied to select the samples as per convenience. 

Example: You want to research the election campaigns. In this situation, you need to gather information from the available candidates (political leaders, media persons, voters) whenever and wherever you get any chance to meet them; otherwise, you will need to wait for the next election campaign.

This type of sampling is done when some standards are already adjusted. In this sampling, the representatives are selected from the population. This selected sample should resemble all the characteristics traits of the population. The size of the sample should reflect the. The participants are selected until sufficient information is gathered. 

Example: You want to identify and compare the high school’s academic performance, and you are allowed to select only 100 participants as per the standards of your study. You can select 25 students of the ninth standard, 25 students of the tenth standard, 25 students of the eleventh standard, and 25 students of the twelfth standard.

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Advantages of Sampling Method

Sampling has many advantages, such as:

  • It saves a lot of time, as contacting the entire population would be difficult and time-consuming.
  • It’s cost-effective.
  • It has greater scope and adaptability.
  • It provides accurate results.
  • It can be managed easily.

Disadvantages of Sampling Method

  • It may cause a feeling of discrimination among the participants who are not selected for the study.
  • The researcher needs to be skilled, experienced, and qualified to ensure efficient sampling.
  • It requires a lot of time, and results may not be reliable.

Frequently Asked Questions

What is sampling and its types.

Sampling is the process of selecting a subset of individuals or items from a larger population to gather data. Types include:

  • Random Sampling: Each member has an equal chance.
  • Stratified Sampling: Divides population into groups for proportional representation.
  • Systematic Sampling: Every nth member is chosen.
  • Cluster Sampling: Population is divided into clusters; random clusters are selected.
  • Convenience Sampling: Convenient individuals are chosen.
  • Snowball Sampling: Existing subjects refer new ones.

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Sampling Methods In Reseach: Types, Techniques, & Examples

Saul Mcleod, PhD

Educator, Researcher

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, Ph.D., is a qualified psychology teacher with over 18 years experience of working in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

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Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

Sampling methods in psychology refer to strategies used to select a subset of individuals (a sample) from a larger population, to study and draw inferences about the entire population. Common methods include random sampling, stratified sampling, cluster sampling, and convenience sampling. Proper sampling ensures representative, generalizable, and valid research results.
  • Sampling : the process of selecting a representative group from the population under study.
  • Target population : the total group of individuals from which the sample might be drawn.
  • Sample: a subset of individuals selected from a larger population for study or investigation. Those included in the sample are termed “participants.”
  • Generalizability : the ability to apply research findings from a sample to the broader target population, contingent on the sample being representative of that population.

For instance, if the advert for volunteers is published in the New York Times, this limits how much the study’s findings can be generalized to the whole population, because NYT readers may not represent the entire population in certain respects (e.g., politically, socio-economically).

The Purpose of Sampling

We are interested in learning about large groups of people with something in common in psychological research. We call the group interested in studying our “target population.”

In some types of research, the target population might be as broad as all humans. Still, in other types of research, the target population might be a smaller group, such as teenagers, preschool children, or people who misuse drugs.

Sample Target Population

Studying every person in a target population is more or less impossible. Hence, psychologists select a sample or sub-group of the population that is likely to be representative of the target population we are interested in.

This is important because we want to generalize from the sample to the target population. The more representative the sample, the more confident the researcher can be that the results can be generalized to the target population.

One of the problems that can occur when selecting a sample from a target population is sampling bias. Sampling bias refers to situations where the sample does not reflect the characteristics of the target population.

Many psychology studies have a biased sample because they have used an opportunity sample that comprises university students as their participants (e.g., Asch ).

OK, so you’ve thought up this brilliant psychological study and designed it perfectly. But who will you try it out on, and how will you select your participants?

There are various sampling methods. The one chosen will depend on a number of factors (such as time, money, etc.).

Probability and Non-Probability Samples

Random Sampling

Random sampling is a type of probability sampling where everyone in the entire target population has an equal chance of being selected.

This is similar to the national lottery. If the “population” is everyone who bought a lottery ticket, then everyone has an equal chance of winning the lottery (assuming they all have one ticket each).

Random samples require a way of naming or numbering the target population and then using some raffle method to choose those to make up the sample. Random samples are the best method of selecting your sample from the population of interest.

  • The advantages are that your sample should represent the target population and eliminate sampling bias.
  • The disadvantage is that it is very difficult to achieve (i.e., time, effort, and money).

Stratified Sampling

During stratified sampling , the researcher identifies the different types of people that make up the target population and works out the proportions needed for the sample to be representative.

A list is made of each variable (e.g., IQ, gender, etc.) that might have an effect on the research. For example, if we are interested in the money spent on books by undergraduates, then the main subject studied may be an important variable.

For example, students studying English Literature may spend more money on books than engineering students, so if we use a large percentage of English students or engineering students, our results will not be accurate.

We have to determine the relative percentage of each group at a university, e.g., Engineering 10%, Social Sciences 15%, English 20%, Sciences 25%, Languages 10%, Law 5%, and Medicine 15%. The sample must then contain all these groups in the same proportion as in the target population (university students).

  • The disadvantage of stratified sampling is that gathering such a sample would be extremely time-consuming and difficult to do. This method is rarely used in Psychology.
  • However, the advantage is that the sample should be highly representative of the target population, and therefore we can generalize from the results obtained.

Opportunity Sampling

Opportunity sampling is a method in which participants are chosen based on their ease of availability and proximity to the researcher, rather than using random or systematic criteria. It’s a type of convenience sampling .

An opportunity sample is obtained by asking members of the population of interest if they would participate in your research. An example would be selecting a sample of students from those coming out of the library.

  • This is a quick and easy way of choosing participants (advantage)
  • It may not provide a representative sample and could be biased (disadvantage).

Systematic Sampling

Systematic sampling is a method where every nth individual is selected from a list or sequence to form a sample, ensuring even and regular intervals between chosen subjects.

Participants are systematically selected (i.e., orderly/logical) from the target population, like every nth participant on a list of names.

To take a systematic sample, you list all the population members and then decide upon a sample you would like. By dividing the number of people in the population by the number of people you want in your sample, you get a number we will call n.

If you take every nth name, you will get a systematic sample of the correct size. If, for example, you wanted to sample 150 children from a school of 1,500, you would take every 10th name.

  • The advantage of this method is that it should provide a representative sample.

Sample size

The sample size is a critical factor in determining the reliability and validity of a study’s findings. While increasing the sample size can enhance the generalizability of results, it’s also essential to balance practical considerations, such as resource constraints and diminishing returns from ever-larger samples.

Reliability and Validity

Reliability refers to the consistency and reproducibility of research findings across different occasions, researchers, or instruments. A small sample size may lead to inconsistent results due to increased susceptibility to random error or the influence of outliers. In contrast, a larger sample minimizes these errors, promoting more reliable results.

Validity pertains to the accuracy and truthfulness of research findings. For a study to be valid, it should accurately measure what it intends to do. A small, unrepresentative sample can compromise external validity, meaning the results don’t generalize well to the larger population. A larger sample captures more variability, ensuring that specific subgroups or anomalies don’t overly influence results.

Practical Considerations

Resource Constraints : Larger samples demand more time, money, and resources. Data collection becomes more extensive, data analysis more complex, and logistics more challenging.

Diminishing Returns : While increasing the sample size generally leads to improved accuracy and precision, there’s a point where adding more participants yields only marginal benefits. For instance, going from 50 to 500 participants might significantly boost a study’s robustness, but jumping from 10,000 to 10,500 might not offer a comparable advantage, especially considering the added costs.

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  • An Bras Dermatol
  • v.91(3); May-Jun 2016

Sampling: how to select participants in my research study? *

Jeovany martínez-mesa.

1 Faculdade Meridional (IMED) - Passo Fundo (RS), Brazil.

David Alejandro González-Chica

2 University of Adelaide - Adelaide, Australia.

Rodrigo Pereira Duquia

3 Universidade Federal de Ciências da Saúde de Porto Alegre (UFCSPA) - Porto Alegre (RS), Brazil.

Renan Rangel Bonamigo

João luiz bastos.

4 Universidade Federal de Santa Catarina (UFSC) - Florianópolis (RS), Brazil.

In this paper, the basic elements related to the selection of participants for a health research are discussed. Sample representativeness, sample frame, types of sampling, as well as the impact that non-respondents may have on results of a study are described. The whole discussion is supported by practical examples to facilitate the reader's understanding.

To introduce readers to issues related to sampling.

INTRODUCTION

The essential topics related to the selection of participants for a health research are: 1) whether to work with samples or include the whole reference population in the study (census); 2) the sample basis; 3) the sampling process and 4) the potential effects nonrespondents might have on study results. We will refer to each of these aspects with theoretical and practical examples for better understanding in the sections that follow.

TO SAMPLE OR NOT TO SAMPLE

In a previous paper, we discussed the necessary parameters on which to estimate the sample size. 1 We define sample as a finite part or subset of participants drawn from the target population. In turn, the target population corresponds to the entire set of subjects whose characteristics are of interest to the research team. Based on results obtained from a sample, researchers may draw their conclusions about the target population with a certain level of confidence, following a process called statistical inference. When the sample contains fewer individuals than the minimum necessary, but the representativeness is preserved, statistical inference may be compromised in terms of precision (prevalence studies) and/or statistical power to detect the associations of interest. 1 On the other hand, samples without representativeness may not be a reliable source to draw conclusions about the reference population (i.e., statistical inference is not deemed possible), even if the sample size reaches the required number of participants. Lack of representativeness can occur as a result of flawed selection procedures (sampling bias) or when the probability of refusal/non-participation in the study is related to the object of research (nonresponse bias). 1 , 2

Although most studies are performed using samples, whether or not they represent any target population, census-based estimates should be preferred whenever possible. 3 , 4 For instance, if all cases of melanoma are available on a national or regional database, and information on the potential risk factors are also available, it would be preferable to conduct a census instead of investigating a sample.

However, there are several theoretical and practical reasons that prevent us from carrying out census-based surveys, including:

  • Ethical issues: it is unethical to include a greater number of individuals than that effectively required;
  • Budgetary limitations: the high costs of a census survey often limits its use as a strategy to select participants for a study;
  • Logistics: censuses often impose great challenges in terms of required staff, equipment, etc. to conduct the study;
  • Time restrictions: the amount of time needed to plan and conduct a census-based survey may be excessive; and,
  • Unknown target population size: if the study objective is to investigate the presence of premalignant skin lesions in illicit drugs users, lack of information on all existing users makes it impossible to conduct a census-based study.

All these reasons explain why samples are more frequently used. However, researchers must be aware that sample results can be affected by the random error (or sampling error). 3 To exemplify this concept, we will consider a research study aiming to estimate the prevalence of premalignant skin lesions (outcome) among individuals >18 years residing in a specific city (target population). The city has a total population of 4,000 adults, but the investigator decided to collect data on a representative sample of 400 participants, detecting an 8% prevalence of premalignant skin lesions. A week later, the researcher selects another sample of 400 participants from the same target population to confirm the results, but this time observes a 12% prevalence of premalignant skin lesions. Based on these findings, is it possible to assume that the prevalence of lesions increased from the first to the second week? The answer is probably not. Each time we select a new sample, it is very likely to obtain a different result. These fluctuations are attributed to the "random error." They occur because individuals composing different samples are not the same, even though they were selected from the same target population. Therefore, the parameters of interest may vary randomly from one sample to another. Despite this fluctuation, if it were possible to obtain 100 different samples of the same population, approximately 95 of them would provide prevalence estimates very close to the real estimate in the target population - the value that we would observe if we investigated all the 4,000 adults residing in the city. Thus, during the sample size estimation the investigator must specify in advance the highest or maximum acceptable random error value in the study. Most population-based studies use a random error ranging from 2 to 5 percentage points. Nevertheless, the researcher should be aware that the smaller the random error considered in the study, the larger the required sample size. 1

SAMPLE FRAME

The sample frame is the group of individuals that can be selected from the target population given the sampling process used in the study. For example, to identify cases of cutaneous melanoma the researcher may consider to utilize as sample frame the national cancer registry system or the anatomopathological records of skin biopsies. Given that the sample may represent only a portion of the target population, the researcher needs to examine carefully whether the selected sample frame fits the study objectives or hypotheses, and especially if there are strategies to overcome the sample frame limitations (see Chart 1 for examples and possible limitations).

Examples of sample frames and potential limitations as regards representativeness

Sampling can be defined as the process through which individuals or sampling units are selected from the sample frame. The sampling strategy needs to be specified in advance, given that the sampling method may affect the sample size estimation. 1 , 5 Without a rigorous sampling plan the estimates derived from the study may be biased (selection bias). 3

TYPES OF SAMPLING

In figure 1 , we depict a summary of the main sampling types. There are two major sampling types: probabilistic and nonprobabilistic.

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Sampling types used in scientific studies

NONPROBABILISTIC SAMPLING

In the context of nonprobabilistic sampling, the likelihood of selecting some individuals from the target population is null. This type of sampling does not render a representative sample; therefore, the observed results are usually not generalizable to the target population. Still, unrepresentative samples may be useful for some specific research objectives, and may help answer particular research questions, as well as contribute to the generation of new hypotheses. 4 The different types of nonprobabilistic sampling are detailed below.

Convenience sampling : the participants are consecutively selected in order of apperance according to their convenient accessibility (also known as consecutive sampling). The sampling process comes to an end when the total amount of participants (sample saturation) and/or the time limit (time saturation) are reached. Randomized clinical trials are usually based on convenience sampling. After sampling, participants are usually randomly allocated to the intervention or control group (randomization). 3 Although randomization is a probabilistic process to obtain two comparable groups (treatment and control), the samples used in these studies are generally not representative of the target population.

Purposive sampling: this is used when a diverse sample is necessary or the opinion of experts in a particular field is the topic of interest. This technique was used in the study by Roubille et al, in which recommendations for the treatment of comorbidities in patients with rheumatoid arthritis, psoriasis, and psoriatic arthritis were made based on the opinion of a group of experts. 6

Quota sampling: according to this sampling technique, the population is first classified by characteristics such as gender, age, etc. Subsequently, sampling units are selected to complete each quota. For example, in the study by Larkin et al., the combination of vemurafenib and cobimetinib versus placebo was tested in patients with locally-advanced melanoma, stage IIIC or IV, with BRAF mutation. 7 The study recruited 495 patients from 135 health centers located in several countries. In this type of study, each center has a "quota" of patients.

"Snowball" sampling : in this case, the researcher selects an initial group of individuals. Then, these participants indicate other potential members with similar characteristics to take part in the study. This is frequently used in studies investigating special populations, for example, those including illicit drugs users, as was the case of the study by Gonçalves et al, which assessed 27 users of cocaine and crack in combination with marijuana. 8

PROBABILISTIC SAMPLING

In the context of probabilistic sampling, all units of the target population have a nonzero probability to take part in the study. If all participants are equally likely to be selected in the study, equiprobabilistic sampling is being used, and the odds of being selected by the research team may be expressed by the formula: P=1/N, where P equals the probability of taking part in the study and N corresponds to the size of the target population. The main types of probabilistic sampling are described below.

Simple random sampling: in this case, we have a full list of sample units or participants (sample basis), and we randomly select individuals using a table of random numbers. An example is the study by Pimenta et al, in which the authors obtained a listing from the Health Department of all elderly enrolled in the Family Health Strategy and, by simple random sampling, selected a sample of 449 participants. 9

Systematic random sampling: in this case, participants are selected from fixed intervals previously defined from a ranked list of participants. For example, in the study of Kelbore et al, children who were assisted at the Pediatric Dermatology Service were selected to evaluate factors associated with atopic dermatitis, selecting always the second child by consulting order. 10

Stratified sampling: in this type of sampling, the target population is first divided into separate strata. Then, samples are selected within each stratum, either through simple or systematic sampling. The total number of individuals to be selected in each stratum can be fixed or proportional to the size of each stratum. Each individual may be equally likely to be selected to participate in the study. However, the fixed method usually involves the use of sampling weights in the statistical analysis (inverse of the probability of selection or 1/P). An example is the study conducted in South Australia to investigate factors associated with vitamin D deficiency in preschool children. Using the national census as the sample frame, households were randomly selected in each stratum and all children in the age group of interest identified in the selected houses were investigated. 11

Cluster sampling: in this type of probabilistic sampling, groups such as health facilities, schools, etc., are sampled. In the above-mentioned study, the selection of households is an example of cluster sampling. 11

Complex or multi-stage sampling: This probabilistic sampling method combines different strategies in the selection of the sample units. An example is the study of Duquia et al. to assess the prevalence and factors associated with the use of sunscreen in adults. The sampling process included two stages. 12 Using the 2000 Brazilian demographic census as sampling frame, all 404 census tracts from Pelotas (Southern Brazil) were listed in ascending order of family income. A sample of 120 tracts were systematically selected (first sampling stage units). In the second stage, 12 households in each of these census tract (second sampling stage units) were systematically drawn. All adult residents in these households were included in the study (third sampling stage units). All these stages have to be considered in the statistical analysis to provide correct estimates.

NONRESPONDENTS

Frequently, sample sizes are increased by 10% to compensate for potential nonresponses (refusals/losses). 1 Let us imagine that in a study to assess the prevalence of premalignant skin lesions there is a higher percentage of nonrespondents among men (10%) than among women (1%). If the highest percentage of nonresponse occurs because these men are not at home during the scheduled visits, and these participants are more likely to be exposed to the sun, the number of skin lesions will be underestimated. For this reason, it is strongly recommended to collect and describe some basic characteristics of nonrespondents (sex, age, etc.) so they can be compared to the respondents to evaluate whether the results may have been affected by this systematic error.

Often, in study protocols, refusal to participate or sign the informed consent is considered an "exclusion criteria". However, this is not correct, as these individuals are eligible for the study and need to be reported as "nonrespondents".

SAMPLING METHOD ACCORDING TO THE TYPE OF STUDY

In general, clinical trials aim to obtain a homogeneous sample which is not necessarily representative of any target population. Clinical trials often recruit those participants who are most likely to benefit from the intervention. 3 Thus, the more strict criteria for inclusion and exclusion of subjects in clinical trials often make it difficult to locate participants: after verification of the eligibility criteria, just one out of ten possible candidates will enter the study. Therefore, clinical trials usually show limitations to generalize the results to the entire population of patients with the disease, but only to those with similar characteristics to the sample included in the study. These peculiarities in clinical trials justify the necessity of conducting a multicenter and/or global studiesto accelerate the recruitment rate and to reach, in a shorter time, the number of patients required for the study. 13

In turn, in observational studies to build a solid sampling plan is important because of the great heterogeneity usually observed in the target population. Therefore, this heterogeneity has to be also reflected in the sample. A cross-sectional population-based study aiming to assess disease estimates or identify risk factors often uses complex probabilistic sampling, because the sample representativeness is crucial. However, in a case-control study, we face the challenge of selecting two different samples for the same study. One sample is formed by the cases, which are identified based on the diagnosis of the disease of interest. The other consists of controls, which need to be representative of the population that originated the cases. Improper selection of control individuals may introduce selection bias in the results. Thus, the concern with representativeness in this type of study is established based on the relationship between cases and controls (comparability).

In cohort studies, individuals are recruited based on the exposure (exposed and unexposed subjects), and they are followed over time to evaluate the occurrence of the outcome of interest. At baseline, the sample can be selected from a representative sample (population-based cohort studies) or a non-representative sample. However, in the successive follow-ups of the cohort member, study participants must be a representative sample of those included in the baseline. 14 , 15 In this type of study, losses over time may cause follow-up bias.

Researchers need to decide during the planning stage of the study if they will work with the entire target population or a sample. Working with a sample involves different steps, including sample size estimation, identification of the sample frame, and selection of the sampling method to be adopted.

Financial Support: None.

* Study performed at Faculdade Meridional - Escola de Medicina (IMED) - Passo Fundo (RS), Brazil.

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Home » Sampling Methods – Types, Techniques and Examples

Sampling Methods – Types, Techniques and Examples

Table of Contents

Sampling Methods

Sampling refers to the process of selecting a subset of data from a larger population or dataset in order to analyze or make inferences about the whole population.

In other words, sampling involves taking a representative sample of data from a larger group or dataset in order to gain insights or draw conclusions about the entire group.

Sampling Methods

Sampling methods refer to the techniques used to select a subset of individuals or units from a larger population for the purpose of conducting statistical analysis or research.

Sampling is an essential part of the Research because it allows researchers to draw conclusions about a population without having to collect data from every member of that population, which can be time-consuming, expensive, or even impossible.

Types of Sampling Methods

Sampling can be broadly categorized into two main categories:

Probability Sampling

This type of sampling is based on the principles of random selection, and it involves selecting samples in a way that every member of the population has an equal chance of being included in the sample.. Probability sampling is commonly used in scientific research and statistical analysis, as it provides a representative sample that can be generalized to the larger population.

Type of Probability Sampling :

  • Simple Random Sampling: In this method, every member of the population has an equal chance of being selected for the sample. This can be done using a random number generator or by drawing names out of a hat, for example.
  • Systematic Sampling: In this method, the population is first divided into a list or sequence, and then every nth member is selected for the sample. For example, if every 10th person is selected from a list of 100 people, the sample would include 10 people.
  • Stratified Sampling: In this method, the population is divided into subgroups or strata based on certain characteristics, and then a random sample is taken from each stratum. This is often used to ensure that the sample is representative of the population as a whole.
  • Cluster Sampling: In this method, the population is divided into clusters or groups, and then a random sample of clusters is selected. Then, all members of the selected clusters are included in the sample.
  • Multi-Stage Sampling : This method combines two or more sampling techniques. For example, a researcher may use stratified sampling to select clusters, and then use simple random sampling to select members within each cluster.

Non-probability Sampling

This type of sampling does not rely on random selection, and it involves selecting samples in a way that does not give every member of the population an equal chance of being included in the sample. Non-probability sampling is often used in qualitative research, where the aim is not to generalize findings to a larger population, but to gain an in-depth understanding of a particular phenomenon or group. Non-probability sampling methods can be quicker and more cost-effective than probability sampling methods, but they may also be subject to bias and may not be representative of the larger population.

Types of Non-probability Sampling :

  • Convenience Sampling: In this method, participants are chosen based on their availability or willingness to participate. This method is easy and convenient but may not be representative of the population.
  • Purposive Sampling: In this method, participants are selected based on specific criteria, such as their expertise or knowledge on a particular topic. This method is often used in qualitative research, but may not be representative of the population.
  • Snowball Sampling: In this method, participants are recruited through referrals from other participants. This method is often used when the population is hard to reach, but may not be representative of the population.
  • Quota Sampling: In this method, a predetermined number of participants are selected based on specific criteria, such as age or gender. This method is often used in market research, but may not be representative of the population.
  • Volunteer Sampling: In this method, participants volunteer to participate in the study. This method is often used in research where participants are motivated by personal interest or altruism, but may not be representative of the population.

Applications of Sampling Methods

Applications of Sampling Methods from different fields:

  • Psychology : Sampling methods are used in psychology research to study various aspects of human behavior and mental processes. For example, researchers may use stratified sampling to select a sample of participants that is representative of the population based on factors such as age, gender, and ethnicity. Random sampling may also be used to select participants for experimental studies.
  • Sociology : Sampling methods are commonly used in sociological research to study social phenomena and relationships between individuals and groups. For example, researchers may use cluster sampling to select a sample of neighborhoods to study the effects of economic inequality on health outcomes. Stratified sampling may also be used to select a sample of participants that is representative of the population based on factors such as income, education, and occupation.
  • Social sciences: Sampling methods are commonly used in social sciences to study human behavior and attitudes. For example, researchers may use stratified sampling to select a sample of participants that is representative of the population based on factors such as age, gender, and income.
  • Marketing : Sampling methods are used in marketing research to collect data on consumer preferences, behavior, and attitudes. For example, researchers may use random sampling to select a sample of consumers to participate in a survey about a new product.
  • Healthcare : Sampling methods are used in healthcare research to study the prevalence of diseases and risk factors, and to evaluate interventions. For example, researchers may use cluster sampling to select a sample of health clinics to participate in a study of the effectiveness of a new treatment.
  • Environmental science: Sampling methods are used in environmental science to collect data on environmental variables such as water quality, air pollution, and soil composition. For example, researchers may use systematic sampling to collect soil samples at regular intervals across a field.
  • Education : Sampling methods are used in education research to study student learning and achievement. For example, researchers may use stratified sampling to select a sample of schools that is representative of the population based on factors such as demographics and academic performance.

Examples of Sampling Methods

Probability Sampling Methods Examples:

  • Simple random sampling Example : A researcher randomly selects participants from the population using a random number generator or drawing names from a hat.
  • Stratified random sampling Example : A researcher divides the population into subgroups (strata) based on a characteristic of interest (e.g. age or income) and then randomly selects participants from each subgroup.
  • Systematic sampling Example : A researcher selects participants at regular intervals from a list of the population.

Non-probability Sampling Methods Examples:

  • Convenience sampling Example: A researcher selects participants who are conveniently available, such as students in a particular class or visitors to a shopping mall.
  • Purposive sampling Example : A researcher selects participants who meet specific criteria, such as individuals who have been diagnosed with a particular medical condition.
  • Snowball sampling Example : A researcher selects participants who are referred to them by other participants, such as friends or acquaintances.

How to Conduct Sampling Methods

some general steps to conduct sampling methods:

  • Define the population: Identify the population of interest and clearly define its boundaries.
  • Choose the sampling method: Select an appropriate sampling method based on the research question, characteristics of the population, and available resources.
  • Determine the sample size: Determine the desired sample size based on statistical considerations such as margin of error, confidence level, or power analysis.
  • Create a sampling frame: Develop a list of all individuals or elements in the population from which the sample will be drawn. The sampling frame should be comprehensive, accurate, and up-to-date.
  • Select the sample: Use the chosen sampling method to select the sample from the sampling frame. The sample should be selected randomly, or if using a non-random method, every effort should be made to minimize bias and ensure that the sample is representative of the population.
  • Collect data: Once the sample has been selected, collect data from each member of the sample using appropriate research methods (e.g., surveys, interviews, observations).
  • Analyze the data: Analyze the data collected from the sample to draw conclusions about the population of interest.

When to use Sampling Methods

Sampling methods are used in research when it is not feasible or practical to study the entire population of interest. Sampling allows researchers to study a smaller group of individuals, known as a sample, and use the findings from the sample to make inferences about the larger population.

Sampling methods are particularly useful when:

  • The population of interest is too large to study in its entirety.
  • The cost and time required to study the entire population are prohibitive.
  • The population is geographically dispersed or difficult to access.
  • The research question requires specialized or hard-to-find individuals.
  • The data collected is quantitative and statistical analyses are used to draw conclusions.

Purpose of Sampling Methods

The main purpose of sampling methods in research is to obtain a representative sample of individuals or elements from a larger population of interest, in order to make inferences about the population as a whole. By studying a smaller group of individuals, known as a sample, researchers can gather information about the population that would be difficult or impossible to obtain from studying the entire population.

Sampling methods allow researchers to:

  • Study a smaller, more manageable group of individuals, which is typically less time-consuming and less expensive than studying the entire population.
  • Reduce the potential for data collection errors and improve the accuracy of the results by minimizing sampling bias.
  • Make inferences about the larger population with a certain degree of confidence, using statistical analyses of the data collected from the sample.
  • Improve the generalizability and external validity of the findings by ensuring that the sample is representative of the population of interest.

Characteristics of Sampling Methods

Here are some characteristics of sampling methods:

  • Randomness : Probability sampling methods are based on random selection, meaning that every member of the population has an equal chance of being selected. This helps to minimize bias and ensure that the sample is representative of the population.
  • Representativeness : The goal of sampling is to obtain a sample that is representative of the larger population of interest. This means that the sample should reflect the characteristics of the population in terms of key demographic, behavioral, or other relevant variables.
  • Size : The size of the sample should be large enough to provide sufficient statistical power for the research question at hand. The sample size should also be appropriate for the chosen sampling method and the level of precision desired.
  • Efficiency : Sampling methods should be efficient in terms of time, cost, and resources required. The method chosen should be feasible given the available resources and time constraints.
  • Bias : Sampling methods should aim to minimize bias and ensure that the sample is representative of the population of interest. Bias can be introduced through non-random selection or non-response, and can affect the validity and generalizability of the findings.
  • Precision : Sampling methods should be precise in terms of providing estimates of the population parameters of interest. Precision is influenced by sample size, sampling method, and level of variability in the population.
  • Validity : The validity of the sampling method is important for ensuring that the results obtained from the sample are accurate and can be generalized to the population of interest. Validity can be affected by sampling method, sample size, and the representativeness of the sample.

Advantages of Sampling Methods

Sampling methods have several advantages, including:

  • Cost-Effective : Sampling methods are often much cheaper and less time-consuming than studying an entire population. By studying only a small subset of the population, researchers can gather valuable data without incurring the costs associated with studying the entire population.
  • Convenience : Sampling methods are often more convenient than studying an entire population. For example, if a researcher wants to study the eating habits of people in a city, it would be very difficult and time-consuming to study every single person in the city. By using sampling methods, the researcher can obtain data from a smaller subset of people, making the study more feasible.
  • Accuracy: When done correctly, sampling methods can be very accurate. By using appropriate sampling techniques, researchers can obtain a sample that is representative of the entire population. This allows them to make accurate generalizations about the population as a whole based on the data collected from the sample.
  • Time-Saving: Sampling methods can save a lot of time compared to studying the entire population. By studying a smaller sample, researchers can collect data much more quickly than they could if they studied every single person in the population.
  • Less Bias : Sampling methods can reduce bias in a study. If a researcher were to study the entire population, it would be very difficult to eliminate all sources of bias. However, by using appropriate sampling techniques, researchers can reduce bias and obtain a sample that is more representative of the entire population.

Limitations of Sampling Methods

  • Sampling Error : Sampling error is the difference between the sample statistic and the population parameter. It is the result of selecting a sample rather than the entire population. The larger the sample, the lower the sampling error. However, no matter how large the sample size, there will always be some degree of sampling error.
  • Selection Bias: Selection bias occurs when the sample is not representative of the population. This can happen if the sample is not selected randomly or if some groups are underrepresented in the sample. Selection bias can lead to inaccurate conclusions about the population.
  • Non-response Bias : Non-response bias occurs when some members of the sample do not respond to the survey or study. This can result in a biased sample if the non-respondents differ from the respondents in important ways.
  • Time and Cost : While sampling can be cost-effective, it can still be expensive and time-consuming to select a sample that is representative of the population. Depending on the sampling method used, it may take a long time to obtain a sample that is large enough and representative enough to be useful.
  • Limited Information : Sampling can only provide information about the variables that are measured. It may not provide information about other variables that are relevant to the research question but were not measured.
  • Generalization : The extent to which the findings from a sample can be generalized to the population depends on the representativeness of the sample. If the sample is not representative of the population, it may not be possible to generalize the findings to the population as a whole.

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  • Open access
  • Published: 13 December 2023

Professional identity and sense of coherence affect the between compassion fatigue and work engagement among Chinese hospital nurses

  • Yiming Zhang 1 , 2 ,
  • Qianwen Peng 1 ,
  • Wanglin Dong 1 ,
  • Cui Hou 1 &
  • Chaoran Chen 1  

BMC Nursing volume  22 , Article number:  472 ( 2023 ) Cite this article

Metrics details

With the continuous improvement of people’s health needs, the public’s requirements for medical care are also getting higher and higher. Work engagement is a positive psychological state related to the work. It is very important to maintain nurses’ work engagement, however, due to many factors, the level of nurses’ work engagement is not high and nursing managers should identify the influencing factors of work engagement, and take positive measures to fully improve nurses’ work engagement.

To explore the influence of compassion fatigue, professional identity and sense of coherence on nurses’ work engagement.

From January 2022 to June 2022, convenience sampling was used to select clinical nurses from 9 tertiary hospitals in Henan Province of China as the research objects for a questionnaire survey. Statistical methods included descriptive statistical analysis, Pearson correlation analysis and the PROCESS Macro Model 4 and 7 in regression analysis.

The results showed that compassion fatigue was significantly negatively correlated with sense of coherence, professional identity and work engagement ( P <0.01), professional identity was significantly positively correlated with sense of coherence and work engagement ( P <0.01), and there was a significant positive correlation between sense of coherence and work engagement ( P <0.01). Professional identity played a partial mediating role between compassion fatigue and work engagement, accounting for 46.40% of the total effect; meanwhile, sense of coherence moderated the effect of compassion fatigue on professional identity and formed a moderated mediation model.

Conclusions

Compassion fatigue has a negative predictive effect on nurses’ work engagement. Professional identity and sense of coherence further explained the relationship of compassion fatigue on compassion fatigue and work engagement through mediating and moderating effects.

Peer Review reports

Introduction

Clinical nurses are an important occupational group in medical and health institutions and play an important role in providing medical and health services [ 1 ]. However, most countries are facing a shortage of nurses. The World Health Organization predicts that the global shortage of nurses will reach 5.7 million by 2030 [ 2 ]. In addition, due to the particularity, high risk and high load of nursing work, many nurses are under high work pressure and workload, and are prone to job burnout and turnover intention [ 3 ], which leads to a more serious shortage of nursing human resources. The shortage of nursing human resources aggravated the work burden of nurses, which made many nurses unable to fully devote themselves to work. Work engagement is most often defined as ‘’a positive, fulfilling, work-related state of mind characterized by vigour, dedication, and absorption’’ [ 4 ]. Previous studies have shown that work engagement is a positive psychological state related to work, which can not only effectively reduce nurses’ job burnout and turnover intention, but also further improve nurses’ satisfaction with nursing work and improve their job performance and nursing service quality [ 5 , 6 ]. Understanding the status of nurses’ work engagement and exploring the factors affecting nurses’ work engagement are not only the focus of current nursing managers, but also very important for improving the quality of nursing service and promoting the healthy development of nursing.

Researchers have conducted extensive exploration on the factors that may affect nurses’ work engagement, and found that nurses’ work engagement is not only affected by external factors such as organizational climate, leadership style, workload [ 1 , 5 , 7 ], but also closely related to individual subjective psychological factors [ 8 ]. As providers of health care, nurses aim to provide care, support, and assistance to patients according to their physical, psychological, emotional, and spiritual needs. However, nurses have to face the pain of patients every day in the process of caring for patients, and have compassion for the pain suffered by patients. In the long run, nurses will have job burnout due to excessive compassion investment, and their compassion for the pain suffered by patients will be reduced, and compassion fatigue will occur [ 9 ]. Compassion fatigue is defined as the negative emotional consequences of chronic and repeated exposure to the mental and physical suffering of others in the provision of care [ 10 ]. As an important stress response, compassion fatigue has received considerable attention among medical workers [ 11 ]. In the study of Korean nurses, compassion fatigue has a significant positive predictive effect on work stress and is a significant negative predictor of job satisfaction [ 12 ]. A study of Australian nurses showed that compassion fatigue was significantly and positively correlated with high levels of anxiety and depression [ 13 ]. In the study of physicians and nurses in China, it was found that compassion fatigue was closely related to burnout and depression of physicians and nurses [ 14 ]. Compassion fatigue of nurses is closely related to the level of work engagement [ 15 ]. However, there are few studies on the overall relationship between compassion fatigue and work engagement, and the process of the relationship between the two is still unclear, which needs further in-depth research.

Professional identity is defined as an individual’s understanding of their own profession in professional practice, and it is a feeling and cognition of the professional value of their profession and the development of their personal ability in the profession [ 16 ]. According to the three-dimensional professional identity theory [ 17 ], an individual’s recognition of professional value and role value will be enhanced through the development of individual cognition, emotion and intentional behavior, thereby improving individual work engagement. Relevant studies have shown that nurses’ compassion fatigue can affect nurses’ professional identity [ 18 ], and professional identity plays a crucial role in nurses’ work engagement [ 19 ]. Therefore, nurses’ professional identity may be an important mediating variable in the relationship between compassion fatigue and work engagement. Furthermore, nurses’ own internal psychological resources may moderate this process. Sense of coherence is defined as a person’s general orientation towards life that guides people to find and use resources to stay healthy, especially during times of considerable strain, which helps to cope with life stress [ 20 ]. It refers to the ability of individuals to maintain their physical and mental health in the face of stressful situations in work and daily life. It is not only an important psychological resource for medical and health personnel, but also an important predictor of nurses’ professional identity [ 21 , 22 ]. Thus, as a positive psychological resource, nurses’ sense of coherence may play a moderating role in this process.

According to the job demand resource theory, nurses need to compassion to the patients they are caring for in the nursing work. In the long run, nurses’ energy can be eroded by their compassion for patients, leading to problems such as poor job performance and work engagement. Professional identity and sense of coherence, as important psychological factors of individuals, are also regarded as the work resources of nurses in nursing work, and may play an important role in the relationship between compassion fatigue and work engagement of nurses. Existing studies have examined the relationship between compassion fatigue and work engagement, the relationship between compassion fatigue and professional identity, and the relationship between professional identity and work engagement [ 15 , 18 , 23 ]. But through a search of the relevant literature, we found that there is no study on the relationship between the four variables of compassion fatigue, professional identity, sense of coherence and work engagement, especially in clinical nurses. Based on this, this study used the independent variable of compassion fatigue, professional identity as the mediating variable, sense of coherence as the moderating variable, and work engagement as the dependent variable to construct and verify the mediating effect model, in order to provide new ideas and suggestions for hospitals and nursing managers to further improve the work engagement of clinical nurses, and provide reference and empirical basis for improving the level of nursing service.

Theoretical basis

The job demand-resource theory is proposed by Demerouti [ 24 ]. This model believes that the job characteristics of each occupation will be expressed in two forms: job requirements and job resources. Among them, job demand refers to the individual’s physical, cognitive or emotional investment in the process of work due to the job demand. Work resources refer to those resources that have an incentive effect on individual growth and can promote individuals to achieve work goals. Job demands and job resources can affect job burnout and job engagement through two different pathways, namely “fatigue process” and “motivation process”. The “fatigue process” refers to the process by which an employee’s original energy is eroded by constant effort to cope with long-term job demands, leading to poor performance, decreased job engagement, and physical and mental health problems. “Motivational process” refers to the process of improving the degree of work engagement, work enthusiasm and work performance of individuals due to the sufficiency of work resources. Compassion plays the role of “job demand” in the job demand-resource model. Therefore, when nurses pay too much compassion and appear compassion fatigue, it will lead to the decline of individual work engagement. Professional identity acts as a “job resource”, which can promote individuals to cope with job requirements, complete work tasks, and improve individual work engagement. The sense of coherence can make individuals identify, benefit from, and control and use their own resources, so it may play a moderate role in individual resources. Based on the above theoretical basis, this study designed the research hypothesis model as shown in Fig.  1 , and put forward the following hypotheses:

figure 1

The theoretical model of this study

H1. Compassion fatigue has a direct negative impact on nurses’ work engagement.

H2. Compassion fatigue has a direct negative impact on nurses’ professional identity.

H3. Professional identity has a direct positive impact on nurses’ work engagement.

H4. Professional identity plays a mediating role between compassion fatigue and work.

engagement.

H5. Sense of coherence plays a moderating role between professional identity and work engagement.

Design and participants

Using the convenient sampling method, a questionnaire was distributed to clinical nurses who met the inclusion criteria from 9 tertiary hospitals in Henan Province from January 2022 to June 2022. The nine surveyed tertiary hospitals were all general hospitals, mainly from three prefecture-level cities in Henan province, including four affiliated university hospitals. The number of hospital beds in the survey ranged from 1200 to 3541, and the annual outpatient visits ranged from 0.30 million to 1.90 million. The number of nurses participating in the survey in these 9 hospitals were 121, 182, 116, 158, 189, 207, 145, 138, 142, respectively. The inclusion criteria included (1) clinical registered nurses; (2) engaged in clinical nursing work for more than 1 year; (3) nurses with informed consent and voluntary cooperation to participate in this investigation. Nurses who were not directly involved in patient care and intern nurses were excluded. According to the sample size calculation formula N  = 4Uα 2 S 2 /δ 2 [ 25 ] and the results of the pre-survey, the standard deviation S was 1.63, and the allowable error δ was set as 0.2, α = 0.05, N  = 4 × 1.96 2  × 1.63 2 /0.2 2 ≈1021. Due to the possibility of loss of follow-up and invalid questionnaires in the process of issuing questionnaires, 1398 questionnaires were actually issued after adding 20% of the sample size. 38 regular response questionnaires and 43 lost of follow-up were excluded, and 1317 valid questionnaires were finally collected, with an effective recovery rate of 94%.

Data collection

On the basis of the agreement of the hospital nursing department and departments, the questionnaires were distributed to the nurses who met the inclusion and exclusion criteria. The purpose and precautions of this study were explained before the questionnaire was distributed, and the questionnaire could not be distributed until the informed consent of the nurses was obtained. The validity of the questionnaire was checked by two people on the spot when the questionnaire was collected to ensure the accuracy and completeness of the data.

Demographic information questionnaire

Based on the research content and purpose of this study, the demographic data questionnaire of clinical nurses was designed by the researchers. There were 12 items in this part, which mainly investigated the age, gender, work departments, working years, professional title, education level, average weekly working hours, average monthly night shifts, average monthly income, marital status, number of children and mode of employment.

Compassion fatigue scale

The Chinese version of Compassion Fatigue Short Scale (C-CF) compiled by Adams [ 9 ] and revised by Lou [ 26 ]. The scale consisted of two dimensions of secondary trauma and job burnout, with a total of 13 items. Among them, secondary trauma was composed of 5 items, and job burnout was composed of 8 items. The scale was scored by Likert 10 points, and the full score was 130 points. The higher the total score, the more serious the degree of compassion fatigue. The level of compassion fatigue can be divided into three levels according to the average score of items: mild (< 4 points), moderate (4–7 points) and severe (> 7 points), and the questionnaire has good construct validity. The Cronbach’s α coefficient of the total scale in this study was 0.918.

Professional identity scale

The professional identity rating scale for nurses compiled by Liu [ 27 ] was adopted. The scale had 5 dimensions, a total of 30 items. The scale was scored by Likert 5-point scale, with a total score of 150 points, and the higher the score, the higher the professional identity. The Cronbach’s α coefficient of the scale in this study was 0.960.

Sense of coherence scale

Sense of coherence scale (SOC-13) with 13 items compiled by Antonovsky [ 28 ] was selected for measurement, and the Chinese version was revised by domestic scholar Bao [ 29 ]. In this scale, there are five reverse scoring items, and the remaining eight items are all positive scoring. The scale included 3 sub-dimensions of comprehensibility, manageability and meaning-fulness. Likert 7-point scale was used, and the full score was 91. A higher total score indicates a higher level of sense of coherence. The scale has good reliability and validity, and the criterion-related validity is ideal. The Cronbach’s α coefficient of the scale in this study was 0.870.

Work engagement scale

The short version of the 9-item Work Engagement Scale (UWES-9) compiled by Schaufeli [ 4 ] was used. The scale included 3 dimensions, and each dimension included 3 items. The scale uses a 7-point Likert scoring method, ranging from 0 (never) to 6 (every day), with a full score of 54. Higher scores indicate higher individual work engagement. According to the average score of the items, they could be divided into three levels: mild (< 2 points), moderate (2–4 points) and severe (> 4 points). The scale has good reliability and validity, and some domestic scholars have confirmed that the Chinese version of the scale has good reliability and validity [ 30 ]. The total Cronbach’s α coefficient of the scale in this study was 0.945.

Ethical considerations

This study was reviewed and approved by the Sub-Committee of Biological Science Research Ethics of Henan University (No. HUSOM2021-286) and followed the ethical principles of informed consent, timely explanation was given to the investigators before the investigation, and informed consent was obtained before the investigation. Besides, this study was an anonymous survey, and all investigators could voluntarily withdraw from this study, and the data collected were strictly confidential and used only for this study.

Data analysis

The statistical analyses were carried out using the SPSS 26.0 software and the PROCESS Macro. Frequency, mean and standard deviation were used for descriptive analysis, and the relationships among compassion fatigue, professional identity, sense of coherence and work engagement were analyzed by Pearson’s correlation analysis.PROCESS is a plug-in developed by Hayes that specializes in analyzing mediating and moderating effects [ 31 ]. According to the methods provided by Hayes [ 31 ], PROCESS Models 4 (to test mediating effects) and 7 (to test the moderating variable’s moderating effect on the first half path) in the SPSS regression analysis PROCESS v4.1 program were used to test the moderated mediation effect. Besides, in order to better explain the moderating effect of sense of coherence and determine whether the mediating effect varied with the moderating variable, we used the simple slope method and the 5000 resample bootstrapping method to divide the sense of coherence into high and low groups based on the mean +/ -1 SD of the sense of coherence [ 32 , 33 ], and tested whether the mediating effect was significant in different level of the moderating variable. All statistical analyses within this study were two-sided, with a significance level of 0.05, and a P value of less than 0.05 was considered to indicate statistical significance.

Participant characteristics

A total of 1317 valid questionnaires were collected in this survey, of which the proportion of female nurses was 80.4%. In terms of age, 39.0% of the nurses were between 26 and 35 years old. Most of the nurses worked in internal medicine and surgery departments, accounting for 28.0% and 25.7%, respectively. 40.5% of the nurses had worked for 1–5 years. The nurses with average monthly income of 6001–9000 yuan were the most, accounting for 34.1%. The results of participant characteristics are shown in Table  1 .

Pearson’s correlation analysis

Means, standard deviations (SD), and Pearson correlations of each variable are shown in Table  2 ., among clinical nurses, the overall average scores of compassion fatigue, professional identity, sense of coherence and work engagement were (3.68 ± 1.84), (3.40 ± 0.59), (4.39 ± 0.92) and (3.57 ± 1.21) respectively. Pearson’ correlation analysis showed that the correlation coefficients of compassion fatigue, professional identity, sense of coherence and work engagement were − 0.565, 0.647, 0.431, respectively (P < 0.01). The correlation coefficient between professional identity and sense of coherence was 0.368 (P < 0.01), and the correlation coefficient between professional identity and compassion fatigue was − 0.555 (P < 0.01), and the correlation coefficient between sense of coherence and compassion fatigue was − 0.273 (P < 0.01).

Mediating effect with moderating analysis

Through multiple linear regression analysis, it was found that professional title and average monthly income had a significant impact on work engagement, so they were used as control variables in the mediating effect. After the variables were standardized, PROCESS model 4 in SPSS was used to analyze the mediating effect, and the Bootstrap sampling number was set to 5000 times to test the mediating effect [ 31 ]. The results showed that after controlling for nurses’ title and average monthly income, compassion fatigue negatively predicted work engagement (c=-0.556, t=-24.823, P < 0.001). When compassion fatigue and professional identity were included in the regression equation, the effect size of compassion fatigue negatively predicting work engagement was reduced (c’=-0.299, t=-12.7589, P < 0.001), and compassion fatigue negatively predicted professional identity (a=-0.552, t=-24.121, P < 0.001). Professional identity also had a significant positive predictive effect on work engagement (b = 0.469, t = 19.727, P < 0.001). The Bootstrap test showed that professional identity had a significant mediating effect between compassion fatigue and work engagement (ab=-0.258, P < 0.001), Boot SE = 0.018, 95%CI [-0.296, -0.225], the proportion of mediating effect in the total effect was ab/ (ab + c’) = 46.40%, that is, in the relationship between compassion fatigue and work engagement of clinical nurses, professional identity played a part of the mediating effect, and the mediating effect accounted for 46.40% of the total effect. As shown in Fig.  2 .

figure 2

Mediating effect of professional identity

The PROCESS model 7 was used to analyze the moderating effect. The results showed that model 1 was significant (F = 202.272, P < 0.001, R 2  = 0.316) when demographic variables were controlled. Compassion fatigue could negatively predict professional identity (β=-0.552, t=-24.121, P < 0.001), model 2 was significant (F = 319.427, P < 0.001, R 2  = 0.493). Compassion fatigue could negatively predict work engagement (β=-0.298, t=-12.589, P < 0.001), and professional identity could positively predict work engagement (β = 0.469, t = 19.727, P < 0.001), model 3 was significant (F = 172.187, P < 0.001, R 2  = 0.396). Compassion fatigue had a significant negative predictive effect on professional identity (β=-0.536, t=-23.340, P < 0.001), and the interaction term between compassion fatigue and sense of coherence was significant (β=-0.163, t=-8.427, P < 0.001), indicating that sense of coherence had a moderating effect on the relationship between compassion fatigue and professional identity in the above mediation model, as detailed in Table  3 .

The level of sense of coherence was divided into high score group and low score group, and the simple effect slope figure as shown in Fig.  3 . Clinical nurses with low score group (M-1SD), the impact of compassion fatigue on professional identity was negatively significant (β=-0.373, t=-14.192, P < 0.001). For clinical nurses with high level of sense of coherence (M + 1SD), compared clinical nurses who with the low level of sense of coherence, the negative slope of compassion fatigue on professional identity was greater (β=-0.699, t=-20.963, P < 0.001), see Table  4 for details. This means that under the same level of compassion fatigue, nurses with high sense of coherence can obtain more professional identity growth than nurses with low sense of coherence.

figure 3

Moderating effect of sense of coherence

This study explored the relationships among compassion fatigue, professional identity, sense of coherence and work engagement of nurses. The findings are consistent with the proposed theoretical framework. First, the result showed that compassion fatigue was negatively correlated with professional identity, sense of coherence and work engagement, while the remaining variables were positively correlated with each other. Second, the mediating effect showed that professional identity played a partial mediating effect between compassion fatigue and work engagement of clinical nurses. Third, sense of coherence has a significant moderating effect between compassion fatigue and professional identity, and under the same level of compassion fatigue, nurses with high level of sense of coherence can obtain more professional identity growth than those with low sense of coherence.

In this study, the compassion fatigue score was consistent with the study by Yang and Zhu [ 34 ], but higher than that in the study by Barnett [ 35 ] and Arıkan [ 36 ]. This difference may be related to the different nursing departments. In different departments, the severity of patients’ illness and the workload of nurses are different, which may cause the differences in compassion fatigue of nurses in different departments. The professional identity of clinical nurses is at a medium level, which is consistent with the research results of Yu [ 37 ]. It shows that nurses have certain cognition of nursing profession, but they still need to further improve their professional identity. In addition, the level of sense of coherence of clinical nurses in this study (57.06 ± 11.94) was lower than the score of nurses in developed countries Sweden (61.43 ± 0.76) and Spain (67.9 ± 10.02) [ 21 , 38 ]. China has a large population, the aging population and the “two-child” policy have greatly increased the demand for nurses [ 39 , 40 ]. Due to the shortage of nursing staff, Chinese nurses face heavier work and bear greater pressure than developed country. Previous studies have shown that sense of coherence is negatively correlated with job stress [ 41 , 42 ]. These may be the reasons why the level of sense of coherence of nurses in China is lower than that in Sweden and Spain. Finally, nurses in this study had a moderate level of work engagement, which was lower than the studies of Baghdadi [ 43 ]and Borges [ 44 ]. The reason for the inconsistent results may be related to the hospital level. This study investigated a tertiary hospital in China. Compared with other levels of hospitals, tertiary hospitals have a large number of patients, more serious conditions, and a large nursing workload, which may also be the reason for lower results than in other studies.

The findings support H1 that compassion fatigue is a significant negative predictor of work engagement, which is consistent with the findings of Cao and Chen [ 15 ]. Compassion fatigue is a work-related stressor and easily lead to the consumption of individual psychological resources. According the conservation of resources theory [ 45 ], when nurses perceive compassion fatigue, they may need more resources to cope with compassion fatigue, and in order to further reduce the loss of their own resources, nurses are less engaged in their work, which results in lower work engagement [ 46 ]. In addition, according to the job demand-resource model [ 24 ], on the one hand, nurses need to have compassion for patients in the process of patient care, and compassion is a job requirement of nurses in nursing work. On the other hand, nurses need to be exposed to the trauma caused by patients’ pain for a long time in clinical nursing work, and are in the risk environment of secondary trauma. If they cannot be adjusted and recovered in time, they will have a negative impact on their physical and mental health [ 47 ], reduce their job satisfaction [ 48 ], produce job burnout [ 49 ], and treat their work with a negative attitude [ 14 ]. As a result, the level of work engagement of nurses decreased.

The results of this study show that compassion fatigue is significantly negatively correlated with professional identity, which supports H2 and is also consistent with previous research [ 50 ]. The study of Yi [ 18 ] showed that compassion fatigue of nursing interns reduced their enthusiasm for nursing and professional identity. The formation of professional identity is a continuous development process, and an individual’s educational experience, life experience, work experience and social media will all have an impact on their professional identity [ 18 ]. Relevant studies also believe that nurses’ basic values and the formation, development and maintenance of values will also have resulting in a profound impact on their practice [ 51 , 52 ]. Geoffrion [ 51 ] believe that caregivers’ cognition of their profession will change their basic beliefs about the world due to long-term trauma, which in turn may endanger nurses’ self-cognition of the nursing profession, affect individuals’ attitudes, values and cognition of their profession, and have a negative impact on mental health. Therefore, nurses with higher compassion fatigue also had lower identification with their profession.

The positive correlation between professional identity and work engagement supports H3, indicating that the higher the level of professional identity of clinical nurses, the higher the level of work engagement, which is consistent with Zhang [ 23 ]. When individuals have a positive identity with their profession, they will devote more energy and enthusiasm to their work, and the dissatisfaction caused by the working environment will be eliminated to a certain extent [ 17 ]. According to self-determination theory [ 52 ], professional identity can affect internal cognition and enhance internal motivation through individual self-regulation, which has a positive role in promoting nurses’ work engagement. Therefore, with the continuous increase of nurses’ professional identity, the perceived work pressure and job burnout level of nurses in clinical nursing work also decreases, and their self-worth can also be reflected in the work. They often show positive attitudes and behaviors, and can put more enthusiasm and energy into clinical nursing work from the heart [ 53 , 54 ]. Therefore, nurses with higher professional identity have stronger work enthusiasm and higher work engagement.

The mediating effect showed that professional identity played a partial mediating role in the relationship between compassion fatigue and professional identity of clinical nurses, and this result supported H4. The reason may be that the more serious the compassion fatigue of clinical nurses is, the weaker the perception and compassion of clinical patients are, and they cannot meet the emotional needs of patients in a timely and effective manner [ 11 ]. In addition, the long-term high-load, high-pressure and high-risk clinical work and the long-term exposure to patients’ pain and trauma environment, the original energy of nurses is eroded in order to cope with the work requirements [ 55 ]. With the continuous loss of their own resources, negative emotional problems gradually occurred, and their recognition of the nursing profession also decreased [ 56 ]. As a means of maintaining resources, professional identity can reduce the consumption of own resources. If the professional identity of clinical nurses decreases, the nurses’ sense of professional benefits will also decrease, and the fatigue and burnout will increase at work, and they will often adopt a negative attitude to deal with work, which will lead to the decrease of their work efficiency and work engagement [ 19 ]. On the contrary, when nurses’ professional identity is high, their dissatisfaction at work will be correspondingly reduced, and their perceived professional benefits in nursing work will be correspondingly increased, so as to reduce the negative impact of compassion fatigue on work engagement.

This study also supports H5 that sense of coherence played a moderating role between compassion fatigue and professional identity, and nurses with a high level of sense of coherence had a greater impact on professional identity than nurses with a low level of sense of coherence. This indicates that nurses with a high sense of coherence can obtain more career identity growth than those with a low sense of coherence at the same level of compassion fatigue. The reason may be that nurses with a high sense of coherence have a high level of professional identity, under the same level of compassion fatigue, nurses with high sense of coherence can get relief faster and better than those with low sense of coherence. Therefore, with the compassion fatigue decrease, the slope value of high sense of coherence in the simple slope adjustment effect chart is also larger. Nurses with a high sense of coherence can correctly evaluate stress, think that the stimulation and stress they suffer are predictable and interpretable, adopt more adaptive strategies to cope with the emerging compassion fatigue by regulating stressful events, and effectively extract and mobilize existing resources to cope with compassion fatigue [ 4 , 19 , 57 ]. Since nurses with a high sense of coherence have a high level of professional identity, slight changes in the face of compassion fatigue may cause large fluctuations in their professional identity level. Nurses with low sense of coherence have a low level of professional identity. Even with the alleviation of compassion fatigue, the change of professional identity is smaller than that of nurses with high sense of coherence. It’s important to note, through the simple moderating effect chart, it can be seen that no matter how the level of sense of coherence changes and how the slope value decreases, the level of professional identity of nurses with high sense of coherence is higher than that of nurses with low sense of coherence.

Implication for nursing management

This study provides a new perspective for improving the level of nurses’ work engagement, and also provides a theoretical and practical basis for subsequent scholars to carry out further in-depth research on nurses’ work engagement. First, nurses can relieve compassion fatigue at work by communicating more with family and colleagues, participate in more positive psychological lectures, and continuously improve their nursing knowledge and skills to increase their coping ability and ability to deal with problems. Second, nursing managers should timely identify the tendency of compassion fatigue in nurses, and relieve nurses’ negative psychology and emotions through reasonable scheduling lecture on psychology and psychological counseling, so as to reduce nurses’ compassion fatigue. In addition, nursing managers should pay attention to the mediating role of professional identity and the moderating role of sense of coherence, build a good humanistic atmosphere of the department, provide a supportive working environment for clinical nurses, improve nurses’ working attitude by reasonable allocation of work resources (such as arrange shift patterns scientifically and allocate workforce rationally, and reduce nurses’ workload [ 58 ]), change the leadership style according to the characteristics and actual situation of nurses (such as studies have shown that transformational leadership can improve nurses’ work engagement [ 59 ]), strengthen training and education and other measures (such as implementation of programs to increase sense of coherence among nurses [ 60 ], support for staff education contributes to professional identity and practice [ 61 ]), so as to improve nurses’ work engagement. Finally, medical institutions should pay enough attention to nurses, provide training and promotion opportunities for nurses, meet the needs of nurses for career development, let nurses feel their working status has been improved, their self-worth has been affirmed, and reasonable allocation of nursing human resources according to workload and actual work needs, and further improve the welfare and work performance of nurses. Medical colleges should also pay attention to the cultivation of professional quality of nursing students, strengthen the education of professional value and sense of coherence.

Limitations

This study has several limitations. First, the study was a cross-sectional study and was only investigated in a single time period. In future research, longitudinal studies can be conducted to understand and compare the dynamic development and changes of compassion fatigue, professional identity and work engagement in clinical nurses. ent. Second, according to hospital scale, research direction, human resources and technical strength, Chinese hospitals were divided into three levels, namely primary hospitals, secondary hospitals, and tertiary hospitals. The number of patients and the severity of the patient’s condition are different in primary, secondary and tertiary hospitals, the workload and work stress of nurses are also different. This study used convenience sampling and only investigated tertiary hospitals; primary and secondary hospitals were not involved. Different hospital characteristics, different levels of hospitals and convenient sampling methods may affect the measured variables in this study, resulting in underrepresentation of the sample and limiting the conclusions. Therefore, future studies should improve their design, adopt a multicenter design and include both primary and secondary hospitals to increase the representativeness and generalization of the results. Third, this study only investigated the relationship between compassion fatigue, sense of coherence, professional identity and work engagement, and has not explored the effects of other variables on nurses’ work engagement. The results of conceptual analysis and other study results suggest that personal-professional integrity, coping strategies resilience and self-efficacy are important influencing variable of work engagement [ 15 , 62 , 63 ]. Besides, resilience as a stress coping resource, it can effectively help buffer the negative impact of workplace stressors on nurses, and it closely intertwined with self-coherence, personal-professional integrity, compassion fatigue, and various other similar concepts within diverse nursing environments. Therefore, Future research should consider including more variables, especially resilience, to comprehensively explore the important factors affecting clinical nurses’ work engagement.

Clinical nurses have a low level of compassion fatigue, and their professional identity, sense of coherence and work engagement are at a medium level. Compassion fatigue of clinical nurses was significantly correlated with professional identity, sense of coherence and work engagement. Professional identity plays a partial mediating role between compassion fatigue and work engagement in nurses, and sense of coherence plays a moderating role in the indirect path of compassion fatigue affecting nurses’ professional identity. Nursing managers should pay more attention to compassion fatigue of clinical nurses and pay attention to improving nurses’ professional identity and sense of coherence, so as to further improve their work engagement.

Data Availability

The datasets can be made available to any interested person(s) contacting the corresponding author via email.

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Acknowledgements

Qianwen Peng is the co-first author. The authors thank all the participants and research assistants involved with this study.

This research was funded by the Graduate Education Innovation and Quality Improvement Program of Henan University, grant number SYL19060141; the Henan Provincial Social Science Planning Decision Consulting Project, grant number 2018JC38; and the Graduate Education Reform and Quality Improvement Project of Henan Province, grant number YJS2021AL074.

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Institute of Nursing and Health, School of Nursing and Health, Henan University, Kaifeng, People’s Republic of China

Yiming Zhang, Qianwen Peng, Wanglin Dong, Cui Hou & Chaoran Chen

Department of Health and Wellness, Nanyang Vocational College of Science and Technology, Nanyang, People’s Republic of China

Yiming Zhang

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CH and CC designed the content of this study, YZ and QP wrote the main manuscript text, YZ prepared figures and drew the table. YZ、QP and CC participated in data collection, YZ and WD made adjustments to the format of the manuscript. The manuscript was examined by all the authors, and all authors are responsible for the content and have approved this final version of the manuscript.

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Correspondence to Cui Hou or Chaoran Chen .

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This study has been reviewed and approved by Institutional Review Board of Henan Provincial Key Laboratory of Psychology and Behavior (reference: No. HUSOM2021-286) and performed in accordance with the Declaration of Helsinki. All participants gave their voluntary written informed consent prior to study participation. All methods were performed in accordance with the relevant guidelines and regulations.

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Zhang, Y., Peng, Q., Dong, W. et al. Professional identity and sense of coherence affect the between compassion fatigue and work engagement among Chinese hospital nurses. BMC Nurs 22 , 472 (2023). https://doi.org/10.1186/s12912-023-01596-z

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DOI : https://doi.org/10.1186/s12912-023-01596-z

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research paper sampling methods

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Computer Science > Robotics

Title: rms: redundancy-minimizing point cloud sampling for real-time pose estimation in degenerated environments.

Abstract: The typical point cloud sampling methods used in state estimation for mobile robots preserve a high level of point redundancy. The point redundancy slows down the estimation pipeline and can make real-time estimation drift in geometrically symmetrical and structureless environments. We propose a novel point cloud sampling method that is capable of lowering the effects of geometrical degeneracies by minimizing redundancy within the cloud. The proposed method is an alternative to the commonly used sparsification methods that normalize the density of points to comply with the constraints on the real-time capabilities of a robot. In contrast to density normalization, our method builds on the fact that linear and planar surfaces contain a high level of redundancy propagated into iterative estimation pipelines. We define the concept of gradient flow quantifying the surface underlying a point. We also show that maximizing the entropy of the gradient flow minimizes point redundancy for robot ego-motion estimation. We integrate the proposed method into the point-based KISS-ICP and feature-based LOAM odometry pipelines and evaluate it experimentally on KITTI, Hilti-Oxford, and custom datasets from multirotor UAVs. The experiments show that the proposed sampling technique outperforms state-of-the-art methods in well-conditioned as well as in geometrically-degenerated settings, in both accuracy and speed.

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